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journal.ppat.1003582

A Comprehensive Analysis of In Vitro and In Vivo Genetic Fitness of Pseudomonas aeruginosa Using High-Throughput Sequencing of Transposon Libraries

The complex interaction of a pathogenic bacterium with a host leading to disease can be viewed as the coordinated and highly-regulated actions of a multitude of factors that allows the infecting organisms to successfully colonize tissue , occasionally disseminate and avoid the activities of host defense mechanisms ., In each setting , individual or combinations of specific genetically-encoded factors contribute to the overall fitness of an organism and its ability to survive within a specific environment ., Tools and strategies of bacterial genetics , particularly the ability to engineer isogenic mutants , have been extensively exploited for precise determinations of the requirements for particular gene products ( including those providing various sensory and regulatory inputs ) at diverse stages of the infectious process ., More recently , DNA microarray based methods , such as signature-tagged mutagenesis 1 , 2 , 3 or transposon site hybridization 4 , 5 , have been utilized to determine the importance of individual genes in the infection process based on the negative selection of mutants ., Such genome-wide approaches provide another level of depth for understanding the role of virulence factors in the host , since they analyze the growth phenotype of individual bacterial mutants in the context of the entire population , i . e . , in cells surrounded by otherwise phenotypically wild type ( WT ) siblings ., The availability of high-throughput DNA sequencing technologies makes it feasible to obtain millions of DNA sequences from a single microbial sample ., This tool has emerged as a major means to detect variations in genetic fitness of individual mutants in a population undergoing selection in infected hosts ., By preparing highly saturated random transposon ( Tn ) insertion libraries using a specifically designed Tn followed by ascertaining the site of insertion via sequencing of the Tn junctions within the chromosomal DNA ( variably referred to as Tn-seq or INSeq ) 6 , 7 , 8 , unique insights into the role of individual virulence factors and their regulators in the infectious process can be obtained ., The basic principles and applications of the INSeq methodology have been published 6 ., Fitness , as determined from INSeq results , can identify negatively selected phenotypes , presumably due to mutational inactivation of genes whose products are required for optimal growth and/or survival within the host ., A large number of these phenotypes represent virulence factors or their regulators ., Moreover , positively selected phenotypes whose loss enhances virulence in the host could indicate genes that repress the expression of virulence determinants or genes affecting production of conserved microbe-associated molecules such as flagellin that are recognized by the host defense mechanisms and increase resistance to infection ., Additionally , the INSeq technology can identify genes involved in other novel and important aspects of microbial virulence , including those coordinately regulated both within and outside of defined operons ., Furthermore , studies of fitness can lead to improved gene annotation ., By comparing quantitative levels of mutated genes between input and output populations that contribute to specific phenotypes ( inhibited or enhanced growth in a particular environment ) , logical follow-up studies can be carried out to determine the biological role played by the products of genes of unknown function ., Finally , by recognition of co-selected mutations with comparable quantitative changes in the occurrence of Tn insertions in a population undergoing selection , relationships of shared or related biological function can be identified ., We applied the INSeq approach to analyze a comprehensive library of ∼300 , 000 mariner Tn insertions in the extensively characterized Pseudomonas aeruginosa strain PA14 9 known for its high virulence in numerous models due to the production of the ExoU cytotoxin and carriage of two important pathogenicity islands , but also containing some endogenous mutations such as in the ladS regulatory gene whose dysfunction also increases virulence 10 ., We utilized a well-established mouse infection model that strongly mimics the course of human infection in patients with cancer and bone-marrow transplantation 11 , 12 , 13 to determine factors needed for mucosal colonization and systemic dissemination following induction of neutropenia 14 , 15 ., When compared to the input mutant population , many of the Tn insertions underrepresented in the pool of colonizing organisms were in genes for well-established virulence factors , which not only validated the overall approach and its accuracy but also allowed a unique and comprehensive view of all the known virulence factors of P . aeruginosa ( http://www . mgc . ac . cn/VFs/main . htm ) ., The results presented here demonstrate the power of comprehensive , genome-wide approaches , such as INSeq in an established animal model of infection , for uncovering finer details of processes that bacteria deploy during establishment , progression and outcome of an infection ., Importantly , this work and similar studies could facilitate assignments of new biological activities to proteins of unknown functions leading to a more complete understanding of the complex biological processes taking place during host-pathogen interactions ., We generated a bank of approximately 300 , 000 Tn insertions in P . aeruginosa strain PA14 which was used to determine fitness dynamics in an infection cycle starting with colonization of the ceca followed by dissemination into the spleens after induction of neutropenia , as outlined in Figure 1 and described previously in detail 15 ., To identify the genes that influence colonization , the ceca of the mice were harvested 6 days after colonization commenced ( Figure 1A ) , the surviving P . aeruginosa strains with the Tn insertions grown , DNA extracted and digested with the MmeI enzyme , DNA fragments in the range of 1 , 200–1 , 500 bp obtained by gel fractionation and the recovered oligonucleotides prepared for high-throughput sequencing 6 ., To determine all the genes of P . aeruginosa necessary for systemic dissemination following neutrophil depletion , strains with Tn insertions that were able to disseminate were recovered from the spleens ( Figure 1B ) ., Since colonization is a prerequisite for dissemination , we would only be able to quantify changes in representation among Tn insertions able to colonize the ceca which , following dissemination , would show a reduced or enhanced representation in the Tn library recovered from the spleens ., Quantitative analysis of the overall frequency of Tn insertions into the chromosome was based on use of one million sequencing reads to normalize the data from different DNA preparations ., In the LB-grown input bank there was a relatively homogeneous distribution of Tn insertions across the PA14 chromosome ., There were 636 genes with <10 sequencing reads ( Figure 2A and Table S1 ) , thus defined as unable to grow ., Of these 636 genes , 407 were previously identified as essential in strain PA14 16 and 198 of these 407 are also deemed essential in strain PAO1 17 ( Figure 2A ) while 210 essential genes in PA14 are absent from the PAO1 genome ( Table S1 ) ., Forty-five ( 0 . 77% ) genes had Tn insertions with more than 1 , 000 sequencing reads , and they had an average adenine-plus-thymine ( A+T ) composition of 40 . 2% , whereas the ( A+T ) content of the entire genome of PA14 is 33 . 7% ( www . pseudomonas . com ) ., This increase in the density of Tn insertions in A/T rich genes is likely due to the site preference of the mariner transposon , targeting any A/T dinucleotide 16 , 18 ., The gene with the largest number of Tn insertions was PA14_39470 ( 7 , 399 reads ) , encoding for a hypothetical protein that has an A+T content of 57% ( Figure S1 and Table S2 ) ., Interestingly , strains with Tn insertions in some well-characterized global regulatory genes such as the vfr , mvfR and lasR that control many aspects of quorum sensing ( QS ) and virulence in P . aeruginosa 19 were also highly represented in the input library , with more than 1 , 000 reads recovered per gene in the LB-grown Tn insertion pool ., The corresponding Tn insertions were absent from the strains recovered from the spleens ., Thus , maintaining an intact QS capability is essential for P . aeruginosas ability to cause systemic infection but has little selective advantage for survival in planktonic cultures ., The sequencing results allowed us to comprehensively analyze at the full genomic scale the genetic basis for the relative contributions to overall fitness of P . aeruginosa under these in vivo conditions ., By ranking the differential changes in the recovery of the Tn insertions from LB with the cecal and the splenic outputs , we determined the contribution to fitness in three specific areas ( Figure S2 ) : 27 functional genomic classes 20 ( www . pseudomonas . com ) ( Figure 2B ) ; individual operons; and finally , genes potentially encoding targets for immunotherapy ., These were defined as those making a strong contribution to virulence due to a >10-fold decrease in sequencing reads when comparing the splenic and cecal outputs , but with reads not decreasing more than two times from the LB to the cecum and with at least 10 reads in the cecum ( i . e . , able to colonize this tissue ) , combined with an annotation indicative of a likely outer membrane location and thus surface exposure ( Table S3 ) ., The analyses of the 27 described functional genomic classes of P . aeruginosa revealed differential requirements for colonization and systemic spread ., In all but three categories , a decrease in the number of reads from the input pool of LB grown Tn-insertion strains was obtained when compared to the sequencing reads in Tn insertions recovered from ceca ( Figure 2B ) ., Interestingly , we found 89 genes where Tn insertions resulted in positive selection during Gastrointestinal ( GI ) colonization , defined as a ≥2-fold increase in sequencing reads of the bacterial population in the cecal output compared to the LB input , with a minimum of 10 reads ( Table S4 ) ., Insertions in genes belonging to three functional classes ( “Transport of Small Molecules , ” “Motility and Attachment” and “Chemotaxis ) represented the majority of those undergoing positive selection during cecal colonization . Among these 89 positively selected Tn insertions only a subset were also recovered from the spleens , with most unable to systemically disseminate . Thus , strains with Tn insertions resulting in enhanced GI colonization mostly displayed reduced fitness for systemic dissemination during neutropenia . A closer examination of the specific genes with Tn insertions leading to enhanced cecal colonization revealed a consistent trend among those involved in the formation of type IVa pili 21 ( Figures 3 and S3 ) . The Tn insertions were scattered among several gene clusters around the P . aeruginosa chromosome encoding proteins associated with production of the pilus structural components , including regulation of expression and assembly . The shared phenotype for all of these Tn insertion strains is the lack of synthesis of the pilin subunit and/or the absence of pili on the bacterial surface . Strong positive selection was demonstrated for strains with Tn insertions in genes specifying structural components and biogenesis functions ( pilA , B , C , D , pilE , G , H , I , J , K , M , N , O , P , W , X , Y1 , Z and fimU ) as well as the pilS/pilR encoding for the pilin gene specific regulatory two-component system . In contrast , genes that have been previously shown not to have an effect on piliation ( pilK ) and those that do not affect pilus assembly but whose loss appears to result in a hyper-piliated phenotype leading to defective twitching motility ( pilU and pilT ) 22 , 23 , 24 did not display an increased fitness for cecal colonization , although Tn-insertions into pilH , which had decreased in vivo fitness , had an overlapping phenotype with Tn-insertions into the pilU and pilT genes 25 . There was also a positive selection for cecal colonization for all of the Tn insertions in chpA , a pilin-linked chemosensory system ( Figures 3 and S3 ) . Insertions in pilF , encoding a lipoprotein needed for proper membrane localization and multimerization of the secretin , PilQ that are both essential for type IVa pilus synthesis displayed poor growth in LB with only 5 . 8 reads ( i . e . , essential ) compared to an average of 479 . 8 reads for the Tn insertions in the other pilin genes in strains grown in LB . The basis for this is unclear as a Tn-insertion in pilF in P . aeruginosa strain PAO1 did not appear to affect growth 26 . The enhanced GI colonization by the strains defective in the production of Type IVa pili suggests that during this stage of infection the pilus structure is a potential target of early recognition by host defense mechanisms , promoting clearance of WT P . aeruginosa PA14 . Notably , enhanced colonization did not lead to enhanced dissemination as every strain with a Tn insertion in a gene that affected the production or function of the Type IVa pili was recovered in reduced numbers in the spleens ( Figures 3 and S4 ) , indicating that these organelles play a critical role in systemic dissemination . However , as a majority of the population maintained production of these possible PAMPs , another explanation such as greater metabolic efficiency from loss of pilus production could account for the observed colonization advantage of pilus-defective mutants . Several strains with Tn insertions in genes encoding three global regulators of multiple virulence factors , including alginate and pilus production ( rpoN , algZ , algR , Table S4 ) were also over-represented in the ceca compared to LB . Since mutations in genes involved in the biosynthesis of the alginate exopolysaccharide showed reduced fitness for cecal colonization ( see below ) , these findings indicate that the increased fitness of organisms lacking pili compensates for a potential decrease in the ability of alginate-defective bacteria to colonize . A number of additional surface adhesive organelles , some of which assemble into pilus-like filaments , have been described in P . aeruginosa 27 , 28 , 29 . In contrast to the type IVa class , we observed uniform attenuation in GI colonization of Tn insertions in genes encoding the Cup fimbriae ( cupA , B , C , E ) and the Type IVb1 ( flp ) pilus ( Figure 3 ) . Interestingly , insertions into genes encoding for two fimbrial systems within in the PAPI-1 pathogenicity island , CupD and Type IVb2 pili , also resulted in poor cecal colonization and subsequently , poor systemic dissemination ( Figure 3 ) . Although common features of all these surface fimbrial structures is their role in attachment to abiotic surfaces or to each other , only the Type IVa pili appear to have an inhibitory role in establishing GI mucosal colonization . The strongest positively selected strains were those with Tn insertions into the oprD gene encompassing 42% of the Tn insertions recovered from the ceca . They were enriched 816-fold compared to LB ( 420 , 621 reads vs 515 reads; Figure 4 ) and also comprised a striking 94 . 7% of the strains recovered from the spleens ( 947 , 397 reads; Figure 4 ) . OprD is an outer membrane porin previously identified as the main channel for the entry of carbapenem antibiotics into the P . aeruginosa periplasm and its loss confers resistance to this important class of antimicrobials 30 . Enhanced fitness of oprD-deficient strains both during cecal colonization and systemic dissemination indicates that the expression of this protein in WT P . aeruginosa may be detrimental for its survival in mice . OprD could be either involved in mediating the transport of toxic molecules found in mouse tissues or , like the Type IVa pili , this outer membrane protein could present a target for recognition and elimination by the innate host defenses . A detailed analysis of the molecular basis for this enhanced fitness and the role of carbapenem resistance in pathogenesis of P . aeruginosa is currently ongoing . Strains with Tn insertions in 13 genes encoding components of the P . aeruginosa flagella system were also positively selected , consistent with the known ability of flagellin to activate host innate immunity via the Toll-like receptor ( TLR ) 5 and cytosolic innate immune responses that promote bacterial clearance 31 , 32 , 33 ( Table S4 ) . To avoid any bias due to an overrepresentation in the drinking water of the Tn-insertions in either the oprD gene or the genes needed for type IVa pili production , we sequenced the bank of mutants present after 48 h in the drinking water . Tn-insertions in oprD represented less than 0 . 5% of the mutants in the water while the sum of the Tn-insertions in the genes encoding the type IVa pili that represented 34% of the Tn-insertions recovered from the ceca represented only 1 . 15% of the Tn-insertions in the water . Thus , selection in the drinking water did not account for the enhanced recovery of oprD and type IVa pilus Tn-inserts in the cecum . As expected , negative selection was the predominant feature of the changes in the relative proportions of the Tn insertions comparing the LB-grown input pool to the cecal output pool with Tn insertions in 1 , 333 genes ( 24 . 9% ) able to grow in the input pool that were completely absent from the cecal output pool ( Table S5 ) . Among these 1 , 333 genes with Tn insertions absent in this pool the largest number , 583 ( 43 . 7% ) , are annotated as hypothetical , unclassified or unknown . These could represent an interesting group of genes for further research towards assigning their products biological functions based on the phenotype of reduced fitness during animal colonization . The analysis of the distribution of Tn insertions combined with the direction and magnitude of the resultant fitness phenotype can be exploited for the identification of genes that are coordinately regulated and therefore could be related by function . Many of these are found in operons and we therefore analyzed the INSeq results utilizing the previously developed list of defined transcriptional units ( TU ) of P . aeruginosa PA14 34 . We organized the TUs into three functional categories ( virulence factors , aerobic/anaerobic respiration and utilization of nutrients ) since this would be most informative about P . aeruginosa fitness for survival and proliferation in the mammalian host environment ( Figures 5 and 6 ) . For this analysis and the discussion of phenotypes , the effects of different insertions within an operon are considered comparable , regardless of the location of insertions , whether they arose by polar effects on the downstream gene or by direct inactivation of individual genes . This assumption is not unreasonable , considering most of the genes within operons affect related functions . Similarly , for insertions that are thought to be linked to the same pathway , a double mutation should not have an additive effect on fitness . From this analysis we were able to assign putative functions to groups of previously un-annotated genes due to the similarity of their sequences to other annotated genes as well as exhibiting quantitative changes in the output comparable to the annotated genes . The 113 operons of un-annotated genes found to be important for GI colonization were classified as operons specific to the PA14 genome ( Table S6 ) , operons generally found among sequenced P . aeruginosa strains ( Table S7 ) or operons also present in other Pseudomonas species ( Table S8 ) . Analysis of the negatively-selected genes and operons showed that all of the known P . aeruginosa virulence factors compiled in the Virulence Factors of Pathogenic Bacteria database ( http://www . mgc . ac . cn/VFs/main . htm ) contribute to the overall fitness of P . aeruginosa for both colonization and systemic spread ( Figure 5A and Figure S5 for a detailed representation of the genes ) . For example , strains with Tn insertions in all of the known genes associated with extracellular secretion pathways of P . aeruginosa ( Types 1–3 , 5 and 6 secretion systems ) , including all their previously-characterized secreted effectors , were recovered from the ceca in reduced numbers relative to their representation in LB and were absent from the spleens ( Figure 5A ) . Moreover , we identified a cluster of Tn insertions in previously un-annotated genes that were weak colonizers and unable to disseminate to the spleens ( Figure S5 ) . Careful analysis of sequence similarities to proteins in GenBank led to the conclusion that these represent coding sequences for a new Type 1 secretion system ( T1SS ) that appears to be expressed and functional , and a truncated T2SS similar to the HplR-X export machinery in strain PAO1 ( Figure S6A ) 35 . These two secretion systems appear to mediate the secretion of virulence factors that contribute to the fitness of P . aeruginosa in the host during GI colonization and systemic spread , although it was not determined precisely where and when in the infection cycle they are functional . The new T1SS was defined by insertions in the putative T1SS operon ( PA14_40230-60 ) that has limited sequence similarity to the Apr-T1SS and Has-T1SS . The first three genes in this cluster , PA14_40230 , PA14_40240 , PA14_40250 encode products belonging to the HlyD family of membrane fusion proteins , an ABC transporter and a predicted outer membrane protein , respectively , an arrangement found in all T1SSs ( Figure S6A ) . The factor secreted by this new T1SS is likely encoded by the last gene of the operon PA14_40260 that is predicted to encode a large protein ( 1 , 256 amino acids ) with 17 repeats of the Big_3_4 sequence ( “Bacterial Ig-like domain” , Pfam PF13754 ) and a single C-terminal domain called a SWM_repeat ( “Putative flagellar system-associated repeat” , Pfam PF13753 ) ., It has similarity to the LapA protein that functions as an adhesin during colonization of plant seeds by P . putida 36 and to an ortholog in P . fluorescens that plays a role in irreversible attachment to abiotic surfaces during biofilm formation 37 ., It also shares sequence similarity to several proteins of Gram-negative bacteria annotated as hemolysins/hemagglutinins ., A second set of clustered insertions were in an operon that likely represents a truncated T2SS similar to the hplR-X genes described in strain PA01 35 composed of a cluster of 10 genes ( PA14_29480-570 ) ( Figure S6A ) ., They include proteins encoding an ABC transporter , proteins with 69 , 49 , 57 , 45 , 51 , 65 and 43% sequence similarities to XcpRSTVW , HxcW and XcpX , respectively , an MFS transporter and a hypothetical protein ( Figure S6 ) ., Missing from this operon are genes encoding the components needed for a complete T2SS including homologs of XcpP/HxcP and XcpQ/HxcQ ., Products of two unlinked genes xphA and xqhA that were also reduced in frequency in the cecal output could conceivably carry out this function ., Also missing from this cluster is the gene for the prepilin peptidase required for processing of the pseudopilin components of the T2SS ( PA14_21950-PA14_29550 ) , however , it is possible that this function is provided by the product of the highly-conserved pilD ( xcpA ) gene ., In addition to observing the expected negative selection of Tn insertions into genes needed for LPS O-side chain production , whose loss greatly increases the serum sensitivity of P . aeruginosa 38 , Tn insertions into genes involved in synthesis of the Pel and alginate polysaccharides also showed reduced fitness for colonization and dissemination ( Figure S6B ) 39 , 40 ., Pel ( PA14_22480-22560 ) has been previously shown to be a component of the biofilm matrix promoting bacterial attachment to each other or to inert surfaces ., While less firmly associated with biofilm formation by phenotypically non-mucoid ( i . e . low alginate ) strains like PA14 , alginate is a strong candidate for a bona fide virulence factor for strain PA14 as Tn insertions into genes within the alginate biosynthetic or regulatory operons led to a colonization defect ( Figure S6B ) ., Exceptions were in certain genes involved in global regulation of alginate expression ( rpoN , algZ , algR ) , where they also controlled the expression of Type IVa pili ., Thus , their inactivation by Tn insertions likely provided a fitness benefit by eliminating the production of pili although at this point we cannot exclude more pleiotropic effects resulting from loss of these global regulatory genes ., Nonetheless , these findings indicate that either significant amounts of alginate are induced early during animal colonization , or the low levels of alginate produced by non-mucoid strains provides the bacterial cells with a fitness benefit needed to establish mucosal infection ., An interesting finding was decreased colonization fitness in strains with Tn insertions in the PA14_35550-35690 genes , annotated as pslE-pslO ., In strain PAO1 and 4 other sequenced P . aeruginosa strains , the orthologs of these genes are part of a larger operon containing 4 additional genes , pslABCD , needed for synthesis of the Psl polysaccharide ., The lack of pslABCD in strain PA14 results in no synthesis of the Psl polysaccharide , yet the decreased colonization fitness of Tn insertions in the PA14_35550-35690 genes indicates they have a role in P . aeruginosa GI colonization unrelated to Psl polysaccharide synthesis ., Two clusters of genes , PA14_07990-08300 and PA14_48880-49010 correspond to integrated prophages , similar to phages 1 and 6 found as either intact or partial genomes in the chromosome of various P . aeruginosa strains 41 ., The INSeq analysis for fitness in the ceca and spleens demonstrated that Tn insertions in the phage 1 and 6 genes in strain PA14 had reduced fitness ( Figure S8B ) ., Except for the Tn insertions in the gpI gene ( PA14_08040 ) , all the Tn insertions in all of the other genes for these two prophages were weak GI colonizers and none were able to disseminate to the spleens ( zero reads recovered for all of them; Figure S8B ) ., Interestingly , the transcription of genes from these two prophages in strain PAO1 grown under anaerobic conditions in the presence of nitrate and nitrite were highly up-regulated 42 , consistent with a role for these prophages in P . aeruginosa in vivo fitness ., The supporting information text ( Text S1 ) contains detailed descriptions of the InSeq results for these aspects of P . aeruginosa pathogenesis and host responses ., Strikingly , even in neutropenic mice , among the 4 , 522 strains carrying Tn insertions in genes that still allowed some level of colonization of the mouse ceca , only 493 ( 10 . 9% ) of these Tn insertion strains were able to disseminate to the spleens ( Table S9 ) ., This unexpected large number of P . aeruginosa genes essential for systemic dissemination during neutropenia highlights the complexity and cooperation among the various bacterial factors needed to overcome host barriers and disseminate into organs ., To validate the INSeq approach for identification of genes and gene products affecting P . aeruginosa fitness , we used competitive challenges by inoculating mice with a 1∶1 mixture of WT P . aeruginosa PA14 and different strains selected from the ordered Tn insertion library created in the same strain 16 ., The Tn insertions were chosen based on their relative fitness for GI tract colonization as ascertained by the number of sequencing reads recovered ( Figure 7A ) and included the Tn-algJ and Tn-nirF strains with reduced fitness , the Tn-PA14-64320 and Tn-nirQ strains with slightly reduced and slightly increased fitness for colonization , respectively , and the Tn-pilE strain with enhanced fitness for colonization ., We confirmed that the Tn insertion in the pilE gene resulted in the expected loss of twitching motility ( Figure 7C ) ., Overall , the competition experiments validated the INSeq results , with the more fit Tn-pilE strain representing more than 99% of the strains recovered from the ceca after inoculation with a 1∶1 mixture of WT strain PA14 ( Figure 7B ) and the less fit PA14 Tn-algJ and Tn-nirF insertions representing , on average , only 25% and 20% of the recovered isolates relative to WT , respectively , from the tested mice ., For the two strains with intermediate Tn insertions , on average , 50% recovered from the ceca were the PA14 Tn-nirQ strain while the PA14 Tn-64320 insertion strain , which had lower reads than the Tn-nirQ insertion strains , survived on average , at a level of about 38% compared to WT PA14 ( Figure 7B ) ., Thus the ratio of strains with Tn insertions in these five genes to WT P . aeruginosa determined by INSeq were experimentally verified by direct in vivo competition experiments ., The increased fitness of a mutant in the formation of type IVa pili was also confirmed using a strain with a clean deletion of the pilA gene ( Figure 7B ) ., It is important to rule out a potential bottleneck effect that could account for the high level of recovery of Tn_oprD strains from the spleen ., We therefore hypothesized that strains with Tn-insertions capable of both colonizing the GI tract and disseminating to the spleen in the original saturated Tn library would be recovered from the spleens of neutropenic mice ( following 6 days of GI colonization ) when in direct competition with a Tn_oprD strain at relative levels comparable to those detected in the original bank ., In contrast , Tn-insertions in genes that still allowed for measurable GI colonization but eliminated the ability to spread systemically would similarly be restricted from spreading when in competition with a Tn_oprD strain ., In these experiments the Tn_oprD strain selected from the ordered Tn-insertion library in strain PA14 16 was mixed at a 1∶1 ratio with one of six strains from this same library with Tn-insertions in un-annotated genes , representing strains with Tn insertions that were recovered from the GI tract with 30–2 , 800 RPKM reads , indicative of a range of abilities to colonize the cecum ., Three of the Tn-insertion strains ( PA14 Tn-68490 , PA14 Tn-53820 and PA14 Tn-61020 ) were also able to disseminate to the spleen at three different levels ( ∼7×102 RPKM , 7×103 RPKM and 2×104 RPKM , Figure 8A ) when present in the original saturated Tn-insert library , whereas the additional three Tn-insertion strains , although able to colonize the GI tract , were unable to disseminate to the spleen ( strains PA14 Tn-02460 , PA14 Tn-12550 , and PA14 Tn-10530 ) ( Figure 8A ) ., The three strains with Tn-insertions in genes that had been able to disseminate when part of the original Tn-insert library were recovered from the spleens when in competition with the Tn-oprD strain at levels comparable to their in vivo fitness in the entire saturated Tn-insertion library ( Figure 8B ) ., This result indicated that the mutants with the Tn insert in the oprD gene did not disseminate to the spleen due to an advantage reflecting a bottleneck effect ., The three strains unable to disseminate to the spleen from the initial saturated Tn-insertion library were recovered at levels of <2% when in competition with the Tn_oprD strain ., These outcomes confirmed that strains in the original Tn library with Tn inserts that essentially completely compromised their ability to disseminate systemically in the setting of neutropenia were similarly unable to do so when placed in competition with a Tn_oprD strain , indicative of a true selective advantage for the Tn_oprD strain ., We used the INSeq technique and high-throughput sequencing that determine the location and abundance of Tn insertions in a chromosome to assess the comparative fitness of strains with these insertions in P . aeruginosa PA14 grown in laboratory media , recovered from the ceca of colonized mice and then from the spleens of neutropenic mice ., Fitness determinations were based on the normalized quantification of the changes in the relative amounts of the Tn-interrupted genes determined in each population ., The findings produced a data set that a

Introduction, Results, Discussion, Materials and Methods

High-throughput sequencing of transposon ( Tn ) libraries created within entire genomes identifies and quantifies the contribution of individual genes and operons to the fitness of organisms in different environments ., We used insertion-sequencing ( INSeq ) to analyze the contribution to fitness of all non-essential genes in the chromosome of Pseudomonas aeruginosa strain PA14 based on a library of ∼300 , 000 individual Tn insertions ., In vitro growth in LB provided a baseline for comparison with the survival of the Tn insertion strains following 6 days of colonization of the murine gastrointestinal tract as well as a comparison with Tn-inserts subsequently able to systemically disseminate to the spleen following induction of neutropenia ., Sequencing was performed following DNA extraction from the recovered bacteria , digestion with the MmeI restriction enzyme that hydrolyzes DNA 16 bp away from the end of the Tn insert , and fractionation into oligonucleotides of 1 , 200–1 , 500 bp that were prepared for high-throughput sequencing ., Changes in frequency of Tn inserts into the P . aeruginosa genome were used to quantify in vivo fitness resulting from loss of a gene ., 636 genes had <10 sequencing reads in LB , thus defined as unable to grow in this medium ., During in vivo infection there were major losses of strains with Tn inserts in almost all known virulence factors , as well as respiration , energy utilization , ion pumps , nutritional genes and prophages ., Many new candidates for virulence factors were also identified ., There were consistent changes in the recovery of Tn inserts in genes within most operons and Tn insertions into some genes enhanced in vivo fitness ., Strikingly , 90% of the non-essential genes were required for in vivo survival following systemic dissemination during neutropenia ., These experiments resulted in the identification of the P . aeruginosa strain PA14 genes necessary for optimal survival in the mucosal and systemic environments of a mammalian host .

To determine the contribution of all non-essential genes of Pseudomonas aeruginosa to overall fitness in laboratory and infectious settings , we used a high throughput sequencing method that quantitatively identifies the bacterial genes interrupted by random insertion of transposons ( Tn ) into the genome of strain PA14 ., This Tn-library was used to colonize the gastrointestinal tract of mice for 6 days after which systemic spread was induced by making mice neutropenic ., DNA from the bacteria recovered from the cecum of colonized mice and spleens of systemically infected mice underwent high-throughput sequencing leading to identification of all non-essential Tn-interrupted genes whose levels were either increased or decreased in these selective situations compared to growth in a laboratory medium ., A complete dataset was generated that yielded a comprehensive view of the contribution of all non-essential genes of P . aeruginosa to genetic fitness in in vivo settings .

systems biology, medicine, infectious diseases, genetics, immunology, biology, microbiology, critical care and emergency medicine

journal.pcbi.0030174

A Computational Approach to the Functional Screening of Genomes

The search for LUCA , the Last Unknown Common Ancestor , is an open problem in evolutionary theory , which has been addressed using many different approaches ., After the completion of several bacterial genomes , some authors tried to infer a possible minimal genome ruling out of non essential genes from existing small bacterial genomes ., Dispensable genes were detected using both wet-lab techniques ( e . g . , see 1 , 2 ) and comparative genomics methods ( e . g . , see 3 ) ., The implicit hypothesis underlying these approaches is that the ancestor genome is made of singular elements only , and therefore would have a minimum size ., We are aware of the criticisms raised about this hypothesis ( e . g . , see 4 , 5 ) , but a discussion on this subject would be off-topic for the present paper ., Instead , we shall examine how such a simplified organism can be inferred by a comparative genomics approach , specifically following Mushegian and Koonin 3 ., They considered the two very small genomes of Haemophylus influenzae and Mycoplasma genitalium , and manually scanned the two correspondent gene lists , so as to remove any element that looked redundant for biological function ., The final result of this work was the so-called Minimal Gene Set ( MGS ) , made of 254 singular genes ( the original paper declared 256 genes , but two genes , -mg297 and mg336- , have been counted twice ) ., This hypothetical minimal genome was claimed to specify for a very essential prokaryote , but no argument was provided to address the fundamental question of whether a cell equipped with MGS ( call it MGS-prokaryote ) is able to live or not ., A direct , biological approach to answer this question could consist in synthesizing this genome , in cloning it in a ghost bacterium , and in evaluating the overall cell viability ., However , there are many severe technical problems along this way , which make it hard to get an answer quickly ., We instead described this hypothetical cell as a computer program and simulated its behavior in silico ., We then tested whether it shows some fundamental properties of living organisms ., First of all we checked whether it enjoys homeostasis , i . e . , the capability to reach a steady state in which the concentration of all the chemical species inside the cell fluctuates within a narrow range ., We also investigated the capability of a MGS-prokaryote to produce biomass ., To model the MGS-prokaryote , we used ( a variant of ) the π-calculus , a process calculus designed to specify concurrent processes , which has already been used to describe biological phenomena 6 , 7 ., We represented a complete set of metabolites , metabolic pathways , etc . , involving the genes of the MGS-prokaryote ., We ran in silico simulations and we observed the concentration of fundamental metabolites ( ATP , NADH , etc . ) , checking the trend of their time courses toward constant values ., To specify the metabolites and their relationships in terms of biochemical reactions , we used an enhanced version of the π-calculus , which has already been shown to be suitable for describing biological entities 8 , 9 ., We refer the reader to 10 and 11 for a complete presentation of the ( enhanced ) π-calculus , and here we survey very briefly its fundamentals ., The π-calculus was designed to express , run , and reason about concurrent systems ., These are abstract systems composed of processes , i . e . , autonomous , independent processing units that run in parallel and eventually communicate , by exchanging messages through channels ., A biochemical reaction between two metabolites , catalyzed by an enzyme , can be modeled in π-calculus as a communication ., The two metabolites are represented by two processes , and , in our approach , the enzyme is modeled as the channel which permits the communication ., In addition to communications , the π-calculus also allows us to specify silent internal actions , used to model those activities of the cell , the details of which we are not interested in ( e . g . , the pure presence of a catalyst in a reaction , where it is not actively involved ) ., The calculus has the means to express alternative behavior , when a metabolite can act in different possible manners: the way to follow is chosen according to a given probability distribution ., The main difference between the standard π-calculus and the enhanced version we used in this work is the notion of address ., An address is a unique identifier of a process , totally transparent to the user , automatically assigned to all of its child subprocesses ., This labeling technique helps in tracking the history of virtual metabolites and reasoning about computations , in a purely mechanical way ., In particular , stochastic implementation or causality are kept implicit and are recovered as needed ., The enhanced π-calculus shares with other language-based approaches a number of advantages with respect to other formal descriptions ., The very specification of the cell is actually a program and can be executed , giving rise to a virtual experiment , unlike other static descriptions such as the SBML 12 ., Additionally , specifications turn out to be rather compact when compared , for example , with those expressed by P-systems 13 , which , however , also describe membranes and their activities that we neglect here ., Also , the specification of a whole organism is given by composing its constituents in a remarkably straightforward way ., This is sometimes not the case with other approaches , e . g . , those based on Petri Nets and used since 14 , that have a nice graphical notation , but lack a linguistic framework ., For a survey on process calculi for modeling biological entities , see also 15 ., We specified in the π-calculus all the elements of the molecular machinery of the cell ., Each element is specified in isolation , only defining its potential interactions with the environment ., Then these pieces are put together in a compositional , holistic fashion ., We wrote an interpreter for the enhanced π-calculus in Java , and we used it to run simulations ., Simulations play the role of virtual experiments , performed according to given different initial conditions ., The input file contains the definitions of all the metabolites inside the cell , the initial inner concentrations of the metabolites , and the rates of enzymatic activities , derived from the available real experimental data ., The interpreter stores and displays some information about the virtual experiment , typically the concentrations of all the virtual metabolites ( i . e . , the number of the corresponding processes ) or the usage of the different enzymes ( i . e . , the number of accesses of each channel ) at given instants ., With the first output , we determined the time course of the concentration of any virtual metabolite during the simulation; with the second one , we inspected the usage rate of the enzymes specified in the definitions , and , therefore , we tested the presence of unused metabolic pathways ., The MGS-prokaryote has been exhaustively described in the enhanced π-calculus ., We represented the 237 genes , their relative products , and the metabolic pathways expressed and regulated by the genes , as the corresponding processes and channels ., In particular: the Glycolytic Pathway , the Pentose Phosphate Pathway , the pathways involved in nucleotide , aminoacids , coenzyme , lipids , and glycerol metabolism ., Moreover , MGS genes encode for a set of membrane carriers for metabolite uptake , including the PTS carrier ., We placed this virtual cell in an optimal virtual environment , in which all nutrients and water were available , and where no problems were present in eliminating waste substances ., A large number of simulations ( about 5 , 000 ) have been run , differing in the values of the initial parameters ., We independently varied the amount of glucose in the extracellular environment ( the number of copies was in the range 100–5 , 000 ) and the time interval of observation T ( in the range 10–10 , 000 ) ., Recall that in our model time steps correspond to the occurrence of transitions , so we set T establishing the length of the computations performed by the simulator ., In all the studied cases , the MGS-prokaryote could not reach a steady state; most of the essential metabolites fell to zero in a short period , as is clearly shown in Figures 1 and 2 , which display the typical time course of ATP and 2-Acyl-Glycerol ( 2AG ) ., These results lead us to the conclusion that this MGS-based cell was not able to live , at least in silico ., Our approach to a functional screening of genomes was shown to be valid ., In particular , our results have been obtained very cheaply with respect to a possible wet-lab approach involving de novo synthesis of the examined genome ., Clearly , if a hypothetical genomes does not pass the in silico test , it will be unlikely to give rise to a living organism ., It is hard to sustain the opposite: we cannot affirm that a hypothetical genome passing the test is able to sustain a living organism , and only a wet-lab approach can validate the proposal ., Indeed , in silico experiments can help us to select which proposals are coherent , and thus more promising ., As evidence of this , our work shows that the minimal genome we proposed for ViCe is surely more biologically reliable than an MGS .

Introduction, Methods, Results/Discussion

Comparative genomics usually involves managing the functional aspects of genomes , by simply comparing gene-by-gene functions ., Following this approach , Mushegian and Koonin proposed a hypothetical minimal genome , Minimal Gene Set ( MGS ) , aiming for a possible oldest ancestor genome ., They obtained MGS by comparing the genomes of two simple bacteria and eliminating duplicated or functionally identical genes ., The authors raised the fundamental question of whether a hypothetical organism possessing MGS is able to live or not ., We attacked this viability problem specifying in silico the metabolic pathways of the MGS-based prokaryote ., We then performed a dynamic simulation of cellular metabolic activities in order to check whether the MGS-prokaryote reaches some equilibrium state and produces the necessary biomass ., We assumed these two conditions to be necessary for a living organism ., Our simulations clearly show that the MGS does not express an organism that is able to live ., We then iteratively proceeded with functional replacements in order to obtain a genome composition that gives rise to equilibrium ., We ruled out 76 of the original 254 genes in the MGS , because they resulted in duplication from a functional point of view ., We also added seven genes not present in the MGS ., These genes encode for enzymes involved in critical nodes of the metabolic network ., These modifications led to a genome composed of 187 elements expressing a virtually living organism , Virtual Cell ( ViCe ) , that exhibits homeostatic capabilities and produces biomass ., Moreover , the steady-state distribution of the concentrations of virtual metabolites that resulted was similar to that experimentally measured in bacteria ., We conclude then that ViCe is able to “live in silico . ”

The origins of life represent a fascinating problem that has been addressed using different approaches and a wide variety of technologies ., A theoretical approach consists of inferring a possible oldest ancestor genome from a well-defined comparison of current ones ., A crucial problem concerns the validation of the proposed genome ., The direct solution of synthesizing such a genome in a laboratory is often extremely difficult , due to the great complexity of a biological cell ., In this paper , we present an approach for evaluating the chances a hypothetical organism has to be really viable , relying on computer simulations ., Our method is based on a certain formal language , through which we specify a whole metabolic network , and we study its dynamics , in particular for verifying if a living organism has some fundamental properties , e . g . , homeostasis ., This approach is not equivalent to a wet-lab one , but it allows for early pruning of most of the inconsistently designed hypothetical organisms , thus saving biologists time and resources .

none, computational biology

journal.pcbi.1003388

Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota

The intestinal microbiota has been receiving much attention lately ., Recent studies , propelled by metagenomics and next-generation DNA sequencing technologies , establish novel connections between the intestinal microbial species composition and diseases 1–3 ., An imbalance in bacterial composition has been linked to chronic diseases such as obesity 4 , Crohns disease 5 and type 2 diabetes 6 ., Even drug-induced transient changes in the microbial community can increase the risk of developing diseases such as acute intestinal infections 7 , or pulmonary viral infections 8 in mammalian hosts ., Although its importance has long been acknowledged 9–12 studies of the microbiota had been limited by the fact that most microbes are uncultivable in the lab ., The recent developments in metagenomic high-throughput sequencing allow this by enabling the investigation of species composition directly without the need for culturing 13 ., This has opened a new window into the microbial ecosystem residing in the intestinal tract ., Our present view is that the intestinal microbiota is a relatively resilient ecosystem 14 , with a composition that is quite stable over time 15 , 16 ., External perturbations , such as dramatic changes in diet 17 or antibiotic administration 18 , can shift the composition ., For example , broad-spectrum antibiotics can remove highly abundant species and allow less abundant , antibiotic-tolerant bacteria to dominate 7 ., Antibiotic-induced losses of biodiversity increase the risk of bacterial infections 19 , 20 and the effects can persist for several days after antibiotic treatment 18 , 19 , 21 ., Perturbation-induced composition shifts are often observed in multispecies microbial ecosystems and may affect macroscopic overall functionality 22 ., The loss of protective species can be resolved by reintroducing normally resident ( or commensal ) microbes ., Faecal transplantation , i . e . the reestablishment of a patients intestinal microbiota by introducing the microbiota of a healthy donor , is highly effective against Clostridium difficile induced colitis 23 , 24 ., Similarly , the reintroduction of anaerobic flora with high levels of Barnesiella sp ., can clear intestines from highly abundant vancomycin-resistant Enteroccocus in mice 25 ., In order to understand how commensal consortia confer resistance against pathogens it is crucial to identify the network of interactions between the species 26 ., Interactions can be mediated by a direct secretion of substances such as bacteriocins 27 , or ecological competition between the microbes 28 , or even indirect interactions through immune system modulation 29 ., Most quantitative studies of the intestinal microbiota so far focused on comparing the composition of different samples using quantitative indices and correspondence analyses 14 and cross-sectional statistical tests 1 , 30 ., Likewise , associations between microbial species are often obtained using correlation-based algorithms 26 , 31–36 , which results in undirected interaction networks ., Singular value decomposition 28 or mixture model engines 37 allow for individuating stereotypical modes of response to external perturbations ( i . e . grouping species positively or negatively affected by the stimulus ) but they provide no information on the interactions themselves ( Figure 1A ) ., We recently introduced an ecological model of microbiota dynamics that considers both species interaction networks and extrinsic perturbations such as antibiotics 28 ., The model can explain how relatively simple ecological interactions such as competition for nutrients can lead to complex phenomena as , for example , multi-stability or antibiotic-mediated catastrophic shifts ., Importantly , we concluded that quantitative knowledge of the microbial interactions could enable the prediction of microbiota dynamics ., Predictive models can be of great therapeutic value by guiding antibiotic selection to reduce the risk of antibiotic-induced enteric disease 20 ., However , no study to date has generated predictive models of ecological interactions and antibiotic perturbations ., Inspired by work on interaction inference in cheese-associated microbial communities 38 we extend the generalized Lotka–Volterra equations 39 , 40 to infer microbiota ecology and predict its temporal dynamics under time-dependent external perturbations ., A related approach based on linear ordinary differential equations has already been applied to gene-interaction networks 41–44 ., Specifically , our method enables the quantification of ( 1 ) growth rates of microbial species , ( 2 ) species–species interactions , and ( 3 ) susceptibilities of microbial groups to time-variable external perturbations such as antibiotics ., Moreover , we can use these parameters to numerically predict dynamics of the microbiota and to characterize its stability ( Figure 1B ) ., Using this method , we analyze data from a recent mouse study 19 , which shows that the antibiotic clindamycin increases susceptibility to Clostridium difficile colonization ., Our results suggest the existence of resilience and multistability in the intestinal microbiota and lead to a hypothesis on a subnetwork of microbial groups involved in the native resistance against pathogen colonization ., This study demonstrates that data-derived models of microbiota dynamics can have significant analytic and predictive power ., As such , inference and prediction algorithms could be used in combination with metagenomics to assist in the rational design of therapies such as antibiotic or probiotic therapies 12 ., Extracting model parameters using a time-discrete Lotka–Volterra system has already been presented in the context microbial communities 38 , 45 , 46 ., We extend this approach by introducing time-variable perturbations and applying Tikhonov regularization to solve the discretized Lotka–Volterra equations ., Furthermore , we use the obtained parameters to predict dynamics and assess the systems stability ., In this spirit , we adopt the general deterministic approach of modeling time-dependent ecological dynamics using generalized Lotka–Volterra equations 39 with the addition of external perturbations ., Formally , this model consists of autonomous , non-linear , coupled first-order ordinary differential equations , ( 1 ) Here is the concentration of a focal species , , at time , is its specific growth rate , is the effect of the interaction of species on species and is the susceptibility to the time-dependent perturbation ( for instance , an antibiotic or diet ) ., Ecological time-series data , such as longitudinal metagenomic sequencing data 15 , 47 , provide the composition of a community at discrete time points ., Temporally resolved metadata , such as the timing of antibiotic administration 20 or of changes in diet regimes 17 , may also be available and provide information about processes that perturb the microbiota ., In order to translate the time-discrete data to a time-continuous dynamical system we divide ( 1 ) by and discretize ( see Materials and Methods ) , ( 2 ) The model parameters are determined by a linear system of equations , which is then solved using Tikhonov regularization 48 in order to ensure uniqueness and stability of the solution , ( 3 ) The values for the regularization parameters , , can for example be found in -fold cross-validation ( we use ) as the minimizer of the mean-squared stepwise prediction error to set the optimal trade-off between data fit and robustness towards the introduction of unseen or missing data 49 ., The inference method was first tested on in silico data by generating trajectories for a Lotka–Volterra model as defined in ( 1 ) ., We created multiple trajectories of ecological systems characterized by different population sizes , random growth rates , interaction values and susceptibility parameters while ensuring system stability 50 , 51 ., The simulations were also subjected to random perturbations of variable duration and white noise was added to simulate measurement uncertainty ( Figure S1 ) ., The test confirms that the minimum of the stepwise prediction error can be used as a suitable proxy for the minimization of the parameter inference errors ( Figure S2 ) ., Given the inferred parameters we can now predict the temporal dynamics by solving ( 1 ) ., We applied this approach to in silico data ., The results are presented in Figure S3 ., In a recent study , Buffie et al . described experiments on the effect of the antibiotic clindamycin on the intestinal colonization with the spore-forming pathogen C . difficile 19 ., The experiments were performed in a mouse model and high-throughput DNA sequencing was used to measure the relative abundance of bacterial species in cecum and ileum ., The experiment consisted of three distinct populations of mice ., The first population received spores of C . difficile , and was used to determine the susceptibility of the native microbiota to invasion by the pathogen ., The second population received a single dose of clindamycin to assess the effect of the antibiotic alone ., Finally , the third population received a single dose of clindamycin and , on the following day , was inoculated with C . difficile spores ., The untreated mice challenged with C . difficile ( population #1 ) did not develop infection and maintained a stable microbiota throughout the entire experiment ., The single dose of antibiotic ( population #2 ) resulted in a dramatic reduction in the microbiota biodiversity , with more than 90% of the initial species dropping below detection ., The effects of this perturbation were long lasting , and biodiversity did not return to pre-treatment levels even 28 days after the clindamycin dose ., Finally , mice that received C . difficile following the treatment with clindamycin ( population #3 ) were colonized by the pathogen , with 40% of those mice dying due to C . difficile induced colitis ., The experiment was performed in three replicates: for each population three mouse colonies were uniformly treated , but separately housed ., Each time point represents a mouse from each colony which was sacrificed to determine the intestinal microbiota composition ., Mice from the same colony are biological replicates which justifies the interpretation of these compositions as one time line representing one co-housed mouse population 19 ., We used the cecal content data to infer microbial interactions , growth rates and susceptibilities to clindamycin ( see Materials and Methods ) ., Our mechanistically-based model presupposes absolute abundances ., Therefore , we converted the normalized DNA sequence abundances obtained by metagenomics by multiplying with the number of universal 16S rRNA per gram of cecal content ( measured using qPCR ) multiplied by the sample density , 52 ( the actual density value has little importance for the inference of the interactions given the model scaling invariance , see Materials and Methods ) ., For consistency with the previous study 19 we integrated only the ten most abundant genera including the pathogen C . difficile , together accounting for the vast majority ( approx . 90% ) of the total sequences obtained from 16S rRNA high-throughput DNA sequencing ( Figure S4 ) ., The remaining lower abundance microbes were grouped into a category called “Other” ( see Materials and Methods ) ., This choice resulted in less than 30% of undetected entries in the data matrix ., The choice of a higher number of independently treated genera , e . g . 15 , could result in more than 50% of missing values in the data matrix ( Figure S5 ) ., Consistent with the underlying biological assumptions , the specific growth rates obtained from our inference method ( Figure 2A ) are all positive , and concordant with values measured in vitro using representative species of human colonic microbiota ( 0 . 55–1 . 78 per day 53 compared to 0 . 2–0 . 9 from Figure 2A ) ., The diagonal elements of the obtained interaction matrix ( Figure 2B ) are negative ., This is again consistent with the underlying biology , since it means that each of these species would eventually reach carrying capacity even in the absence of other species ., Coprobacillus is found to be the genus with strongest interactions by value in the ecological network ., Specifically , it appears to primarily inhibit all the other microbes , including C . difficile , with the exception of Akkermansia and Blautia which also show inhibitory effect on C . difficile ., The strongest inhibitory effect is on Enterococcus which together with the group of unclassified Mollicutes is inferred to positively interact with the pathogen C . difficile ., This positive association is consistent with previous reports 54 , 55 ., Intriguingly , our method also suggests Barnesiella to mildly inhibit Enterococcus , which agrees with previous findings in mice and humans 25 ., Susceptibilities to clindamycin ( Figure 2C ) propose that the antibiotic inhibits all of the genera , except for Enterococcus and the group of undefined Enterobacteriaceae ., C . difficile itself is mildly repressed by the antibiotic ., Next , we investigated the implications of the inferred model parameters for microbiota dynamics ., First , we tested the models performance in predicting microbiota trajectories ., To do so , we inferred the growth , interaction and susceptibility parameters on of the available data , leaving of the trajectories to test the model prediction ., Subsequently , we solved eq ., ( 1 ) numerically using the inferred parameters , initial compositions and the metadata of antibiotic and/or C . difficile inoculation ( see Materials and Methods for further details ) ., In Figure 3 , we compare the observed dynamics of the second replicates with the dynamics inferred from the first and third replicate ., Figure S6 shows the full comparison for all the three replicates ., The simulated trajectories show a good agreement with the experimental data for all the three populations with respect to order of magnitude and qualitative behavior ., There are , however , discrepancies especially in Figure 3B ., Here , the experimental data shows a community take-over of Akkermansia and Blautia three days after clindamycin treatment ., Our method predicts the same behavior but with several days delay ( see Discussion for possible explications and model limitations ) ., The rank correlation between data and prediction is of 62% along time ( Figure 3D ) ., We then investigated the long-term stability of the system ., We calculated the steady-state composition of the microbiota , , as a solution of eq ., ( 1 ) for vanishing the time-derivatives in the absence of any perturbation ., Consequently , there are steady states where is the number of microbial groups in the system ., Of these , one state corresponds to the trivial case of total extinction ( ) , one state corresponds to the case of total coexistence ( , for invertible ) , and states correspond to the permutations of existence or extinction for every other species 56 ., A priori , we have no knowledge about which one of these states the system will attain ., This depends on the initial composition , presence and duration of the external perturbations ., Therefore , we determine the steady state by simulating long-term dynamics to obtain information on species extinction and coexistence ., Once this information is obtained , we can analytically evaluate the steady state of the system and its qualitative behavior by determining the spectrum of the corresponding Jacobian matrix evaluated in that state ( see Materials and Methods ) ., The principle of linearized stability states that if the real part of the largest eigenvalue of the Jacobian is negative then the composition represents a stable microbiota ( an asymptotically stable state ) ., Otherwise , it is unstable 57 ., For instance , the total extinction state , , is unstable if any of the growth rates is positive , which is true for our data ( Figure 2A ) ., However , the dynamics of high-dimensional Lotka–Volterra systems allow for a large variety of different qualitative behaviors such as limit cycles , chaos or attractors 39 ., We applied this analysis to our system and identified one unique steady state for each independent replicate ( Figure 4A ) ., The replicate corresponding to untreated mice challenged with C . difficile ( population #1 ) is characterized by high abundance of clindamycin-sensitive bacteria such as Barnesiella , undefined Lachnospiraceae and unclassified Lachnospiraceae ., The steady state corresponding to clindamycin application ( population #2 ) is characterized by a take-over by Blautia , unclassified Enterobacteriaceae and unclassified Mollicutes ., Finally , for the case corresponding to C . difficile after clindamycin ( population #3 ) , the steady state predicts severe C . difficile colonization in addition to the genera emerging in population #2 ., Interestingly , these steady states agree in order of magnitude , community profiles and composition with the last experimentally measured data point of Figure 3A–C ., However , in the observed trajectories the composition still changes between the last two observed data points ., This could be due to the fact that the microbiota is not yet stabilized ( i . e . still in transient dynamics ) or due to the effect of fluctuations 15 ., Although this cannot be discerned from a simple observation of the data , assuming that our model captures the actual microbiota ecology our analysis suggests that the microbiota of the perturbed microbial communities did not recover their original composition within 28 days from treatment cessation ., Rather , the microbiota stays in distinct , perturbation-history dependent equilibria ., The intact microbiota is , by antibiotic administration , driven towards a composition which is more susceptible to C . difficile colonization ., By subsequent introduction of the pathogen , the community is dragged into an alternative stable composition including the otherwise repelled C . difficile; this may be an example of “niche opportunity” 58 , 59 ., Interestingly , when considering the landscape of all possible steady states of the inferred Lotka–Volterra model , unstable steady states , i . e . those referring to critical compositions which drive communities with similar compositions to a collapse or catastrophic shift 60 , are significantly more often observed than stable ones ., Given the inferred parameters , we find that of the steady states which the system is able to attain from a composition of L initially present genera , about 98% are found to be unstable ( Figure 4B ) ., Nonetheless , our model predicts the existence of multiple stable compositions in each of the three experimental arms ., Our results , therefore , may indicate the existence of alternative stable compositions of the intestinal microbiota; switches between these states are induced by perturbation with clindamycin or C . difficile inoculation ., This concept is reminiscent of ecological stability and resilience discussed by Connell and Sousa 61 ., The inspection of the model inferred from mouse experiments 19 could suggest a possible ecological mechanism for C . difficile colonization ( Figure 5A ) ., In the intact microbiota , our method proposes that Coprobacillus interacts positively with the genera of Akkermansia and Blautia ., Additionally , Coprobacillus inhibits Enterococcus , which , when increasing in abundance , enhances C . difficile establishment ., Without clindamycin , the three genera Coprobacillus , Akkermansia and Blautia , maintain intestinal stability and confer resistance against C . difficile colonization ( Figure 5B ) ., However , when clindamycin is administered , Coprobacillus , Akkermansia and Blautia , are inhibited while Enterococcus is promoted ., As the three protective groups decrease in abundance , our results suggest that Enterococcus increases in abundance and may facilitate colonization by C . difficile ., We discuss the validity of this mechanism in the Discussion section ., We presented a general method for the inference and prediction of multispecies ecological community dynamics under perturbations ., Although this method was primarily developed having in mind the intestinal microbiota , the same method may be potentially applied to time-resolved data from any ecological systems , such as bioreactors 62 , marine 63 or soil ecosystems 64 ., Our method quantifies growth rates , community interactions and susceptibilities to external perturbations in a single inference ., The modeling approach is based on the generalized Lotka–Volterra model ( eq ., ( 1 ) ) , a system of non-linear ordinary differential equations , whose governing parameters can be stably determined by a regularized regression on the discretized version of the model ( eq ., ( 2 ) ) ., Microbiota metagenomics data often have a high number of microbial species which is much larger than the number of available time points ., This presents a challenge to inference ., We solved this problem in two steps ., The first step was to group the bacterial sequences at the genus level of phylogenic classification and consider only the ten most abundant microbial genera including the pathogen C . difficile and merge all remainders to “Others” ., The second step was to apply Tikhonov regularization , a procedure that provides a unique and stable solution and , in combination with cross-validation , reduces the risk of overfitting noisy data ., Our inference method was tested using in silico data ( Figure S1 ) and evaluated by its ability to recover left-out data using a cross-validation approach ( Figures S2 , S3 ) ., The application of inference methods to temporal metagenomic data shows great promise ., Still , the development of accurate , predictive models , for example for clinical application , will require further developments and the next few years are sure to see major improvements in this area ., For example , the method used here to group microbial sequences may be expanded by adding functional information in addition to taxonomic information ., Future methods will benefit from deeper sequencing of the metagenome 65 to inform new ways to define functional microbial groups ., Such analyses can shed new light , for example , on the mechanisms by which the abundance of certain species seem to correlate with susceptibility to colonization by closely related pathogenic bacteria 66 ., Regarding antibiotic effects , even though we are not yet able to measure the effective concentrations of the antibiotic in the intestine in a high-throughput manner , more accurate information on the pharmacokinetics in vivo will greatly enhance the applicability of this method to clinical settings ., Likewise , experimental advancements with animal models will also be crucial ., The experiments analyzed here consisted of a single dose of clindamycin of by intraperitoneal injection 19 ., Comparing antibiotic perturbed mice with intact mice in this case is similar to comparing a thriving forest with one that has burnt to the ground ., The same antibiotic administered in gradual dosages , or the use of other antibiotics , will surely produce distinct effects and would allow for analyzing the communities with distinct compositions ., Also , engineered artificial microbiota with defined numbers of bacteria in germ-free mice could be a valuable tool to test the resilience of communities with increasing complexity ., Longitudinal data collected from such experimental models can give valuable new insight into the mechanisms of protection against C . difficile ., Other differences between data and simulation results may stem from approximating the infinitesimal by time-discrete dynamics and the fact that the Lotka–Volterra model incorporates only pairwise , second-order interactions ( eq ., ( 1 ) ) ., This could be relaxed in the future by extending the model to third or higher-order interactions once more data becomes available ., Furthermore , due to the requirements of the Lotka–Volterra framework our method cannot be applied directly to read count data without additional information on the total number of bacteria per volume unit ., If this information is not available it needs to be estimated which can be a source of deviations between measured and predicted results ., Nevertheless and even though we cannot claim that the inferred interactions are revealing real causative relationships among microbes , we believe that our results go beyond the explanatory power of widely-used correlations and other methods used ., A major advantage of this method is its foundation on a mechanistic framework ., This allows for the determination of directional interactions as well as the simulation of microbial dynamics with considerable agreement with the actual data ., Based on our inference results , we also hypothesized on a mechanism of C . difficile colonization ., However , making a substantiated statement on this mechanism would require further analysis across different host systems and under various antibiotic perturbations ., Moreover , due to the limited phylogenetic resolution of the 16S rRNA sequencing , our approach would assign the effects of possibly few , interaction-mediating strains to the whole genus ., Nevertheless , the analysis presented here suggests possible experiments focusing on the role of Enterococcus , Coprobacillus , Blautia and Akkermansia in mediating C . difficile colonization ., This could be investigated , for example , in mice with engineered microbial consortia ., Specifically , the microbiota of these mice could be manipulated to lack the genus of Enterococcus or to contain after antibiotic treatment representative strains of genera such as Coprobacillus , Blautia and Akkermansia which are predicted to have protective effect ., Non-colonization and clearance of C . difficile in this system after clindamycin application would then support our hypothesized infection mechanism ., There is an urgent need to understand how the commensal intestinal microbial community resists invasion by pathogenic species ., Mathematical modeling and inference can help shed new light on this problem by disentangling the contribution of each factor at play ., The combination of increasingly accurate and affordable sequencing methods with solidly grounded mathematical theory can help advance our understanding of the relationship between the human host and its microbial inhabitants ., A general approach for a deterministic model of time-dependent ecological dynamics is given by the following system of autonomous coupled first-order ordinary differential equations , in which each time course represents the time-variation in abundance , , of an ecological unit in a certain environment , ( 4 ) with unknown parameters , for ., A requirement of ecological models for closed systems is that a unit that once goes extinct cannot return ., Thus , for unit which is extinct at time , we require and at any time independent of any variation of the remaining , ., In the framework of ( 4 ) , this necessitates , for and for such that , if we restrict to only pairwise interactions , we obtain for each unit , ( 5 ) where and for ., This system of equations is also known as the Lotka–Volterra model 39 ., The denotes the unlimited growth rate of unit in absence of any competition ., The interaction term characterizes the effect of unit on ., In particular , stands for activation and for repression ., ( No interaction is accordingly indicated by ) ., In this form , the model , which is governed by the absolute abundances of units and their physical , order-dependent interactions , also captures non-linear dynamics such as Monod-type/Michaelis–Menten kinetics in a first-order approximation ., In addition to growth and interactions we introduce the effect of the application of external time-dependent stimuli , , on each ecological unit such that the full model writes , ( 6 ) where represents an external , time-variable stimulus of a perturbation whose relative susceptibility for each unit is represented by ., In the framework of metagenomic data , one faces large magnitudes of total numbers of bacteria ., A common approach to identify scale-dependencies of the system and to circumvent numerical problems associated with this is to use non-dimensional variables which allow to treat the model relative to changes on typical system scales 67 ., For this purpose , we introduce the following representation of the dynamical variables , ( 7 ) where the dimensionless forms are denoted with asterisks and the barred variables denote the typical scales of the variables ., For the measurements of the intestinal microbiota used in our analysis , we find typical scales for abundance and time of and ., Equation ( 1 ) then reads in dimensionless form as , ( 8 ) We choose the scale for the perturbation signal such that it is scaled to 1 , i . e . ., Thus , we obtain the rescaled growth rates , interaction parameters and susceptibilities as , , and and recover the original equation ( 1 ) by dropping the asterisks ., Given this choice , the ( rescaled ) parameters of growth and susceptibility are found to be scale-invariant of changes in the typical abundance , in contrast to the interaction parameter ., Input variable is one longitudinal data-set in time points with abundances of taxonomic units ( in the following analysis , genera ) , , and time-dependent perturbations represented by their signal ., The parameters of interest are the growth , interaction and susceptibility parameters , , and ., The operational taxonomic units counts per sample and relative phylogenetic profile as presented in 19 were used as input data for our analysis ., As described in the Results section , we considered the ten most abundant genera ( including the pathogen C . difficile ) and a group “Other” containing the remaining lower abundance genera ., The particular grouping was used to reduce sparsity in the data matrix and to avoid spurious , presumably noise-driven contributions ., The choice of using the genus level for phylogenetic resolution was dictated by the fact that, 1 ) it is consistent with the original published paper 19 and, 2 ) it represents the most specific phylogenetic level for which we have classification data ., In our grouping , we denote a microbial genus “undefined” ( abbreviated with “und . ” ) when the phylogenetic classification was non-ambiguous up to a certain phylogenetic level ., In contrast to Buffie et al . 19 in which the data of the three replicates are presented by their average , we use the individual nine time courses from the cecum ( three from each colony ) and concatenate their compositions spanning 86 time points into the data matrices and ., In case of non-detection of an otherwise present genus , we assign a uniformly distributed random value between zero and the detection limit of the corresponding sample ., Whenever a genus is completely absent from all considered samples in a particular inference , its corresponding row in the data matrix of above is set to zero ., The perturbation signal for clindamycin is modeled by a unit pulse of length day centered on the time of antibiotic administration ., Subsequently , the inference was performed as described above with , i . e . in every round of cross-validation , six of the nine time courses were used as training and the remaining three as test set ., Ten rounds of cross-validation yielded the minimizing regularization parameter ., The result for using all the data of nine time courses is presented in Figure 2 ., In the next step , we predicted the behavior of known trajectories only using their initial compositions and clindamycin application and/or C . difficile inoculation and compared it to the measured values ., We used from above to infer , and on six out of the nine trajectories , two from each population ., These parameters were used

Introduction, Results, Discussion, Materials and Methods

The intestinal microbiota is a microbial ecosystem of crucial importance to human health ., Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections ., We present a novel method to infer microbial community ecology directly from time-resolved metagenomics ., This method extends generalized Lotka–Volterra dynamics to account for external perturbations ., Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions , commensal-pathogen interactions , and the effect of the antibiotic on the community ., Stability analysis reveals that the microbiota is intrinsically stable , explaining how antibiotic perturbations and C . difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations ., Importantly , the analysis suggests a subnetwork of bacterial groups implicated in protection against C . difficile ., Due to its generality , our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli .

Recent advances in DNA sequencing and metagenomics are opening a window into the human microbiome revealing novel associations between certain microbial consortia and disease ., However , most of these studies are cross-sectional and lack a mechanistic understanding of this ecosystems structure and its response to external perturbations , therefore not allowing accurate temporal predictions ., In this article , we develop a method to analyze temporal community data accounting also for time-dependent external perturbations ., In particular , this method combines the classical Lotka–Volterra model of population dynamics with regression techniques to obtain mechanistically descriptive coefficients which can be further used to construct predictive models of ecosystem dynamics ., Using then data from a mouse experiment under antibiotic perturbations , we are able to predict and recover the microbiota temporal dynamics and study the concept of alternative stable states and antibiotic-induced transitions ., As a result , our method reveals a group of commensal microbes that potentially protect against infection by the pathogen Clostridium difficile and proposes a possible mechanism how the antibiotic makes the host more susceptible to infection .

journal.pcbi.1000828

Platos Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

A major goal of systems biology is to elucidate the molecular networks that underlie cellular decision-making and predict emergent properties of the system ., Knowledge of molecular networks provides novel insight into the mechanisms underlying both physiological and pathological cellular processes ., Such networks were constructed in yeast 1 , 2 , Escherichia coli 3–5 , Saccharomyces cerevisiae 6 and human 7 , 8 , mostly using large-scale genetic manipulation in order to identify gene to gene interactions , non-coding RNA interactions , and gene to phenotype interactions ., These networks were analyzed , and the function of several network components was elucidated 3 , 4 , 9–13 ., High-throughput gene expression assays , such as microarrays and quantitative real-time PCR , provide insights into mechanisms mediating normal physiology and disease states ., Gene assays have been used to identify novel genes associated with specific cellular events or phenotypes , and to unravel interaction networks between the genes ., Still , for some of the important questions facing cell biologists , the statistical and mathematical approaches used to analyze these data are not applicable ., Specifically , the activity state of many signaling components mediating the cellular response ( e . g . some scaffold proteins or transcription factors ) cannot be measured in systematic high throughput assays , and therefore the interactions between them are not directly decipherable by these approaches ., Several methods have been developed to reconstruct signaling networks from experimental data 14 ., However , most of these methods rely on measuring the activity levels of the signaling components in question under several conditions , and therefore require a large number of experiments for each signaling component ., This applies for bottom-up approaches that use experimental determination of individual biochemical interactions to reconstruct the network 15 , as well as for many top-down approaches such as partial least squares ( PLS ) 16–18 , modular response analysis ( MRA ) 19–23 , many methods using Bayesian inference 24–28 , and methods based on dynamic properties 29–31 ., The few methods that do not require measuring the activity of the signaling components rely on creating large interaction databases by performing many experiments 32 , or integration of large databases from several sources 33 , 34 ., Although the latter methods can be useful for finalizing well-studied networks , they are not appropriate when little data is available about the network ., Gene profiling has been previously used to find interactions between various molecules 32 , 35 , but the focus has been on late time points , when gene activity reaches a quasi-steady state ., At these time-points the initial signal from the signaling component is partly degraded due to feedback and cross-talk between the genes ., At later time points , the changes in expression of many genes may fail to show a simple function that correlates with the activity of the upstream signaling components responsible for regulating that gene 36 ., Thus the use of gene expression measured more than ∼one hour after modulation of the system can provide a non-mechanistic pattern-matching representation of cellular state ., However such approaches may not allow a quantitative reconstruction of the biochemical network in a way that is analogous to construction of a network using measurement of the protein activity states themselves ., A potential technique to measure indirectly the activity levels of signaling components is presented by measuring the activity of early genes , which are defined as genes that do not require any de-novo synthesis in order to start their transcription ., Specifically , their regulatory transcription factors are pre-formed and the activation states of these factors are altered by modulations in cellular signaling ., As a result , their promoters act as direct , quantitative sensors of the cellular signaling state 36–39 ., Such genes are thus the first genes to be induced following a change in the cells condition , and are usually activated within minutes ., To illustrate the linear function correlating signaling components and early genes measured at early time points , we exposed gonadotrope cells to the hormone GnRH at varying concentration and measured , the resulting levels of one active signaling molecule , phosphoERK , as well as the levels of transcripts for several early genes and non-early genes at 0 . 75 and 5 hours ( Fig . 1 ) ., The results show that all of the early genes are linearly correlated with the levels of phosphoERK and the correlation is much higher , ( R2 ranging from 0 . 92 to 0 . 99 ) when measured at 0 . 75 hours than when measured at 5 hours ., Therefore , if additional experiments were performed , such as ERK inhibition , to determine which of these genes are most dependent on activation of ERK , the measurement of such genes would provide an indirect , yet sensitive and accurate measurement of the levels of phosphoERK ., The relationship is most linear and most direct for early genes measured at early time points ., In contrast , in secondary and tertiary genes , which require newly synthesized transcription factors or enhanceosome components to regulate their activity , their activity levels normally do not have a simple function relating it to the activity levels of upstream signaling components at any time point ., Notably , the linear amplification between signals and early genes is the basis of the widespread use of synthetic gene reporter constructs to provide quantitative measurements which accurately reflect changes in cell signaling ( e . g . using the activity of a cAMP response element reporter to reflect changes in adenylate cyclase activity ) ., Because of these considerations , the utilization of early gene profiling provides an experimentally and computationally tractable approach to reverse engineer the interaction network of signaling components ., Here we present a robust and efficient algorithm named PLACA that uses high throughput assays of early gene expression at early time points combines with perturbation of cellular components in order to uncover experimentally verifiable functional interactions between the components upstream of these early genes ., Notably , in addition to the reverse engineered network , PLACA also identifies the specific genes that manifest the functional interaction ., Thus PLACA facilitates experiments to validate the inferred interactions ., We tested the performance of PLACA by reconstructing a synthetic network , and found that when using several independent experiments it is robust to experimental noise ., Additionally , we studied the early gene responses to signaling component perturbations in the pituitary gonadotrope and used PLACA to reverse engineer the network of this crucial component of the reproductive system ., Many of the inferred functional interaction have been previously observed , and novel functional interaction predictions were then successfully tested experimentally ., A web-interface for PLACA is available at http://tsb . mssm . edu/primeportal/ ?, q=placa_prog ., As stated above , current methods for inferring signaling regulatory networks from gene activity data are not suitable for high throughput experiments in large networks ., This represents a significant bottleneck in translating readily obtainable cellular readouts such as mRNA levels into detailed network interaction maps ., Some of the nodes within the signaling network are comprised of elements such as transcription factors and scaffold proteins for which it is difficult to obtain activity measurements systematically ., Even in the case of kinases , where many active state antibodies exist , other activity states may not be well characterized ., Therefore we have developed a robust and efficient algorithm for the analysis of the interactions between signaling components , based on the activity level of early genes that are downstream of these signaling components ., This algorithm infers the activity of the signaling components indirectly from measurement of the activity of early genes ., By analogy to the allegory in which reality is perceived indirectly via shadows cast on the wall of the cavern we inhabit , we refer to this as “Platos Cave Algorithm” , or PLACA ., PLACA is a multi-step algorithm based on an integration of well-established techniques ., We outline the practical steps needed to use PLACA in order to uncover the functional interactions between signaling components , after choosing a list of n signaling components , and a set of m early genes that are predicted or known to be affected by the signaling components ., Fig . 2 illustrates how these steps can be divided into 3 stages: experimental data acquisition , reverse engineering , and post processing ., These stages are briefly explained here , and described in more detail in the following sub-sections ., The data acquisition stage for PLACA consists of n+1 experiments ., The first experiment measures the mean activity and the standard deviation of the activity of all early genes ., In the following n experiments each signaling component is perturbed in turn , and the activity and standard deviation of the activity of all the early genes are measured ., Next , the reverse engineering stage is performed ., First , a weight matrix describing the connections between genes and signaling components is calculated , and used to obtain an estimate of the change in activity of each signaling components following each perturbation ., The estimated change in activity is used to infer the interactions between the signaling components by applying a reverse engineering method , and we chose to use MRA 19–23 for reasons that are detailed below ., In order to achieve higher statistical significance of the results , the signaling activity estimation and MRA are repeated several times using a data re-sampling technique ., Using the results obtained by re-sampling , only interactions with sufficient statistical significance are retained ., Finally , the post-processing stage is applied if several independent experimental results of similar experiments are available , in which case interactions that do not appear in a sufficient number of the experiments are excluded ., See Supplementary text S1 for technical details on the post processing stage ., The algorithm is applied to the mean activity levels and the standard deviation in activity levels of early genes ., These are performed both under normal conditions , and following perturbation of each signaling component ., Experimentally , these perturbations can be performed using chemical inhibitors ( e . g . kinase inhibitors , protease inhibitors , or channel blockers ) , siRNA , expression of over-active or dominant negative constructs , or over expression of the gene ., The activity levels of the genes can be measured using quantitative real time PCR or microarrays ., The activity level of a gene may be the fold-change of the individual transcript compared to some gene , its concentration , or its copy number/cell ., We use the activity of early genes as an estimate for signaling component activity , and derive a weight matrix ( as explained below ) representing how much the change in activity of each early gene contributes to the estimated change in activity of each signaling component ., Specifically , if the change in gene activity after each perturbation is stored in matrix ΔG , and the weight matrix is denoted by W , then the estimated change in activity of the signaling components following each perturbation is given by ΔS\u200a=\u200aW ( ΔG ) T ., This assumes a linear connection between signaling component activity and gene activity , as was observed experimentally ., Mathematically , this is equivalent to assuming that the change in the activity of an early gene depends linearly on the weighted sum of the changes in the activity of the signaling components ., The linearity assumption can also be justified by considering small changes from maximal ( or quasi steady-state ) values of the early gene activity ., To this point we have outlined a method to estimate the activity levels of signaling components under perturbation of each individual component , as well as under normal conditions , without measuring the signaling components themselves ., Our goal is to use this knowledge to reverse engineer the interaction network between the signaling components ., Any number of reverse engineering methods can be applied at this point , but we give several reasons that make a technique called modular response analysis ( MRA ) 19 , 20 the most suitable one ., To apply MRA requires measuring the change in activity level of a representative of each component in the system , after each component is perturbed in turn ., Therefore , the acquired estimated data set is a suitable input for MRA ., In contrast , methods based on Bayesian learning require sufficient statistics to provide likelihood estimates , thus requiring additional experimental results 24–28 ., Furthermore , such methods require a prior probability distribution that represents our belief or knowledge of the architecture of the network ., Other approaches require integrating a large set of experimental data , either from literature or by manufacturing that data 32–34 , and are therefore only appropriate for well studied systems ., In the context of PLACA , the change in activity of each signaling component after each perturbation is given by Rij\u200a=\u200aSij−S0i ., Given this matrix , MRA provides the interaction strengths between the signaling components , which is given by r\u200a=\u200a−dg ( R−1 ) −1R−1 19 , 20 , where dg ( R ) is a diagonal matrix with a diagonal equal to that of R . rij is the linear approximation of the effect component j has on the steady state value of component i ., In its original context , matrix r holds the interaction coefficients between the signaling components , and represents the reverse engineered network ., The meaning of these interaction coefficients in the context of PLACA is discussed below ., One disadvantage of MRA is that it inherently returns an interaction coefficient between every component in the network ., Normally , this results in a need to set an arbitrary cutoff to determine which interactions to consider and which to ignore ., A difficulty in setting such a cutoff when using PLACA arises from the fact that the activity levels are only estimated , and the contribution to the estimated activity from each gene is multiplied by an unknown constant ., Thus , the significance of the actual coefficient values is uncertain apart for their sign , and a single cutoff cannot be set ., This problem can be solved using data re-sampling ( each time considering data from a subset of early genes ) , keeping only interactions that show the same sign for a significant number of the re-sampled data ., Specifically , we used a jack-knifing technique , in which we ignored each gene sequentially and applied PLACA to the remaining data , obtaining a set of interaction coefficients for every pair of signaling components ., For larger data-set , however , other re-sampling methods such as bootstrapping or random cross-validation may be more appropriate 42 ., Using the mean and standard deviation of each coefficient and assuming a normal distribution , we keep only interactions that have a consistent sign with a 95% confidence level ., We emphasize that since PLACA relies on estimating the activity level of signaling components using downstream genes , the interactions that are deduced using PLACA do not necessarily indicate a biochemical interaction between those signaling components ., Rather , inferred interactions between components indicate that they affect a shared set of genes , meaning that they share a biological function in the cell ., For this reason we refer to these as functional interactions , where the two signaling components affect each others function , although they may not affect each other directly ., Conventional diagrams of interaction networks include nodes and arrows , where the nodes represent signaling components , and the arrows represent direct interaction between the two signaling components ., To avoid confusion , we introduce a new notation , in which functional interactions are indicated by an arrow with a diamond in it ., LβT2 cells obtained from Prof . Pamela Mellon ( University of California , San Diego ) were maintained at 37C/5% CO2 in humidified air in DMEM ( Mediatech , Herndon , VA ) supplemented with 10% fetal bovine serum ( FBS ) ( Gemini , Calabasas , CA ) and L-glutamine ., Cells were grown in 10% charcoal-treated FBS ( CT-FBS ) ( Hyclone Laboratories , Inc . , Logan , UT ) 18 hours before treatment with hormones or growth factor ., GnRH was obtained from Bachem ( Torrence , CA ) ., The chemical inhibitors PD98059 , JNK Inhibitor II ( SP600125 ) , Bisindolylmaleimide I , PP2 , KN62 , and AG1478 were obtained from Calbiochem ., The antibodies used were anti-phospho p42/44 MAPK ( Cell Signaling Technology , Beverly , MA , #9106 ) , anti-p42/44 ERK ( Cell Signaling Technology #9102 ) ., For the western blots cells were lysed in NP-40 buffer ( 20mM Tris-HCl , 1%NP-40 , 150mM NaCl ) and protein measurements were performed with protein assay reagent ( BIO RAD , Hercules , CA ) ., 50µg of extract was separated on 10% Tris-HCl SDS-PAGE gels ( BIO RAD ) , and transferred to PVDF membranes ( Amersham , Buckinghamshire , UK ) ., Blocking was performed for 60 minutes with 5% nonfat dry milk in Tris-buffered saline , 0 . 1% Tween-20 and followed by incubation with the primary antibody at 4°C overnight ., Signal was visualized with goat anti-rabbit or goat anti-mouse IgG-HRP ( Santa Cruz Biotechnology ) using the ECL system ( Amersham ) ., To determine the phosphorylation level of ERK with different concentrations of GnRH stimulation , pERK ELISA ( Cell Signaling Technology #7177 ) and total ERK ELISA kits ( Cell signaling technology #7050 ) were used according to manufacturers instruction ., LβT2 cells were stimulated with GnRH ( 0 , 0 . 1 , 0 . 3 , 1 , 3 , 10 , 100 , 1000 nM ) for 5 minutes , and cells were harvested ., For normalization , cell lysate was divided in half; one used for pERK ELISA and another half used for total ERK ELISA ., The acquired absorbance of pERK at OD450 was divided by that of total ERK providing normalized pERK activity ., Quantitative real time PCR was performed and analyzed as follows ., LβT2 cells were cultured and total RNA was prepared as described in a previous study 43 ., RNA was isolated using Absolutely RNA 96 well Microprep Kit ( Stratagene ) ., Approximately 2µg of the RNA was then reverse transcribed with Stratascript ( Stratagene ) according to manufacturer ., For each reaction 1/800 of the RT reaction volume was utilized for 40 cycle three-step PCR in an ABI Prism 7900 ( Applied Biosystems , Foster City , CA ) in 20mM Tris pH 8 . 4 , 50mM KCl , 5mM MgCl2 , 200µM dNTPs , . 5× SYBR Green I ( Molecular Probes , Eugene , OR ) , 200nM each primer and 0 . 5U Platinum Taq ( Invitrogen ) ., We first tested PLACA by attempting to reverse engineer a functional network using early gene expression and perturbation experiments generated by a simulation using an arbitrary network model ( Fig . 4A ) ., The network was constructed by using well-known network motifs such as feed forward loops , bi-fans , and master regulators 3 , 4 , 6 , and by adding a few genes that are regulated by a single signaling component ., Each signaling component was assigned a characteristic activity ( inhibition or activation ) , but exceptions were allowed and introduced to the model ., A detailed description of the network model , which has four signaling components and 10 early genes , and of the simulation methods appear in the Supporting Text S1 ., The functional interaction network derived using PLACA is shown in Fig . 4B ., Overall , the functional reverse engineered network shows high similarity to the model that produced the early gene expression data ., To explain how the various interactions were identified , consider the heat-map representing the change in gene expression following perturbations of each signaling component ( Fig . 4C ) ., First , let us consider the bi-directional positive interaction between signaling components S1 and S2 ., Perturbations of either S1 and S2 cause similar changes in expression in genes G1 , G2 , G4 , and G6 , which are also genes that are chosen as good estimates of both S1 and S2 expression ., Genes G7 , G8 and G10 also show similar behavior , further implying the bi-directional activation between S1 and S2 that is identified by PLACA ., Next , genes G6 , G7 , G8 , and G9 , which are indicators of S3 activity , change in opposite ways following perturbations to S2 and S3 ., However , among these genes only G6 is a good indicator for S2 , and thus PLACA infers that S2 inhibits S3 , but not the other way around ., Similarly , PLACA infers S1 inhibition of S4 through the genes G3 and G5 ( which are good indicators for S4 but not for S1 ) , and the inhibition of S4 by S2 through the genes G4 and G7 ., The aim of PLACA is to reconstruct the functional interaction network from experimental data , which is often quite noisy ., To test robustness to noise , we applied PLACA to the synthetic network with varying levels of noise and analyzed the similarity between the reconstructed networks ., It was found that when considering only results obtained by a majority of several experiments , PLACA remains robust with noise levels of up to 20% of the mean ( Supporting Fig . S1 ) ., A complete description of the methods used in this analysis is given in Supporting Text S1 ., In this manuscript we introduced an algorithm that uses changes in the level of early gene induction in order to estimate the activity of unmeasured upstream signaling components , and then infer the functional interactions between the signaling components ., The algorithm is useful for translating recent advances in technology that utilize high throughput measurement of gene activity into novel insights of cellular network design and signal processing ., Despite the introduction of methods that allow to obtain high throughput data of the levels of protein activity state ( multiplexed ELISA , DNA binding assays ) , in some cases such measurements may be impractical as in the case of scaffold proteins , some transcription factors , and kinases with an unknown number of active states ., As gene expression assays and RNAi component perturbations are both sequence dependent , they are readily performed for any target and PLACA is suitable for systematic large scale reverse engineering of any signaling network ., The experiments suggested by PLACA are easy to design and feasible to perform ., In addition to the problem of measuring the signaling components themselves , in some biological setups , such as the one discussed here , measuring steady state values is not applicable , since the system becomes desensitized when exposed to a prolonged stimulus ., Conventional reverse engineering techniques that rely on steady state analysis will not be able to reverse engineer such systems ., Early gene analysis can help solve this problem , and PLACA can be applied in these cases to reverse engineer the network ., PLACA provides a mathematically efficient algorithm that scales linearly with the number of genes and polynomially ( O ( n3 ) ) with the number of signaling components ., Large gene expression data-set , however , tend to display data-degeneracy , where multiple genes behave similarly under various experimental conditions ., This problem is likely to become worse when the analysis is limited to early gene expression ., However , as long as the number of sets of similarly behaving genes is larger than the number of perturbed signaling components PLACA will treat the genes in each set as one , and thus still be applicable ., It should be noted that like many reverse engineering methods , the output of PLACA is the network of functional interactions between the signaling components , and not direct biochemical interactions ., Such interactions , however , indicate that both signaling components affect a mutual set of genes , and thus provide a useful level of abstraction that gives an indication to which pathways interact in a non-trivial way ., Further experiments are needed in order to identify the molecular mechanisms underlying the functional interactions ., Still , PLACA provides a list of the genes that are most likely involved in each interaction , further reducing the ambiguity in the meaning of the functional interaction , and suggesting a means to perform follow-up experiments to validate the interaction ., A potential problem may arise from using gene activity levels as linear estimates ., The activity of some early genes was shown to follow a linear response curve for a large range of signaling activity 36 ( see also Fig . 1 ) , and the assumption that many pathways work within the linear range of stimulus response is a classical pharmacological concept ., On the other hand , in cases where linearity was not observed , this assumption is only valid when the changes in activity levels are small ., However , experiments are normally designed to produce statistically significant results , and the changes in activity levels are therefore large ., This is a problem that arises in most reverse engineering method relying on perturbations , and may skew the results ., Another disadvantage of the proposed algorithm involves the inability to compare the inferred network to other possible networks , in a similar way to statistical learning algorithms ., However , as mentioned in the methods section , after obtaining the estimated activity of the signaling components it is possible to apply a different reverse engineering algorithm such as a Bayesian learning algorithm ., Such a methodology will require further experiments , but will also reveal more information about the regulatory network ., The experimental results shown here were shown as an example of the ease of use of PLACA , and its applicability to experimental data ., PLACA uncovered much of the known interaction network of the subsystem that was tested , and uncovered several novel interactions ., These interactions must be further explored in order to understand the biochemical interactions underlying them , and in order to understand their biological significance ., PLACA offers a method to exploit the growing amounts of data that are produced by high-throughput experiments ., At the same time PLACA also offers a new level of abstraction that is manifested by functional interactions ., This level of abstraction can be extremely useful in the experimental , pharmacological , and theoretical levels ., It can extend our understanding of emergent phenomena in regulatory networks , and offer new insights into the effects of drugs , hormones and pathogens on cells .

Introduction, Methods, Results, Discussion

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology ., Lesions in signaling networks lead to alterations in gene expression , which in principle should allow network reconstruction ., However , the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology ., Two observations provide the basis for overcoming this limitation:, a . genes induced without de-novo protein synthesis ( early genes ) show a linear accumulation of product in the first hour after the change in the cells state;, b . The signaling components in the network largely function in the linear range of their stimulus-response curves ., Therefore , unlike most genes or most time points , expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components ., Such expression data provide the basis for an efficient algorithm ( Platos Cave algorithm; PLACA ) to reverse engineer functional signaling networks ., Unlike conventional reverse engineering algorithms that use steady state values , PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components , without measuring the signaling components themselves ., Besides the reverse engineered network , PLACA also identifies the genes detecting the functional interaction , thereby facilitating validation of the predicted functional network ., Using simulated datasets , the algorithm is shown to be robust to experimental noise ., Using experimental data obtained from gonadotropes , PLACA reverse engineered the interaction network of six perturbed signaling components ., The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment ., PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships .

Elucidating the biochemical interactions in living cells is essential to understanding their behavior under various external conditions ., Some of these interactions occur between signaling components with many active states , and their activity levels may be difficult to measure directly ., However , most methods to reverse engineer interaction networks rely on measuring gene activity at steady state under various cellular stimuli ., Such gene measurements therefore ignore the intermediate effects of signaling components , and cannot reliably convey the interactions between the signaling components themselves ., We propose using the changes in activity of early genes shortly after the stimulus to infer the functional interactions between the unmeasured signaling components ., The change in expression in such genes at these times is directly and linearly affected by the signaling components , since there is insufficient time for other genes to be transcribed and interfere with the early genes expression ., We present an algorithm that uses such measurements to reverse engineer the functional interaction network between signaling components , and also provides a means for testing these predictions ., The algorithm therefore uses feasible experiments to reconstruct functional networks ., We applied the algorithm to experimental measurements and uncovered known interactions , as well as novel interactions that were then confirmed experimentally .

computational biology/signaling networks

journal.pgen.1003797

Fusion of Large-Scale Genomic Knowledge and Frequency Data Computationally Prioritizes Variants in Epilepsy

Interpretation of high-resolution array comparative genomic hybridization ( aCGH ) data is made challenging by the large number of copy number variation ( CNV ) events identified in each individual ., Analogous problems arise in interpretation of deep sequencing data where the number of variants rapidly outstrips the capacity for manual curation ., Moreover , because of the recent expansion of human populations , most variation in an individual genome is rare and restricted among family lineages , making distinction between rare and pathogenic variation difficult 1 ., Given the scale of variation and the challenge of profile interpretation , a number of groups have developed and utilized computational and machine learning tools to prioritize genetic data 2 , 3 ., Huang and colleagues analyzed the characteristics of a group of genes and their protein products known to cause phenotypes in the haploinsufficient state and compared them to those that were repeatedly deleted in a control population of apparently healthy individuals ( i . e . those haplosufficient ) 4 ., The differences between these groups of genes were used to develop a general quantitative model to predict whether a gene deletion is likely to be deleterious ., While there is broad applicability to such a prioritization scheme , it provides little guidance to help a clinician determine whether a given deletion has a role in a specific phenotype in an individual patient ., Other studies assigned genes to networks based upon particular disease phenotypes , and while useful for directing further studies , these approaches did not attempt to quantify the likelihood of a genes appropriate assignment to a given disease trait or its propensity for actually contributing to disease 5–7 ., Other researchers have developed a computational model that takes into account genomic structure and functional elements to predict whether a given CNV is associated with intellectual disability ( ID ) 8 ., This algorithm represents an excellent tool , however , it does not specifically predict or rank which gene ( s ) within the CNV are most dosage-sensitive or likely to be relevant to the phenotype ., Such specific predictions are necessary to inform clinical interpretation and to aid the development of disease-centered diagnostics ., Moreover , none of these tools use comparative variant frequency information between affected phenotypes in a large database to inform the scoring and prioritization schemes ., Epilepsy is a common neurological disorder for which improved computational tools could be extremely beneficial ., With over 50 million individuals affected , the prevalence of epilepsy ranges from 0 . 2 to 2% depending on the population studied 9 ., In the United States , the overall prevalence is approximately 0 . 5% , with a disproportionate number of cases in infants , children , and the elderly 10 ., The epilepsies are currently grouped into genetic , structural/metabolic , or unknown etiologies 11 ., To date , only a fraction of patients with suspected genetic forms of epilepsy have an etiological diagnosis , meaning accurate recurrence risk , prognosis , and disease-specific surveillance and treatment information are rarely available ., The lack of specific diagnoses is at least partly due to the complex inheritance , variable expressivity , and incomplete penetrance of many forms of epilepsy; although some examples of Mendelian segregation are recognized 12 ., The role of CNVs in common neurological diseases has become increasingly clear , and there are well-studied CNVs that cause isolated or syndromic disorders including ID 13 , autism spectrum disorders ( ASDs ) 14 , 15 , and schizophrenia 16–18 ., Although each CNV itself is rare among individuals with a given disease , when considered as a group , structural variation of the genome is a common cause of such phenotypes ., A number of well-described syndromic disorders with epilepsy are caused by CNVs , including chromosome 1p36 deletion syndrome , Angelman syndrome , and MECP2 duplication syndrome ., A number of studies testing large cohorts of individuals have demonstrated that various CNVs are associated with a wide range of epilepsy phenotypes including non-syndromic idiopathic epilepsy 19–22 ., We hypothesized that information about individual genes gleaned from large-scale knowledge sources could be integrated into an epilepsy-specific pathogenicity score ., We further hypothesized that these scores could be combined with frequency information of gene disruption among individuals with epilepsy to prioritize candidate genes and interpret variants identified in personal genomes ., We used a fixed set of training genes previously published as variant in Mendelian epilepsies to determine training patterns for epilepsy genes in these high dimensional data types and subsequently developed a score matching the training set for each available gene in each knowledge source ., To utilize variant frequency information together with our pathogenicity scores , we took advantage of a Bayesian approach in which the gene pathogenicity scores were used to develop informative prior probabilities for the expected increase in the frequency of variants in an epilepsy population as compared to non-neurologic controls ., This statistical analysis determined Bayes factors as further scores we used to rank and prioritize genes ., We then applied these gene-level scores to characterize CNVs harbored by individuals in a well-defined cohort of subjects with epilepsy identified by electronic medical record ( EMR ) review , and used this analysis of CNVs to assess our pathogenicity score ., We also evaluated the Bayes factors comparing the results of our epilepsy cohort to a matched cohort with non-neurologic indications , and used this method to explore a possible role of multiple genes disrupted within the genome of a single individual with epilepsy ., Finally , we examined the possible utility of our scheme as a clinical decision support tool for patients undergoing genome-wide testing ., Genes involved in the same disease are often similarly annotated in knowledge databases , are expressed in similar tissues or have gene products that physically interact 2 ., We hypothesized that genes involved in epilepsy would show such characteristics ., Indeed , analysis of a set of 83 genes with known epilepsy associations reveals that epilepsy genes form highly connected networks in multiple datasets ( Figure 1 ) ., Thus , we concluded that it would be reasonable to interrogate these knowledge sources to identify as-of-yet unknown genes associated with the phenotype by correlating the features of the training genes with those of all other RefSeq genes ., To improve our prioritization , we sought to include information about how often a given gene was mutated among individuals with epilepsy compared with a background population ( Figure 2 ) ., These complementary strategies and their integration are described below ., We hypothesized that a bioinformatic approach could consolidate information from multiple biological fields into an integrated score of pathogenicity on a genome-wide scale ., To this end , we validated six “features” ( see methods ) using biological information from large-scale knowledge sources and comparing known epilepsy genes , including 20 recognized as causative by the International League Against Epilepsy 23 ( Table S1 ) , to all annotated RefSeq genes ., We considered Gene Ontology ( GO ) and Mouse Genome Informatics ( MGI ) phenotype annotation , protein-protein interaction ( PPI ) data , human tissue expression patterns , micro RNA ( miRNA ) targeting , and the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) pathway data to develop an omnibus epilepsy pathogenicity score to predict whether loss of function of a given gene might be relevant to the phenotype ., To determine the efficacy of our scoring mechanism in an unbiased way , we cross-validated our approach ., We removed each of the training genes and recalculated genome-wide pathogenicity scores based on the remaining training genes; we then evaluated the procedure on the gene excluded from training ., The composite result is presented in Figure S1 ., The cross-validation demonstrates that individual feature scores as well as the composite mean pathogenicity score detect known epilepsy genes more efficiently than random chance ., The PPI score is the most efficient feature , with an area under the curve ( AUC ) of 0 . 84 ., The AUC of the composite score is 0 . 86 ., The individual feature scores and composite mean pathogenicity scores of some well-known genes are presented in Table 1 ., Table S2 provides scores for all RefSeq genes ., Having concluded that our pathogenicity score is capable of differentiating known epilepsy genes from other genes throughout the genome , we hypothesized that pathogenicity scores among genes deleted in individuals would correlate with an epilepsy phenotype ., We theorized that patients with non-neurologic phenotypes would be less likely to harbor CNVs containing genes with high pathogenicity scores , while patients with epilepsy would have CNVs harboring genes with higher scores ., To increase the likelihood of identifying an effect , we investigated the CNVs of a well-described “analysis cohort” of subjects with epilepsy identified from unbiased independent review of EMRs ( see methods ) ., We compared their CNVs to those of a matching analysis cohort of subjects referred to our diagnostic center for non-neurologic indications ., We computed the maximum pathogenicity score among genes varying in copy number for each subject , filtering erroneous calls ( see methods ) ., Considering all CNVs – both gains and losses – across the two analysis cohorts , the total pathogenicity burden is not significantly different between subjects with epilepsy and subjects without neurological disease ( data not shown ) ., However , considering only genes harbored within genomic deletions reveals that the maximum scoring gene of deletion CNVs is significantly higher ( Wilcox Signed Rank Test , p<5 . 2×10−4 ) in patients with epilepsy ( Figure 3A ) ., The maximum scoring genes of genomic duplications are not significantly different between the two patient groups ( Figure 3B ) ., To exclude the possibility that the variation observed in patient-wide pathogenicity scores was due to known epilepsy genes , we elected to remove the training epilepsy genes ( Table S2 ) from our calculations ., In support of our findings , the maximum score of genes disrupted among patients with epilepsy remains statistically greater than those with non-neurologic indications ( Wilcox Signed Rank Test , p<0 . 011 , Figure 4A ) ., Having evidence that our pathogenicity score is correlated with epilepsy phenotype at the patient level , we sought to further improve our gene-based prioritization by including gene deletion frequency among a large cohort ., This step is important because the pathogenicity score is uninformed about mutation or variant frequency of genes ., To accomplish this integration , we took advantage of a Bayesian approach coupled with CNV data collected among 23 , 578 individuals referred to our diagnostic center for genome wide CNV testing because of a variety of phenotypes ., We computationally identified individuals with indications for diagnostic test consistent with epilepsy ( n\u200a=\u200a1616 ) and those consistent with disease , but of non-neurologic etiology ( n\u200a=\u200a2940 , see methods ) ., This resulted in two matched “frequency cohorts” of individuals distinct from our well-phenotyped “analysis cohorts . ”, We determined the observed deletion frequency for each gene as described above ., We then parameterized a family of gamma prior distributions that modeled the baseline deletion frequency for each gene by setting the mean of the distribution equal to the observed deletion rate in the non-neurologic frequency cohort ., Figure 4 demonstrates this processes for KCTD15 , with the baseline distribution shown in grey ., We then allowed the prior mean of the gamma distribution of each gene to increase based on its epilepsy-specific pathogenicity score , while the variance was constrained to be a constant multiple of the mean ., This approach resulted in a second family of prior rate distributions informed not only by gene knowledge but also background deletion frequency ( Figure 4 , pink distribution ) ., Next , we computed the total probability of the observed rate of deletions for each gene among the 1 , 616 subjects in the epilepsy frequency cohort ( Figure 4 , red line ) under the background model ( grey distribution ) and the pathogenicity informed prior ( pink distribution ) ., We calculated the ratio of these probabilities , called the Bayes factor , for each gene to allow us to further prioritize genes associated with the epilepsy phenotype ., Table 2 lists the 10 RefSeq genes with the highest Bayes factors , presenting only the maximum scoring gene for recurrent deletions with multiple high scoring genes ., Table 3 lists 10 additional genes with high Bayes factors but without known associations with epilepsy ., Finally , we computed posterior rate distributions for each gene , taking into account gene knowledge from the pathogenicity score , background deletion frequency from the non-neurologic cohort as well as the observed rate in the epilepsy frequency cohort ( Figure 4 , blue distribution ) ., Table S2 lists the frequencies , Bayes factors and posterior rate distribution parameters for each RefSeq gene ., Having designed Bayes factors to more robustly prioritize candidate epilepsy genes , we revisited our analysis cohort of subjects with epilepsy that we previously analyzed with the pathogenicity score alone ., Using a method analogous to our previous analysis performed at the level of subjects , we calculated the maximum Bayes factor among genes deleted in each individual ., Because the deletion of a gene in a given subject with epilepsy necessarily influences the Bayes factor of that gene , we used a cross-validation approach , and recalculated the genome wide Bayes factors for each subject leaving out their contribution to the frequency data ., We discovered that the cross-validated maximum Bayes factors were significantly higher among subjects with epilepsy than those referred for non-neurologic indications ( Wilcox signed rank test p<1 . 1×10−6 , p<9 . 4×10−6 without training genes Figure 5A ) ., Given that many of our features preferentially identify genes that are more highly expressed in the brain ( not the least of which being the gene expression feature , data not shown ) , we were concerned that our Bayes factors might be identifying genes associated with neurologic phenotypes rather than epilepsy in particular ., To examine this , we generated a cohort of individuals referred for ASDs and not epilepsy who also had abnormal aCGH studies ., In keeping with the Bayes factor score as specific for epilepsy , the maximum Bayes factor is higher among subjects with epilepsy than those referred for ASDs ( Wilcox signed rank test p<6 . 5×10−5 , p<2 . 1×10−9 without training genes Figure 5A ) ., We were also interested to explore whether the Bayes factors of more than one gene deleted in each individual subject might be correlated with phenotype , thus suggesting a digenic or oligogenic effect ., To this end , we performed the same cross-validation calculation , but excluded the contribution of the genes with the single highest Bayes factor from each patient ., Notably , the genes with the second highest Bayes factors are significantly higher among subjects with epilepsy compared with individuals referred for non-neurologic indications ( Wilcox signed rank test , p<2 . 1×10−5 , p<5 . 1×10−5 without training genes , Figure 5B ) ., Such a comparison considering the third highest scoring gene and excluding the two highest scoring genes also results in a significant difference ( Wilcox signed rank test , p<5 . 4×10−6 , p<1 . 8×10−6 without training genes , Figure 5B ) ., As additional assessment of the utility of our scoring method , we calculated the scores of genes published as potentially related to epilepsy by Lemke et al 24 ., Because the training genes are by definition higher scoring , we elected to exclude them from this analysis ., Genes in the published list but not included in the epilepsy training genes ( n\u200a=\u200a263 ) have significantly higher pathogenicity scores than the genome wide average ( Wilcox signed rank test , p<0 . 014 ) ., The same genes also have significantly higher Bayes factors ( Wilcox signed rank test , p<7 . 6×10−12 ) ., Another potential use of our Bayes factor scoring metric is in the identification of candidate epilepsy genes at regions with known associations but no known causative gene 25 ., As a proof of principle , we analyzed the 34 RefSeq genes harbored in the recurrent , low-copy repeat mediated 16p11 . 2 deletion ., No training gene was identified from this region because no definitive association has been made between a gene and epilepsy , although approximately 24% of patients with 16p11 . 2 deletions experience seizures 26 ., If we use frequency data among subjects with epilepsy and those referred for non-neurologic indications alone , little information can be gained because of the recurrent nature of the deletion ( Figure 6 ) ., In fact , because of recent advances in oligonucleotide aCGH probe design , over time additional probes have been placed in regions closer to the flanking LCRs that mediate the CNV formation ., Because of this technical artifact , more subjects with epilepsy were calculated to have deletions of SLC7A5P1 than those subjects with non-neurologic indications ., However , if we instead use Bayes factors , taking into account both frequency and gene knowledge , the highest scoring gene is identified as KCTD13 ( Figure 6 ) ., Dosage of this gene has recently been shown to correlate reciprocally with the phenotype of head size in a Zebrafish model , a hallmark of the 16p11 . 2 deletion and duplication syndromes 27 ., The same authors report a patient with a complex rearrangement of KCTD13 with many of the features of 16p11 . 2 deletion syndrome ., Nonetheless , given the high scores of both DOC2A and TAOK2 , it is not unreasonable to hypothesize that one or more other genes in the region might also contribute to the epilepsy seen in these subjects ., Figure S2 presents similar analyses of other loci with recurrent deletions associated with epilepsy ., Given that our data are derived from diagnostic testing , we were interested to explore our composite Bayes factor result as a possible clinical decision support tool to aid in discrimination of an epilepsy phenotype ., Figure 7 shows the sensitivity and specificity of the maximum Bayes factor among deleted genes in an individual subject when used as a binary decision rule to discriminate between epilepsy and non-neurologic phenotypes across a range of Bayes factor cut-off values ., These parameters are relevant for subjects with abnormal aCGH tests and no neurologic indications other than epilepsy ., As an example , having a deleted gene with a Bayes factor of greater than 1 discriminates with a sensitivity and specificity of 0 . 62 and 0 . 60 , respectively ., These statistics are highly influenced by recurrent deletions at the Velocardiofacial locus with a maximum Bayes factor of 2 . 87 ., Choosing a cut off of 2 . 88 ( thus excluding the effects of the Velocardiofacial region ) results in a sensitivity and specificity of 0 . 37 and 0 . 84 , respectively ., Given the imperfection of our indication and coding data , such a cut off rule would suggest 16% of patients with non-neurological indications should receive increased suspicion based on their CNV data ., We also attempted to construct a decision rule based upon the contribution of multiple genes deleted in a given patient , but concluded the single highest scoring gene produced the best results ( data not shown ) ., Rapid expansion of the human population 28 together with relaxed negative selective pressures secondary to increased food supplies and improved medical care 1 as well as the possible influence of higher mutation rates 29 have skewed much of the allele frequency spectrum of human genomic variation toward rare or private variants ., Purifying selection is expected to eliminate highly deleterious alleles from a population over time 30 , yet it is precisely the new and rare variations that contribute to human disease ., We should expect that novel rare and private variants will continuously be discovered , and there are a nearly infinite number of possible variants and combinations of variants that can occur ., Thus , a fundamental shift in the approach to variant interpretation must occur from simple cataloging of variants at a locus to prediction of the possible effects of highly rare or newly identified variants by integrating the state of knowledge about genes and disease processes ., We contend that effective diagnostics must ultimately incorporate some aspects of discovery , inferring the relevance of new and arcane genomic variants for patient phenotypes by leveraging known information and multiple sources of evidence ., In essence , our approach seeks to automate aspects of expert interpretation processes that are currently undertaken by clinical molecular geneticists and diagnostic laboratories on a daily basis ., These professionals consider what is known about mutated genes—for example whether they are expressed in effected tissues or if their protein products are involved in applicable pathways ., They then consider the frequency of mutations both among normal individuals and patients with similar and related phenotypes ., Together with other information and years of experience , the geneticist combines these data into an assessment of variant relevance ., Although our computational method cannot be as effective as an experienced human at interpretation of an individual variant in a single patient , it does have the advantage of scalability to many variants and to large cohorts of individuals with different phenotypes ., Moreover , this approach and others like it can help to facilitate the interpretation of an expert by providing additional triage of large-scale variant data ., Our method comprises two integrated steps: phenotype specific pathogenicity scoring and Bayesian analysis using frequency data ., The pathogenicity scoring approach provides a quantitative method to evaluate genes with respect to a fixed phenotype using known phenotype specific disease genes as a target , leveraging many sources of knowledge ., However , since the model depends highly on the “epilepsy genes” ( Table S1 ) , the choice of the training genes themselves inherently introduces the bias of past knowledge ., Moreover , the training genes were not otherwise sub-structured to consider their distinct functions or roles in epilepsies with diverse etiology; this simplification was mirrored in the EMR review , where we made binary decisions about the appropriateness of the epilepsy assignment without consideration of natural history data that might otherwise inform or refine the interpretation of genetic data ., Our simplified initial approach might be improved by future methods better informed by sub-classifying the training genes and refined consideration of the phenotype data ., Another facet of past knowledge bias is that the computation relies on available gene data from the literature and public databases ., Thus , the pathogenicity score is only as effective as the a priori knowledge for each individual gene ., If little or no information is known about a gene , or worse yet if a gene is not annotated in the RefSeq , the algorithm cannot accurately calculate a score ., To overcome this limitation we attempted to incorporate less biased information such as gene expression data and other types of genome wide scores , such as miRNA target prediction ., In cases where genes were missing features–such as lack of MGI phenotype data—we attempted to impute missing values using linear regression and other methods ., Ultimately , we concluded that restricting analysis to the available reported data provided better results than statistical imputation ( data not shown ) ., More work in this area is warranted ., Likewise , the knowledge sources we utilized are themselves imperfect ., In ontological systems , the failure to annotate a gene to a category can represent an unobserved value in the annotation system , such as a phenotypic assay that was not performed , and not evidence of a negative annotation ., This property is often summed up as: “the absence of evidence is not evidence of absence . ”, While this issue is an important limitation that requires further study , we believe data will improve over time , making ontological systems progressively more informative as annotations become more comprehensive genome-wide ., A key advantage of our method is incorporation of observed variant frequency data from over 20 , 000 genome-wide assays performed by high-resolution aCGH at out diagnostic lab in addition to the computational gene scoring approach ., The epilepsy cohorts and comparator non-neurologic cohorts were comprised of phenotypically affected individuals with segmental findings ., Our approach was to model the differential frequency of CNVs affecting each gene between these two groups using our pathogenicity score to inform the rate distribution ., Subsequently , we are able to use the machinery of Bayesian model comparison to compute those genes where the epilepsy scoring improved the fit from what would be expected without this phenotype-based knowledge ., The Bayes factor summaries allowed us to rank individual genes using the computational score , but the real frequency data—which are driven by molecular mutation events in actual human populations—necessarily incorporate structural and genomic feature information that are not part of the pathogenicity score ., By exploiting variant frequency in actual subjects , our approach utilizes this genomic information without requiring us to explicitly model or otherwise include the complex biological processes underlying mutation ., Using this approach at the genome and cohort-wide level , our analysis was able to highlight a number of potentially novel genes as relevant to epilepsy ., The highest scoring candidate gene identified by our method is ERBB4 , encoding a member of the ErbB subfamily of tyrosine kinases that functions as a neuregulin receptor 31 ., Rare variants of ERBB4 have been associated with increased risk for schizophrenia 32; an intronic deletion between exons 7 and 8 was also identified in a patient with an ASD 33; and a patient with a de novo reciprocal translocation t ( 2;6 ) ( q34;p25 . 3 ) , apparently disrupting the ERBB4 gene , was identified with early myoclonic encephalopathy 34 ., Recent experiments also showed that Erbb4−/− mice exhibit increased susceptibility to chemically induced seizures 35 ., This evidence taken together with our analysis suggests that mutations of ERBB4 may be associated with a number of epileptic phenotypes ., Given the sizable mutation frequency difference of ERBB4 between the epilepsy and non-neurologic cohorts , identification of the gene would have likely been possible using frequency information alone ., However , our method is also able to call attention to genes that , although rarely mutated , are highly similar to the training genes ., A number of the genes listed in Table 3 exemplify this principle ., For example , although GRM5 , encoding the metabatropic glutamate receptor 5 , was identified as deleted in only one subject , its Bayes factor is in the 98th percentile among genes deleted at least once in any cohort ., This gene is interesting because Grm5−/− mice have increased susceptibility to pharmacologically induced seizures and the human protein product is highly connected to the epilepsy training genes ., Such information could easily be overlooked given the subjects ( 3 . 6 Mb ) deletion also includes 17 other RefSeq genes ., Prioritization of rarely mutated genes that are novel to the epilepsy cohort is an important aspect of our approach ., Notably the exclusion of an individuals mutations from the frequency data for the Bayes factor calculation prevents contribution of such genes to our analysis cohort assessment , lowering our statistical power ., Given the observation of rare variation in human disease , it is likely that some of these variants contribute to patients seizures ., Additional studies will be required to validate the associations of these genes with epilepsy ., In addition to the prioritization of individual genes , our method also naturally lends itself to calculation of multi-variant genetic load , and we were able to evaluate evidence for digenic and oligogenic effects in our analysis cohort ., We saw that not only was the maximum Bayes factor among deleted genes significantly higher in the epilepsy cohort versus non-neurologic comparators , but we also observed significant differences in the second and third highest scoring genes ., Previous analysis of a large set of CNV data has suggested that copy number changes of multiple genes at distant loci but in similar networks may compound at the molecular level to contribute to phenotypic variation seen with well-known recurrent genomic disorders 36 ., This previous study relied on relatively more common recurrent CNVs together with second site mutations ., In contrast , our method collapses different deletion alleles at the gene level and then more globally for the phenotype by scoring variants ., This allows us to identify differences at the cohort level generally rather than considering individual pairs of variants for which there is very little statistical power given their rarity ., Our data suggest that , at least in some patients , the deleterious effects of mutations in two or more genes involved in similar processes may interact on the molecular , cellular , or organism level to results in seizures ., In other patients , genomic structural abnormalities may have little influence ., Previous studies of sequence variant load in epilepsy failed to identify differences between subjects with epilepsy and control individuals 37; however , this analysis focused entirely on channel genes whereas our method was intentionally designed to be broad and to include many gene families known to be involved in epilepsy ., Because of the strong correlation of Bayes factors with epilepsy phenotype , we investigated sensitivity and specificity of our score as a clinical decision support tool as a natural extension of our integrated analysis ., Our approach was to discriminate individuals with epilepsy from those with non-neurologic indications based on their maximum Bayes factors ., The difficulty experienced by physicians making use of genome wide tests represents a major limitation in clinical practice 38 ., However , if genome wide results can be condensed into a quantitative score and studied epidemiologically , as are other quantitative test results ( i . e . serum Troponin-I ) , the results may be made more accessible for physicians to interpret and guide management ., For example , if a patient had a high epilepsy-specific Bayes factor but was not known to experience seizures , it might be reasonable to modify patient care ., Such information could alert the clinician to provide counseling about seizure signs , symptoms , and first aid ., In some instances , the prescription of an emergency abortive medication might be indicated ., Neurologists are frequently asked to discern whether a patients spells are epileptic or non-epileptic in nature; when the pre-test probability for seizures is high due to known genetic risk factors , then clinical decisions such as ordering an electroencephalogram ( EEG ) or longer-term video EEG monitoring or making the decision to start a medication to treat epilepsy could be impacted ., While the Bayes factor score certainly does not capture t

Introduction, Results, Discussion, Materials and Methods

Curation and interpretation of copy number variants identified by genome-wide testing is challenged by the large number of events harbored in each personal genome ., Conventional determination of phenotypic relevance relies on patterns of higher frequency in affected individuals versus controls; however , an increasing amount of ascertained variation is rare or private to clans ., Consequently , frequency data have less utility to resolve pathogenic from benign ., One solution is disease-specific algorithms that leverage gene knowledge together with variant frequency to aid prioritization ., We used large-scale resources including Gene Ontology , protein-protein interactions and other annotation systems together with a broad set of 83 genes with known associations to epilepsy to construct a pathogenicity score for the phenotype ., We evaluated the score for all annotated human genes and applied Bayesian methods to combine the derived pathogenicity score with frequency information from our diagnostic laboratory ., Analysis determined Bayes factors and posterior distributions for each gene ., We applied our method to subjects with abnormal chromosomal microarray results and confirmed epilepsy diagnoses gathered by electronic medical record review ., Genes deleted in our subjects with epilepsy had significantly higher pathogenicity scores and Bayes factors compared to subjects referred for non-neurologic indications ., We also applied our scores to identify a recently validated epilepsy gene in a complex genomic region and to reveal candidate genes for epilepsy ., We propose a potential use in clinical decision support for our results in the context of genome-wide screening ., Our approach demonstrates the utility of integrative data in medical genomics .

Improvements in sequencing and microarray technologies have increased the resolution and scope of genetic testing ., As a result , millions of variations are identified in each personal genome of unrelated individuals ., In the context of testing for genetic diseases , identifying the variant or variants contributing to illness among such a large number of candidates is difficult ., Conventional studies to identify causative variants have relied on patterns of higher frequency in affected patients compared with individuals that are well ., However , it is often the rarest variations that cause human disease , making frequency information alone less useful ., Many groups have turned to computational analysis to aid in interpretation of genetic variants ., Epilepsy is a disease where such tools would be useful , as only a fraction of patients with suspected genetic epilepsy have a specific genetic diagnosis ., To help improve variant interpretation in epilepsy , we used computational analysis to combine knowledge about genes from large cloud information sources with mutation frequency from our diagnostic laboratory to score all genes as to how likely they are to be associated with epilepsy ., We use these scores to identify possible candidate genes in epilepsy , and explore other downstream applications .

journal.pgen.1005056

A Systems-Level Interrogation Identifies Regulators of Drosophila Blood Cell Number and Survival

The regulation of cell number varies greatly and typically depends on developmental and environmental stimuli that determine the intracellular balance of pro- and anti-death , and proliferative signals 1–3 ., Proto-oncogenes and tumor suppressors play roles as regulators of cell number and the pathological extension of cell survival is a major hallmark of tumorigenesis 4 ., Accordingly , understanding the complex signaling networks that regulate cell survival is an important yet incompletely accomplished goal 4 , 5 , which can be facilitated by studying a simple model organism ., Blood cells in the fruitfly Drosophila melanogaster have been instrumental in the discovery of fundamental concepts in immunity , hematopoiesis and wound healing 6–11 , but they are also a convenient model to study mechanisms that regulate cell number ., In particular , the Drosophila PDGF/VEGF Receptor ( Pvr ) , a member of the Receptor Tyrosine Kinase ( RTK ) family , controls anti-apoptotic survival signaling in Drosophila blood cells ( hemocytes ) in vivo and in the embryonic cell line Kc in culture 12 ., In other instances , Pvr has been reported to regulate cell proliferation 13 , 14 , differentiation 15 , 16 , cell size 17 , 18 , cytoskeletal architecture 19 and cell migration 20–22 ., Drosophila Pvr therefore parallels roles of the vertebrate family of PDGF/VEGF Receptors in development and disease 12 , 21 , 23–26 ., Here , we took advantage of the role of Pvr in embryonic blood cell survival and performed a systematic RNAi screen to identify regulators of cell number , using the Drosophila cell line Kc under sensitized conditions of Pvr knockdown ., The screen identified enhancers and suppressors of the Pvr RNAi phenotype , many of which were not found in conventional RNAi screens examining cell growth and viability ., In particular , we found that knockdown of InR enhanced the Pvr RNAi phenotype while knockdown of the Ecdysone receptor ( EcR ) 27 and its co-receptor ultraspiracle ( usp ) 28 suppressed the Pvr RNAi phenotype ., We confirmed functional roles for these genes related to Pvr both in cell culture and in vivo ., Phosphoproteomic analyses revealed major differences in the signaling signature of Pvr deficient cells rescued by activation of InR as compared to inactivation of EcR ., Further , our analysis identified distinct sets of phosphorylation targets , common to both Pvr and InR , and unique to each receptor ., Most importantly , we provide precedence that the selection of phosphorylation targets by signaling receptors can depend on the signaling status of the cell , which may have wide-reaching implications for cell regulatory systems in animal development , disease , and the experimental and therapeutic manipulation of signaling pathways ., Previously , we demonstrated that the Drosophila PDGF/VEGF Receptor , Pvr , is essential for anti-apoptotic survival in embryonic hemocytes and in the related cell line Kc , which maintains autocrine Pvr signaling 12 , 29 ., Taking advantage of these systems , we sought to examine the signaling networks that mediate anti-apoptotic survival and regulate cell number ., First , we confirmed that RNAi-mediated knockdown of Pvr induces apoptotic cell death in Kc cells ., RNAi silencing of the Drosophila inhibitor of apoptosis DIAP1 , or thread ( th ) , served as positive control ( Fig . 1A ) ., Expression of the baculovirus inhibitor of apoptosis p35 30 rescued hemocyte survival , leading us to establish a selected pool of Kcp35 cells ( Kcp35 cells , Fig . 1A ) ., Immunoblotting confirmed that Pvr knockdown was equally efficient in Kc and Kcp35 cells ( S1 Fig ) ., Closer examination by incorporation of the thymidine nucleoside analog EdU ( 5-ethynyl-2’deoxyuridine ) in Kc versus Kcp35 cells revealed that Pvr also moderately contributes toward cell proliferation in this system ( Fig . 1B ) , an effect that could not be distinguished in a previous study employing cell cycle profiling 12 ., Reduction in proliferation was also suggested by immunoblotting , where lysates of equal numbers of cells showed a decrease in the proliferation marker phospho-histone H3 ( pHH3 ) in Pvr knockdown samples ( S1 Fig ) ., Using Kc cells , we queried signaling pathways that might be involved in Pvr-dependent cell survival and proliferation ., Examining activity of the Akt/TOR and Mek/Erk pathways by using antibodies to phosphorylated forms of S6Kinase ( S6K , an Akt pathway target ) , Mek and Erk , we found that both pathways are active in Kc cells ., Pvr RNAi led to a significant reduction in the phosphorylation levels of these proteins , indicating that Pvr is a major activator of these pathways in Kc cells ., Single signaling mediator knockdowns of Akt , the TOR-associated Raptor , S6K , Mek and Erk served as controls ( Fig . 1C ) ., Phosphorylation signals were also quantified and displayed as a ratio with the amount of unphosphorylated signaling mediator ( Fig . 1C ) ., These findings suggest that Pvr triggers activation of the Akt/TOR and Mek/Erk and pathways , thereby supporting anti-apoptotic cell survival and proliferation ., Next , we asked whether silencing of either or both of these pathways is sufficient to affect cell viability and mimic loss of Pvr function ., Combining dsRNAs targeting various mediators of the Akt/Tor and Mek/Erk pathways , we found that , despite efficient knockdown of the genes ( S2 Fig ) , neither single nor simultaneous inhibition of both pathways caused a significant reduction of cell numbers , as quantified by CellTiterGlo assay based on ATP content ( Fig . 1D ) , and cell counting ( Fig . 1E ) ., In contrast , Pvr RNAi , showed significant decreases in cell number ( Fig . 1D , E ) ., This predicted the presence of one or more additional , redundant cell survival/proliferation pathway ( s ) downstream of Pvr ( ‘X’ , Fig . 1F ) , and/or parallel signaling pathways that contribute to the overall survival and proliferation of the cell ( ‘Y’ , Fig . 1F ) ., Based on our prediction , we sought to identify other signaling pathways that contribute to the anti-apoptotic survival of Kc cells ., We hypothesized that re-activation of just one survival or proliferation pathway would be sufficient to rescue cell numbers in Pvr deficient cells ( Fig . 1F ) ., Indeed , silencing of negative regulators of the Akt/Tor and Erk pathways rescued the Pvr RNAi phenotype , validating our screening approach ., For these experiments , we ruled out that silencing of downstream signaling mediators such as Akt would result in upregulation of Pvf2 expression , the major Pvr ligand in Kc cells that mediates autocrine signaling ( S3 Fig ) ., Expanding our approach , we screened the DRSC Genome-Wide RNAi library 1 . 0 ( Drosophila RNAi Screening Center , Harvard Medical School ) for modifiers of cell number , specifically under conditions of Pvr RNAi-mediated silencing compared to a control background ( Fig . 2A ) ., The DRSC 1 . 0 set targets 22 , 914 distinct amplicons based on Flybase release 5 . 51 of the Drosophila genome , corresponding to 13 , 777 unique genes 31 , 6944 of which are expressed in Kc cells 29 ., Screening was performed in 384-well format , quantifying ATP content as a readout of cell number ( CellTiterGlo ) ., To determine an increase or decrease over the average value of ATP content , Z scores were calculated for each well ., Focusing on those dsRNAs that show differential effects in Pvr knockdown ( Pvr RNAi ) versus control cells ( GFP RNAi ) , we calculated the difference of each of the Z scores ( ZDiff = ZPvr-ZGFP ) , and selected amplicons with ZDiff> = 2 and ZDiff< = -2 as primary screen hits ( S1 Table ) ., Cluster analysis of the values ZPvr , ZGFP , and ZDiff for each amplicon revealed three distinct classes of signatures , i . e . Pvr Suppressors , Pvr Enhancers , and Pvr ‘Upstream Genes’ ( Fig . 2B ) ., By our cutoff criteria , 64 amplicons scored as suppressors of the Pvr knockdown phenotype , rescuing cell numbers more effectively in the Pvr RNAi background compared to control cells ., 65 amplicons scored as Pvr Enhancers , exacerbating the Pvr knockdown phenotype ., We classified 290 amplicons as Pvr ‘Upstream Genes’ , reducing cell numbers in control cells , but having rather minor effects in the Pvr silenced background ., Among this group we found amplicons targeting Pvr itself and many ribosomal proteins , suggesting that many of the targeted genes play a role in the production or activation of Pvr ( S1 Table ) ., Subsequent secondary testing of screen hits was carried out for Pvr Suppressors and Pvr Enhancers ., We selected 47 suppressor genes and 47 enhancer genes based on a cutoff of ZDiff> = 2 . 2 and ZDiff< = -2 . 2 ( S2 Table ) and synthesized non-overlapping alternative amplicons that were free of 19bp or larger overlaps with other genes , in order to avoid off-target effects 32 , 33 ., As in the primary screen , amplicons were tested for their ability to modify cell number , specifically comparing Pvr knockdown cells relative to control cells ( S2 Table ) ., To identify promising ‘high confidence candidates’ for further analysis , we calculated the average of the ZDiff scores among all amplicons of a gene from the primary and secondary screens ( ZDiffFinal ) ( S3 Table ) ., Based on ZDiffFinal values of > = 1 . 6 and < = -1 . 2 , we report 30 high-confidence Pvr Suppressors and 14 high-confidence Pvr Enhancers ( S3 Table ) ., Z value cutoffs were guided by the scores of predicted genes within the set , such as members of the Akt/Tor and Mek/Erk pathways ., Candidates of specific interest were confirmed by live/dead cell counting , omitting genes with obvious roles in RNA interference , such as AGO2 ( S4 Fig ) ., Relatively few genes scored as Pvr Enhancers ., Among those , we identified the RTK InR 34 , and cropped ( crp ) encoding the helix-loop-helix transcription factor that is a homolog of the mammalian transcription factor AP-4 35 ., The screen also identified tonalli ( tna ) , encoding a protein similar to mammalian ZMIZ1 and ZMIZ2 involved in sumoylation 36 that interacts genetically with the Brahma ATP-dependent chromatin remodeling complex in Drosophila 37 ., Among the Pvr Suppressors , the screen yielded all known tumor suppressors and negative regulators of the Akt/TOR pathway , including Phosphatase and Tensin Homolog ( Pten ) , Tuberous Sclerosis Protein 1 ( Tsc1 ) , gigas ( gig ) /Tuberous Sclerosis Protein 2 ( Tsc2 ) , SNF4A–a and -γ , also known as AMP-Activated Protein Kinase subunits a and γ ( AMPK–α and AMPK–γ ) , Forkhead Box Protein ( foxo ) , and Lobe ( L ) , a protein with similarities to the vertebrate Proline-rich Akt substrate of 40 kDa ( PRAS40 ) 38–41 ., We further identified negative regulators of the Ras/Erk pathway Mitogen-activated protein kinase phosphatase 3 ( Mkp3 ) , and microtubule star ( mts ) and widerborst ( wdb ) , which encode components of the protein phosphatase PP2A complex 42–44 ., We calculated which protein complexes were over-represented with respect to the frequency of their components among the high confidence hits in the RNAi screen , and found , besides the PP2A complex , two other major protein complexes among the high confidence hits in the RNAi screen ( Fig . 2C ) : the ecdysone receptor complex , consisting of the nuclear hormone receptors EcR and usp 27 , 45 , and the Brahma SWI2/SNF2 family ATPase chromatin-remodeling complex , comprising osa and dalao 46 , 47 ., Other Pvr Suppressors were CG6182 , an ortholog of mammalian TBC1 domain member 7 ( TBC7 ) , and GckIII , and CG31635 , an ortholog of mammalian LRRC68 ., Given the reported interplay between ecdysone and insulin signaling during development 48 , we wanted to dissect whether common and/or distinct downstream mechanisms mediate Pvr suppression , and therefore chose InR and EcR /usp for in vivo validation ., Using Kc cells , we examined the functional roles of InR and EcR/Usp in more detail ., EcR and Usp form a heterodimer and are induced by binding of the steroid hormone 20-hydroxyecdysone ( 20E ) 45 , 49 ., Signaling by the EcR complex plays a major role during molting and metamorphosis 50 , yet a role in embryonic cell death and cell number control has not been established 51 ., We confirmed the effects of silencing or stimulating InR , or silencing EcR or usp , on Pvr RNAi-induced apoptosis using TUNEL assays , and we quantified effects on proliferation using EdU incorporation in Kcp35 cells ( Fig . 3A-C ) ., Consistent with the results from the screen , we found that , in combination with Pvr knockdown , silencing of InR exacerbated apoptosis ., Further , silencing of EcR or usp , or stimulation of InR with insulin rescued apoptosis ( Fig . 3A ) ., In contrast , when examining proliferation , only insulin stimulation or a Tsc2/gigas ( gig ) RNAi Akt pathway control significantly suppressed proliferation defects , suggesting that EcR and Usp mainly function in the regulation of cell death , rather than proliferation ( Fig . 3B , C ) ., InR knockdown seemed to enhance the reduction of EdU incorporation in Pvr knockdown cells , but differences were not statistically significant based on three independent biological replicate experiments ( Fig . 3C ) ., Next we examined the effects of ecdysone stimulation ., Anti-proliferative effects of ecdysone in Kc cells have been reported previously 52–54 , but whether ecdysone also has direct pro-apoptotic effects in embryonic cells not been determined ., To test this , we stimulated Kc cells with 20E at concentrations close to physiological levels ( 0 . 01ug/ml ) 54 , 55 ., Overall , 20E induced a marked reduction in cell number at stimulation times of >48h ( Fig . 3D ) ., As expected , it resulted in a reduction of cell proliferation as measured by EdU incorporation , both in Kc and in apoptosis-resistant Kcp35 cells ( Fig . 3E ) ., However , TUNEL analysis showed a substantial increase in apoptotic cells upon 20E stimulation , which was largely suppressed in Kcp35 cells ( Fig . 3F ) ., 20E did not cause a decrease of Pvr protein levels ( S5 Fig ) , suggesting that molecular mechanisms other than Pvr downregulation account for the observed increase in apoptosis ., During metamorphosis-associated programmed cell death ( PCD ) , several genes have been described as ecdysone-induced pro-death targets , in particular Ecdysone-induced protein 93F ( E93 ) , broad ( br ) , Ecdysone-induced protein 74EF ( E74A ) , and reaper ( rpr ) 56–58 ., When we examined the expression levels of these genes during ecdysone stimulation of Kc cells we found that , indeed , rpr and E93 levels increased from the first day of 20E stimulation ( Fig . 3G ) , consistent with the induction of apoptosis ., We also examined whether Pvr knockdown would have an effect on the expression of rpr and E93 but found no significant difference relative to controls ( S6 Fig ) ., In summary , we conclude that the EcR complex has pro-apoptotic functions in the cell line Kc , which become apparent under sensitized conditions of Pvr loss of function , or experimental addition of 20E ., To test the role of InR and EcR in the suppression and enhancement of apoptosis in vivo , we examined the function of these genes in the survival of hemocytes in the Drosophila embryo ., Drosophila embryos typically show a developmentally fixed number of ~600 hemocytes post stage 11 until early stage 17 , and loss of Pvr signaling causes a rapid decline in hemocytes due to their apoptotic death and phagocytic clearance by the small number of remaining live hemocytes 12 ., Based on our findings in Kc cells , we predicted that inhibition of EcR would rescue , and inhibition of InR would enhance , Pvr loss-of-function in embryonic blood cells 12 ., Indeed , hemocyte-specific suppression of EcR signaling by expression of dominant-negative forms of EcR 59 , 60 partially rescued hemocyte counts in Pvr1 mutant embryos ( Fig . 4 ) , resembling rescue by the baculovirus inhibitor of apoptosis , p35 12 ( see also Fig . 4A ) ., Conversely , expression of dominant-negative InR in hemocytes enhanced the Pvr phenotype , further reducing embryonic hemocyte numbers ( Fig . 4 ) ., Consistently , we previously demonstrated that activated PI3K , a positive mediator of the Akt/TOR pathway downstream of InR , can partially rescue the Pvr mutant in vivo phenotype 12 ., To confirm hemocyte autonomous effects of EcR and InR , we induced embryonic hemocyte death by hemocyte-specific expression of dominant-negative PvrΔC 12 , and examined the effects of co-expressed dominant-negative versions of EcR or InR ., Again , we found that dominant-negative EcR rescued apoptotic loss of hemocytes , while dominant-negative InR exacerbated the cell death phenotype ( Fig . 4 ) ., Expression of the transgenes in the wild type background had no significant effects ( Fig . 4A ) ., Intrigued by the mild increase of hemocyte numbers upon overexpression of dominant-negative EcRdn ( Fig . 4A ) , we asked whether blocking EcR signaling alone would have a positive effect on hemocyte numbers at a later point during development , for example at the transition from the embryo to the larval stage ., A time course of total hemocyte counts in live animals illustrates that , compared to stage 16 embryos , hemocyte numbers in young 1st instar larvae decline to about 60% , suggesting a putative connection with the embryonic ecdysone peak in mid-embryogenesis 61 ( S7 Fig ) ., However , comparing live hemocyte counts of controls to animals with hemocyte-specific expression of EcRdn , we did not see a significant rescue in the total number of hemocytes , despite a mild increase in EcRdn overexpressing larvae ( S7 Fig ) ., Taken together , our findings suggest that EcR signaling accounts for a basic level of pro-death signaling in embryonic hemocytes , which however is revealed only under sensitized conditions such as Pvr loss of function ., Conversely , signaling by InR contributes to the trophic survival of embryonic hemocytes , which acts redundantly with Pvr signaling , and therefore again is only evident in conjunction with loss of Pvr signaling ( Fig . 4G ) ., Based on our findings , we sought to further dissect the relationship between Pvr , InR and EcR signaling ., First , we asked whether signaling by the EcR complex acts epistatically or in parallel with RTK-triggered signaling pathways such as Akt/Tor ., When comparing the effects of silencing of the EcR/Usp and Akt/Tor pathways separately and in combination , we found that simultaneous knockdown of genes from both pathways resulted in increased cell number rescue ( e . g . EcR and Pten ) , which in many cases was significant when compared to knockdown of two genes from the same pathway ( i . e . EcR and usp , or Pten and gig ) ., This suggested a parallel , rather than epistatic relationship ( Fig . 5A ) ., Biochemically , insulin stimulation of Pvr deficient cells restored , albeit to distinctive levels , phosphorylation of downstream signaling mediators of the Akt/Tor and Mek/Erk pathways , while EcR knockdown did not show such effects ( Fig . 5B ) ., This suggested similar but not identical signaling profiles for the RTKs Pvr and InR , and distinct mechanisms for the EcR complex ., To compare the signaling profiles of Pvr , InR and EcR in a more systematic manner , we chose a phosphoproteomics approach ., We utilized mass spectrometry and an isobaric labeling strategy that enables multiplexing and relative quantification between samples 62 , 63 ., For this analysis , we surveyed the phosphoproteome by formally comparing conditions of ( 1 ) ‘high Pvr’ signaling ( + control dsRNA; taking advantage of the high endocrine Pvr activity in Kc cells ) ; ( 2 ) ‘low Pvr’ signaling ( + Pvr dsRNA ) ; ( 3 ) ‘high InR’ signaling ( + insulin , to stimulate endogenous InR in Kc cells ) ; ( 4 ) ‘low InR’ signaling ( + control dsRNA; taking advantage of the low InR activity in Kc cells under standard culture conditions presumably due to low levels of dIlp expression 29 , see also Fig . 5B ) ; ( 5 ) ‘high EcR’ ( endogenous EcR in Kc cells ) ; and ( 6 ) ‘low EcR’ ( + EcR dsRNA ) ., First , we assessed which phosphoproteins were up- or downregulated in the rescue of Pvr silenced Kc cells ., We surveyed the phosphoproteome under conditions of high and low Pvr activity ( Fig . 6A and B , respectively ) , and analyzed separately for the two conditions the effects of EcR silencing or InR activation , assessing biological duplicates ( S4 Table and S5 Table ) ., Under ‘high Pvr’ conditions , approximately 10% of the detected phosphorylation was altered more than 1 . 5-fold under conditions of InR stimulation , which we refer to as the ‘InR-specific set’ ( Fig . 6A and S4 Table ) ., This percentage nearly doubled under ‘low Pvr’ conditions ( Fig . 6B and S5 Table ) ., Although some of these phosphorylations could be attributed to the fact the InR may phosphorylate Pvr targets in the absence of Pvr , this finding also suggested the emergence of new sets of up and down-regulated phosphosites that were not observed upon InR activation under ‘high Pvr’ conditions ( below ) ., Secondly , we sought to directly measure the degree to which phosphosites were altered by InR under conditions of ‘high’ versus ‘low’ Pvr signaling , hypothesizing a ‘sensitization’ of InR signaling by the absence of Pvr ., We repeated our phosphoproteomic analysis , this time directly comparing the six experimental signaling conditions among each other ( Fig . 7A and S6 Table ) ., While nearly three-quarters of the ‘InR-specific set’ of phosphopeptides remained upregulated following InR activation in the absence of Pvr , the ‘InR-specific set’ showed qualitative differences in the absence and presence of Pvr signaling ., For instance , InR stimulation elevated levels of phosphorylation of fifteen phosphoproteins specifically under ‘low Pvr’ activity as compared to ‘high Pvr’ signaling ., These included Chromosome-associated protein ( Cap ) , lava lamp ( lva ) , Enhancer of decapping 3 ( Edc3 ) , Bicaudal D ( BicD ) , lethal ( 2 ) 03709 , eukaryotic translation Initiation Factor 2α ( eIF-2 α ) and several uncharacterized gene products ., InR activation restored phosphorylation to nearly all sites downregulated in Pvr deficient cells , ( Fig . 7C ) ., These phosphorylations likely account for the ability of insulin to rescue Pvr deficiency ., EcR knockdown , meanwhile , had very little effect on the phosphoproteome , both in low and high Pvr conditions ( Fig . 6A , B ) , despite efficient knockdown ( S8 Fig ) ., Similar findings were made from the comparative analysis of all six experimental conditions ( Fig . 7A , B ) ., This is consistent with an alternative mode of action , such as the transcriptional modulation of EcR/Usp target genes ( see Fig . 3G ) ., Lastly , we identified a pool of common phosphoproteins induced by both Pvr and InR , which comprise signaling mediators for common functions in cell survival and proliferation ., At the same time , we distinguished Pvr- or InR-associated targets that may mediate receptor-specific functions ., A common set of phosphorylation targets for Pvr and InR , either direct or indirect , can be inferred from the reciprocal effects of Pvr knockdown and InR stimulation , comparing ‘low Pvr’ and ‘high InR’ conditions ( Fig . 7B; 153 phosphosites: S7 Table ) ., Examples include phosphorylation of Structure specific recognition protein ( Ssrp ) , La related protein ( Larp ) , eukaryotic translation initiation factor 4G ( eIF4G ) , Lamin , NAT1 , Claspin , Gartenzwerg ( Garz ) , Nedd4 , Nopp140 , Lk6 , Yorkie ( Yki ) , Stat92E , and Moleskin ( Msk ) ., Many of these common signaling mediators function in cell survival and cell proliferation ., For example , the transcription factor Yki coordinates cell proliferation and apoptosis by directing the expression of cell cycle and cell death regulators 64 ., Stat92E loss-of-function has been reported to inhibit hemocyte proliferation 65 , 66 , while the importin Msk localizes MAP kinase to the nucleus to promote cell proliferation and survival 67 ., We found enrichment for the regulation of phosphorylation of components of specific complexes by both InR and Pvr , including the Chs5p/Arf1-binding protein complex , the chromatin remodeling FACT complex , the translation initiation factor 2 complex , the cohesion-Sa complex , TRAPP complex and splicing associated factor complex ( Fig . 7D , E ) ., While we do not expect that all components of an individual complex require an alteration in phosphorylation in order for complex activity to change , more confidence for implication of that complex downstream of Pvr or InR is gleaned from multiple components exhibiting altered phosphorylation ., As such , we expect that these complexes play key roles downstream of both InR and Pvr ., To distinguish Pvr- or InR-specific targets that may mediate receptor-specific functions we compared phosphoproteomes under ‘high Pvr , low InR’ and ‘low Pvr , high InR’ conditions ( S8 Table ) ., Among the Pvr-specific phosphorylations , we identified phosphoproteins involved in cell migration , cytoskeleton , and regulation of cell shape such as CIN85 and CD2AP ortholog ( Cindr ) , Tenascin major ( Ten-m ) , Vacuolar protein sorting 4 ( Vps4 ) , Rab7 , Rho GTPase activating protein at 15B ( RhoGAP15B ) , and Sprouty ( Sty ) ., Cindr is a recognized component of the CIN85 complex , one of three complexes for which multiple components exhibited a dependence on Pvr for specific phosphorylation ( Fig . 7F ) ., With respect to InR-specific phosphorylations , we detected phosphoproteins associated with the Gene Ontology Consortium terms growth regulation ( i . e . Gp150 , Foxo , L , Chico ) , glycogen metabolism ( i . e . Glycogen Synthase ) , and the innate immune response ( i . e . G protein-coupled receptor kinase interacting ArfGAP and Mustard ) ., These differential phosphorylations likely provide receptor specificity and function to modulate the activity of specific complexes such as those over-represented in terms of the number of components modulated by InR activity ( Fig . 7G ) ., Previously , we demonstrated that Pvr mediates cell survival in the Drosophila embryonic hematopoietic system and in Drosophila Kc cells in culture 12 ., Similar roles for Pvr in other cell populations such as glia were subsequently reported 69 ., Here , we find that Pvr also contributes to the proliferation of Kc cells , which is revealed when Pvr-dependent cell death is suppressed ., These Pvr functions are well conserved with mammalian systems , where PDGF/VEGF Receptors mediate cell survival and proliferation during normal development 70 , 71 and in pathologies such as leukemias and other forms of cancer 26 , 72 , 73 ., Our findings encompassing the role of Pvr in the activation of the Mek/Erk and Akt/Tor pathways are consistent with previous reports of Pvr-dependent phosphorylation of Erk 21 , 23 , the activation of the TOR1 Complex and Erk by Pvr 14 , and the physical interaction of Pvr with PVRAP , Grb2 , Shc , and the regulatory subunit of PI3K in cell culture 14 , 74 ., Since our screen was designed to eliminate general regulators of cell number and instead focus on those genes that show differential effects under sensitized conditions , it predominantly revealed genes with tumor suppressor-like activities ( Pvr Suppressors ) , many of which were not detected in conventional RNAi screens for cell proliferation or survival previously 17 , 75–78 ., Several of the identified Pvr Enhancers ( 5/14 ) and half of the Pvr Suppressors scored as hits in other genome-wide RNAi screens examining RTK signaling , specifically InR and EGFR signaling using the same screening platform and dsRNA libraries 79; see S3 Table for specific overlap ) ., Many genes identified in the screen regulate redundant pro-survival pathways downstream of Pvr ( Fig . 8 ) , as was predicted by our initial screening hypothesis ( Fig . 1F ) , and which is also supported by others 14 ., However , some regulators identified in the screen instead act in pathways parallel to Pvr signaling , as we demonstrated for InR and EcR signaling ., Among the RNAi screen hits , we distinguished three major classes of modifiers ., First , we identified a large group of ‘Upstream Genes’ that specifically affect cell number only in signaling competent , but not Pvr depleted cells ., Among these , we found a large number of ribosomal protein genes ., Interestingly , a recent Drosophila in vivo study identified ribosomal protein RpS8 as functional upstream regulator of Pvr in hemocytes of the lymph gland , proposing it may exert its function by interaction with Bip1 ( bric à brac interacting protein 1 ) , which shows similar phenotypes 16 ., While our screen did not identify Bip1 , it revealed RpS8 as putative Pvr ‘Upstream Gene’ ., Ribosomal subunits may promote Pvr expression also as part of the general translation machinery , or may play more specialized roles in translation regulation , according to previous reports on target-specific ribosomal activities that may influence the cellular signaling makeup in development and tumorigenesis 80–82 Second , inherent to our system , our screen yielded relatively few Pvr Enhancers ., From this group we chose InR for verification analysis by in vivo genetics , which we further complemented with a phosphoproteomic survey that illuminated synergy between Pvr and InR ., Analogous synergistic relationships between InR with other RTKs have been reported in Drosophila development 69 , 83 , 84 , and vertebrate signaling 85 ., The specificity of redundant RTK signaling pathways is of major interest is the fields of cell signaling and cancer research and subject of ongoing intense study 68 , 86 ., Third , the screen yielded a group of Pvr Suppressors , which function as tumor suppressor-like genes whose loss rescues cell survival under sensitized conditions ., This group contains all negative regulators of the Akt/Tor pathway , many of which are known tumor suppressors in mammalian systems 39–41 , and several negative regulators of the Mek/Erk pathway such as mts and wdb , encoding for components of the PP2a complex 42 , 43 , and Mkp3 , which encodes for a phosphatase known to negatively regulate Erk 44 ., As expected , several genes identified in the Pvr modifier screen also scored in previous screens for signaling mediators of the Pvr , Akt/Tor and RTK/Erk pathways 14 , 83 , 87 ., The screen also revealed novel , or only recently characterized , genes ., CG6182 is an ortholog of the mammalian TBC7 , that interacts physically with Tsc1 88; GckIII is a counterpart of mammalian Serine Threonine Kinase 25 ( STK25 ) , also known as SOK1 , that localizes to the Golgi 89 and induces cell death upon overexpression in mammalian cell culture 90 ., Some of the identified genes have been characterized in Drosophila , yet no role in cell number control in the embryo has been described ., For example , we identified multiple members of the Brahma SWI2/SNF2 family ATPase chromatin-remodeling complex 46 , 47 , with osa and dalao scoring as Pvr Suppressors , and Brahma associated protein 60kD ( Bap60 ) and moira ( mor ) scoring in mixed categories ., Two of the strongest hits among the Pvr Suppressors were genes encoding the nuclear hormone receptors EcR and Usp 27 , 45 , which we followed up with subsequent analyses ., EcR and Usp have previously been studied for their roles in proliferation , differentiation and cell death during larval molting and metamorphosis 50 , 61 , 91 ., In Kc cells , the EcR/Usp ligand ecdysone has been known to arrest the cell cycle and trigger a cell differentiation program 52–54 ., However , neither in the embryo nor in Kc cells has ecdysone signaling been previously associated with cell death 51 , 92 ., Here , we describe a role for ecdysone signaling in embryonic cell death , a function revealed only under sensitized conditions or when directly stimulating EcR pathway activity ., When treating Kc cells with 20E , we find that the EcR targets E93 and rpr are transcriptionally upregulated , consistent with previous re

Introduction, Results, Discussion, Materials and Methods

In multicellular organisms , cell number is typically determined by a balance of intracellular signals that positively and negatively regulate cell survival and proliferation ., Dissecting these signaling networks facilitates the understanding of normal development and tumorigenesis ., Here , we study signaling by the Drosophila PDGF/VEGF Receptor ( Pvr ) in embryonic blood cells ( hemocytes ) and in the related cell line Kc as a model for the requirement of PDGF/VEGF receptors in vertebrate cell survival and proliferation ., The system allows the investigation of downstream and parallel signaling networks , based on the ability of Pvr to activate Ras/Erk , Akt/TOR , and yet-uncharacterized signaling pathway/s , which redundantly mediate cell survival and contribute to proliferation ., Using Kc cells , we performed a genome wide RNAi screen for regulators of cell number in a sensitized , Pvr deficient background ., We identified the receptor tyrosine kinase ( RTK ) Insulin-like receptor ( InR ) as a major Pvr Enhancer , and the nuclear hormone receptors Ecdysone receptor ( EcR ) and ultraspiracle ( usp ) , corresponding to mammalian Retinoid X Receptor ( RXR ) , as Pvr Suppressors ., In vivo analysis in the Drosophila embryo revealed a previously unrecognized role for EcR to promote apoptotic death of embryonic blood cells , which is balanced with pro-survival signaling by Pvr and InR ., Phosphoproteomic analysis demonstrates distinct modes of cell number regulation by EcR and RTK signaling ., We define common phosphorylation targets of Pvr and InR that include regulators of cell survival , and unique targets responsible for specialized receptor functions ., Interestingly , our analysis reveals that the selection of phosphorylation targets by signaling receptors shows qualitative changes depending on the signaling status of the cell , which may have wide-reaching implications for other cell regulatory systems .

Signaling networks that drive cell survival and proliferation regulate cell number in development and disease ., We use a simple Drosophila model of cell number control , which centers on PDGF/VEGF receptor signaling ., Performing a genome-wide RNAi screen under Pvr-sensitized conditions , we identify regulators of cell number that have not been found in conventional screens ., Validation by in vivo genetics reveals previously unrecognized roles for EcR and InR in the balance of cell survival in the Drosophila embryo ., Phosphoproteomic analysis demonstrates distinct mechanisms of cell survival regulation by EcR and receptor tyrosine kinase signaling ., It further identifies common phosphorylation targets of Pvr and InR including regulators of cell survival , and receptor-specific phosphorylation targets mediating unique functions of Pvr and InR ., Importantly , the study provides precedence that the selection of phosphorylation targets by signaling receptors can change with the signaling status of the cell , which may have wide-reaching implications for other cell regulatory systems .

journal.pcbi.1005981

Differential tissue growth and cell adhesion alone drive early tooth morphogenesis: An ex vivo and in silico study

From gastrulation to late organogenesis , animal development involves many examples of reciprocal genetic and biomechanical interactions between epithelial and mesenchymal tissues 1 ., Ectodermal organs , such as hairs , feathers , teeth and mammary glands are easily accessible examples of organs whose development is based on epithelial-mesenchymal interactions ., Despite the diversity in their mature form and function , ectodermal organs share several common features during the early steps of development 2–4 ., Organogenesis begins with the appearance of local epithelial thickenings , placodes , followed by condensation of the underlying mesenchymal cells ., Later , the placode develops into a bud that grows into or out of the mesenchyme ensued by further growth and morphogenesis specific to each organ 2 ., The genetic regulation of this latter process is relatively well understood 2 , 3 , whereas the contributing cellular and bio-mechanical mechanisms have remained largely unexplored ., In this study we explore this latter question for tooth development as an example ectodermal organ ., Teeth develop through a complex process which combines cell signalling , extensive cell movements and tissue deformation 5–8 ., At the late bud stage ( Fig 1A ) , a signalling centre appears in the epithelium , the primary enamel knot , and a mesenchymal condensate forms beneath the epithelial bud ., This is followed by the emergence of two epithelial folds , the buccal and lingual cervical loops , on the respective sides of the tooth germ ., These initially grow laterally but progressively change their orientation , or angle of growth , downwards ( cap stage , Fig 1B ) ., The angle of growth of the cervical loops is defined here as the angle between the tips of the cervical loops and the primary enamel knot in a frontal plane ( Fig 1C ) ., The cervical loops largely delineate the boundaries of the tooth germ ., The epithelium between the cervical loops is called the inner enamel epithelium and its growth and folding largely determine the overall shape of the tooth crown ., The rest of the epithelial sheet , called the outer enamel epithelium , will not form part of the tooth crown ., Similarly , the mesenchyme enclosed by the cervical loops , called the dental mesenchyme , will become part of the tooth crown , whereas the mesenchyme in contact with the outer enamel epithelium , called the follicular mesenchyme , will not ., The tissue located on the apical side of the enamel epithelium is called the suprabasal layer and it is already present at the earliest stages of tooth development ( Fig 1A ) ., At the late cap stage , secondary enamel knots form in the inner enamel epithelium and a cusp will arise under each one of them 9 ., The tooth germ progressively elongates in the anterior-posterior axis and cervical loops form in the anterior and the posterior of the tooth germ as a continuation of the buccal and lingual cervical loops ., The angle of growth of the cervical loops has a general effect on the sharpness of the tooth , with smaller angles leading to sharper teeth 10 , and in some species on the positioning of secondary enamel knots 11 ., At later stages , enamel secreting ameloblasts and dentin secreting odontoblasts differentiate at the interface between the inner enamel epithelium and mesenchyme to form the mineralized tooth ., In this study we build a new mathematical model of early tooth development ., Our aim is to understand how the tooth germ changes in shape from the bud to the cap stage ., This implies understanding how the cervical loops form , what determines the orientation of their growth in the different parts of the tooth germ and how that affects the shape of the tooth germ ., Theoretical studies in development and tissue mechanics predict that epithelial folding can be the result of differential growth between adjacent tissues 12–14 ., For example , differential growth between the epithelium and the suprabasal layer modelled in 2D has been used to produce buckling reminiscent of the bud to cap stage transition 14 ., Although not considering biomechanical properties such as differential adhesion , the previous studies underscore the requirement of differential growth in the progression of epithelial development beyond the bud stage ., Here we hypothesize that the growth and orientation of the cervical loops depends on both the differential growth and differential adhesion between the epithelium , the suprabasal layer , and the mesenchyme ., Variation in differential growth and adhesion should then lead to variation in the orientation of the cervical loops ., We implement this hypothesis in a new 3D cell-based model of tooth morphogenesis using the recent EmbryoMaker modelling framework of animal development 15 ( see S1 Appendix ) ., EmbryoMaker is essentially a modelling tool that uses a mathematical implementation of the basic cellular and molecular processes known in animal development ., We use this modelling framework to build a tooth specific model by specifying to EmbryoMaker the distribution of cell types at the bud stage , gene expression dynamics and signalling , and the cell behaviours involved in early tooth development ., This study , however , does not restrict itself to study the wild-type mouse molar ., The aim is also to understand how the length and orientation of the cervical loops in 3D changes with differential growth and adhesion ., There are several previous mathematical models of tooth development 16 , 17 ., These models implement only the inner enamel epithelium and cannot be used to model the cervical loops and the dynamics that define the boundary between inner and outer enamel epithelium ., Furthermore , these earlier models do not explicitly consider the three main tooth germ tissues , epithelium , mesenchyme , and suprabasal layer and , thus , cannot study , in an integrated way , how their differential adhesion and growth affect early tooth development ., In addition , EmbryoMaker implements much more refined cell and tissue biomechanics than previous models , thereby allowing us to decompose the different roles of tissue growth and adhesion dynamics ., In order to assess which hypotheses were capable of reaching cap morphology , we performed a parameter screening on the tissue-specific growth parameters ., We ran the tooth-specific model under different combinations of tissue growth rates ( sepi , ssup and smes parameters ) and under the three different hypotheses ( Fig 3 , S3 Fig ) ., For all hypotheses , we found that the cervical loops appeared at the buccal and lingual sides of the tooth germ when the proliferation rate in the epithelium was high relative to the suprabasal and mesenchymal proliferation rates ( Fig 3A , 3B and 3E ) ., The length of these loops increased with the epithelial to suprabasal proliferation ratio ( S3A–S3C Fig ) ., High suprabasal growth rates relative to the epithelium growth rates led to enlarged buds lacking cervical loops ( Fig 3C and 3F ) ., However , only in hypotheses II and III ( Fig 3B and 3E ) did the cervical loops grow downwards similarly to the wild-type ( small growth angle ) ., In hypothesis I , the cervical loops grew with an angle close to 180° ( Fig 3A ) , and the follicular mesenchyme was roughly as thick as the dental mesenchyme , unlike wild-type tooth germs ( Fig 1B ) ., Thus , we rejected hypothesis I and concluded that the formation of the cervical loops requires the proliferation rate of the follicular mesenchyme to be lower than that of the dental mesenchyme ., In hypothesis II , low mesenchymal growth ( smes<0 . 20 ) led to an abnormal shape of the dental mesenchyme , with additional epithelial folds between the cervical loops ( Fig 3D ) ., In hypothesis III , the height of the dental mesenchyme ( the difference in height between the tip of the cervical loops and the base of the signalling centre ) increased with the proliferation rate of the mesenchyme ( Fig 3G ) ., Thus , in hypotheses II and III , a sufficiently high proliferation of the dental mesenchyme is necessary for normal cap morphology to arise ., Next we wanted to test whether the model was able to create a realistic cap morphology with tissue growth rates estimated from experimental observations ., We measured the length of the dental epithelium and the surface area of the suprabasal layer from the time-lapse sequence of a developing mouse molar recently published by Morita and collaborators 6 at different time points , thus creating a growth curve for both the epithelium and the suprabasal layer ., The time-lapse in Morita et al . was performed on a molar thick frontal section , thus the data was essentially 2D ., In order to better compare that data to our model , we created a 2D version of the tooth model ( Fig 4A and 4B ) , based on the same principles as the original model , and performed a large parameter screening of tissue growth and adhesion parameters ., For each simulation we calculated the growth curves of the epithelium and the suprabasal layer , and then compared them to the empirical ones by calculating the standard error between the theoretical and empirical curves ( S4 Fig , see S1 Appendix ) ., We performed a sensitivity analysis of the standard error measurement against each of the parameters chosen for the screening ( S5 Fig ) ., In both hypothesis II and III , the model showed the largest sensitivity on the epithelial growth parameter ( sepi ) ( S5A and S5E Fig ) , whereas it showed the lowest sensitivity to the mesenchymal growth parameter ( smes ) and the five adhesion parameters ( S5C , S5D , S5G and S5H Fig ) ., In both hypotheses II and III , a relatively large number of model runs ( approximately 500 in each case ) had a good fit ( standard error < 10 ) with the empirical growth curves ., Visual inspection of the morphology in these better fitting subsets showed that the great majority of model runs achieved a proper cap morphology in the 2D model ( e . g . Fig 4C–4F ) ., Even if the model predicted correctly the overall morphology of the tooth germ , it could be that this was accomplished by different patterns of cell movement compared to the real tooth ., We proceeded to compare cell movement in the 2D model ( see S1 Appendix ) with the empirical cell trajectories during tooth development 6 ., In their study , Morita et al . recorded multiple cell trajectories on the epithelium and suprabasal layer during their time-lapse ( Fig 5A and 5D , see also 6 ) ., In our model , suprabasal cells showed a consistent movement towards the tips of the cervical loops in both hypotheses II and III ( Fig 5B and 5C ) ., We then decided to focus on the behaviour of epithelial cells ., In Morita’s experiments , they tracked a small group of epithelial cells at the tip of both cervical loops , and they observed that by the end of the tracking period they still remained at the tip , even though the tissue had grown significantly ( Fig 5D ) ., When looking at the movement of epithelial cells in our simulations , differences were observed between hypothesis II and III ., In hypothesis II , cells that were at the tip of the cervical loops at the start of the tracking period had moved outwards by the end of the tracking , and cells that were medially located at the start moved to the tip of the cervical loops ( Fig 5E ) ., Lateral movement of epithelial cells in II was caused by the fact that cells only proliferated close to the enamel knot and slid sideways as the epithelium grew ( Fig 5E ) ., In hypothesis III , cells that started at the tip of the cervical loops remained in them by the end of the tracking period ( Fig 5F ) ., Thus , it seems unlikely that epithelial cells proliferate only near to the enamel knot , since the cell movements observed ex vivo should be different than the ones presented by Morita and collaborators ., Thus , we decided to reject hypothesis II and , for the rest of the study we only show the computational analyses corresponding to hypothesis III ., Note , however , that the analyses described in the following sections were also performed for hypothesis II and led to the same conclusions than III , but we chose to exclude them for the sake of simplicity ., We performed a parameter screening in the 3D model for the five adhesion parameters: epithelium homotypic ( bEE ) , epithelium-suprabasal ( bES ) , epithelium-mesenchyme ( bEM ) , suprabasal homotypic ( bSS ) , and mesenchyme homotypic adhesion ( bMM ) ., The resulting morphologies displayed differences in the length and orientation of the cervical loops ( Fig 6 , S6 Fig ) ., By looking at the frontal sections , we observed that high mesenchyme homotypic adhesion led to short cervical loops oriented downwards ( small growth angle ) , whereas low values led to long cervical loops oriented bucco-lingually ( large growth angle ) ( Fig 6A ) ., The latter effect was more marked when the suprabasal homotypic adhesion was high ( Fig 6A , top row ) ., The cervical loops were also oriented downwards when the epithelium-mesenchyme adhesion was high , especially when the mesenchyme homotypic adhesion was also high ( Fig 6B , top row ) ., This seemed to occur because the epithelium deformed to maximize its contact area with the dental mesenchyme , even when the mesenchymal homotypic adhesion was null ( e . g . Fig 6B left column ) ., In contrast , with null epithelial-mesenchymal adhesion , the cervical loops failed to surround the dental mesenchyme , and the growth angle was high ( Fig 6B bottom row ) ., In order to understand how differential adhesion affects the shaping of the tooth germ , we plotted the spatial patterns of mechanical stress at the level of cell-cell contacts ., We observed that there was a strong association between the orientation of the cervical loops and the spatial patterns of stress in the tooth germ ., In simulations where high mesenchymal homotypic adhesion led to downwards oriented cervical loops , there was tension along the surface of the follicular mesenchyme surrounding the tooth germ ( Fig 7A and 7B , left column ) ., When that adhesion was null and the cervical loops grew in the bucco-lingual direction , there was no such tension in the mesenchyme ( Fig 7C , left column ) ., We also observed tension in the suprabasal layer in simulations where high suprabasal homotypic adhesion led to bucco-lingually oriented cervical loops ( Fig 7A and 7C , right column ) ., We did not observe high tension or compression at the interface between the epithelium and the suprabasal layer , nor between the epithelium and the mesenchyme when the epithelial-suprabasal and epithelial-mesenchymal heterotypic adhesions were high ( S7 Fig ) ., Mouse molars are longer in the antero-posterior axis than in the bucco-lingual axis ., This has been associated with cervical loops in the buccal and lingual sides forming earlier and growing more downwards than the cervical loops in the anterior and posterior sides 7 , 9 ., In the model , we observed that the cervical loops form in the same fashion in most cases ( Fig 8A–8D ) , that is , also with an anterior-posterior versus bucco-lingual asymmetry ., In other words , the overall shape of the epithelium in 3D is similar between mouse and model ., For this to happen , however , the tooth germ in the initial condition needs to be at least slightly longer in anterior-posterior axis than wider in the bucco-lingual axis ( as it is the case in mouse tooth germs from very early on , 7 , 9 ) ., If we choose initial conditions with radial symmetry , cervical loops grow equally in all directions , a mode of development that would produce a single fang or canine shaped tooth ( Fig 8E–8H ) ., Results from the model like those shown in Fig 6B suggest that strong adhesion between epithelium and mesenchyme results in downward oriented cervical loops ., A prediction arising from the adhesion dynamics is that an experimental separation of the epithelium and the mesenchyme should lead to a fast increase of the cervical loop growth angle ., To test the model prediction , we performed an enzymatic separation of epithelium and mesenchyme on dissected E14 . 5 mouse first molars sliced in thick frontal sections ( roughly 400 μm ) ., Molar sections were submerged in dispase laying flat on the bottom of a glass dish so that one of the sliced surfaces was facing upwards ., Dispase digests extracellular matrix , including the basement membrane and , given enough time , the epithelium and mesenchyme will separate from one another ( S1 Video , also see 6 ) ., The results show that the follicular mesenchyme on one side of the tooth germ peeled off from the epithelium , and eventually the whole mesenchyme recoiled towards the other side of the tooth germ ( n = 6 ) ., At the same time , the cervical loops detached from the mesenchyme and rapidly changed their orientation , with the growth angle increasing to reach roughly 180° ( Fig 9A ) ., This reorientation of the cervical loops was unlikely to be due to growth since it occurred in less than 1 hour ., No recoil in the mesenchyme was observed when the same experiment was performed on E13 . 5 tooth germs ( bud stage , S8 Fig ) ., We then proceeded to reproduce the separation assay in silico ., Since the tissue deformations essentially took place within a two-dimensional plane , we used the 2D version of the model ., We first simulated tooth development until cap stage ( Fig 9B ) , then we set the epithelial-mesenchymal adhesion to 0 ., We resumed the simulation until the system was at equilibrium again ( i . e . no cell movement ) ( Fig 9C ) ., We repeated the same procedure with different combinations of adhesion parameters ( S9 Fig ) ., We observed in all cases that the mesenchyme buccal and lingual to the tooth germ retracted towards the mid line of the tooth germ and in most cases the growth angle increased ( Fig 9B and 9C , S9 Fig ) , in accordance with the experimental observations ( Fig 9A ) ., In order to quantify the model and experimental deformations , we tracked tissue deformation over time using specific morphological landmarks in the real tooth germs and in the model ( Fig 9A–9C ) , and performed a principal components analysis on shape data extracted from these biological landmarks ( real tooth germs n = 3 , model tooth germs n = 1 , Fig 9D , see Methods ) ., The first principal component ( PC ) explained 91 . 3% of the total variation as an increase of the growth angle of the cervical loops ( Fig 9D ) ., Furthermore , time evolution both ex vivo and in silico consistently correlated with an increase in PC1 ( Fig 9D , arrows ) ., By plotting the mechanical stresses over the tooth germ during the separation simulations we were able to obtain insights about the origins of the observed tissue deformations ., The follicular mesenchyme retraction coincided in time , in the model , with a decrease in the tension between its cells suggesting that this tension may be also responsible for the recoil in the follicular mesenchyme in the ex vivo experiments ( Fig 9B and 9C , S10 Fig ) ., The reorientation of the epithelium and suprabasal layers coincided with an increase in tension in the suprabasal cells and a relaxation of the compression of the epithelial cells ( Fig 9B and 9C , S10 Fig ) ., This reorientation coincided with the arising in the suprabasal layer of an arch of tension along the bucco-lingual axis ( Fig 9C , S10 Fig ) ., In other words , a large part of the tension is distributed along a line of contiguous cells going from the buccal to lingual side of the suprabasal layer ., By following the evolution of the mechanical stresses during the in silico separation , it can be seen that this arch was a result of the expansion of the epithelium that , by being mechanically attached to the suprabasal layer , pulled the suprabasal layer along the length of the cervical loops ., The separation of the epithelium and the mesenchyme allows the suprabasal layer to relax the tension in this arch ., As a result , the curvature and tension in this arch decrease and the cervical loops reorient in the bucco-lingual direction ( S10 Fig ) ., It has been argued that the compression of an epithelium ( along its plane ) will lead to its buckling ( out of plane folding ) ( reviewed in 13 ) ., The formation of small folds , or villi , in the small intestine , for example , has been proposed to occur by this mechanism 12 ., In our model , the expansion of the growing epithelium is restricted by both the mesenchyme and the suprabasal layer ., Suprabasal growth tends to push the epithelium outwards against the mesenchyme , but excessive growth will lead to a globular shape , reminiscent of an enlarged tooth bud ( e . g . S11C Fig , left most column ) ., On the contrary , as long as the suprabasal layer grows relatively slower than the epithelium , the latter will need to fold in order to keep contact with the former due to their heterotypic adhesion ( e . g . S11C Fig , left most column ) ., In other words , a buckled ( cap ) shape requires a higher surface ( i . e . epithelium ) to volume ( i . e . suprabasal layer ) ratio than a globular ( bud ) shape ., The mesenchyme acts as a barrier to the outwards expansion of the epithelium , both due to the accumulated tension on the external layers and the pressure of the growing mesenchyme underneath the enamel knot ., Even when high suprabasal growth is forcing the tooth germ into a globular shape , high mesenchymal growth can contribute to the epithelial folding by pushing the epithelium from underneath ( e . g . S11C Fig , top row ) ., For the same reason , high mesenchymal growth prevents epithelial buckling in the central part of the tooth germ and forces the epithelium to fold around the growing mesenchyme ( Fig 3D ) , a process which also depends on the epithelial-mesenchymal heterotypic adhesion ., Cervical loops , therefore , form only on the sides ( buccal , lingual , anterior and posterior ) of the dental mesenchyme ., Our model predicts that three different factors regulate the orientation of the cervical loops ., This asymmetry can be understood from the geometry of the initial conditions ( see S1 Appendix for more details ) ., The early tooth bud in vivo and in the initial conditions of the model are both longer in the antero-posterior ( AP ) axis than in the bucco-lingual ( BL ) axis ., That asymmetry was not observed when we simulated teeth whose initial conditions had radial symmetry ( i . e . initial conditions are as long in the AP than in the BL axis ) ( Fig 8E–8H ) ., Seen from below ( i . e . from the dental mesenchyme ) , the initial epithelium looks like an ellipsoid and , as such , it has a higher curvature in the AP sides than in the BL sides ( Fig 8A–8D ) , i . e . a higher ratio between surface and enclosed volume ( S12A Fig ) ., Because of that , the same amount of growth will lead to less elongation of the cervical loops in the AP than in the BL sides ., We have devised a simplified geometrical argument showing that the elongation rate of the BL loops would follow an exponential growth function with exponent proportional to t ( time ) , whereas the in the AP loops the exponent would be proportional to t/2 ( S12B Fig , see details of the argument in S1 Appendix ) ., The fact that this asymmetry was not so apparent when the mesenchymal homotypic adhesion or the epithelial-mesenchymal adhesion were very low is simply due to the fact that in these cases the cervical loops did not grow downwards anyway ., Our model , thus , makes apparent that even when the tissues grow at the same rate everywhere , the AP vs . BL asymmetry of the initial conditions would inevitably lead to deeper lateral cervical loops than anterior and posterior loops ., The large recoil observed in the follicular mesenchyme after the experimental separation suggests that there is a line of tension running from buccal to lingual side of the tooth germ ., A similar deformation was observed in the follicular mesenchyme in the in silico separation , although with less intensity ., The most visible deformation in the epithelium during the separation experiment is the reorientation of the cervical loops towards the bucco-lingual axis ., The same kind of reorientation is observed in the in silico experiment ., In the model , the reorientation of the cervical loops is due to the combination of two processes ., One is the expansion of the epithelium that was under compression before the separation , and the other is the relaxation of the tension in the suprabasal layer ., This latter tension is the result of the pull on the suprabasal layer by the expanding epithelium ( as explained above ) ., This latter tension is roughly distributed as an arch that will tend to flatten after the separation , thus reorienting the cervical loops in the bucco-lingual direction ( S10 Fig ) ., The combination of compression in the epithelium and tension in the suprabasal layer may also account for the epithelial deformation observed in the ex vivo separation ., Our experiments , however , were not able to discern between stresses in the epithelium and the suprabasal layer ., In a recent study 22 , a different kind of mechanical perturbation was performed at the placode stage of tooth ( E12 . 5 ) , i . e . before the tooth bud forms ., Using thick frontal slices of tooth germs , Panouspopoulou and Green performed a cut in the oral epithelium at one side of the placode and observed that the epithelium by the side of the placode recoiled towards the mid line ., They also interpreted this recoil as a consequence of a bucco-lingually oriented tension , in this case located in the suprabasal layer of the tooth placode ., Along these lines we have observed that , when the separation experiment is performed on bud stage tooth germs ( E13 . 5 ) the mesenchyme did not recoil ( S8 Fig ) , indicating that the tension in the mesenchyme may build up between the bud and cap stages , as our model predicts ( S13 Fig ) ., In this study we have explored , theoretically and experimentally , the role differential growth and adhesion on the transition from the bud stage , common to several ectodermal organs , to the specific cap shape of the early tooth germ ., Our model simulates the formation of the tooth germ by combining the aforementioned processes , and accounting for cell and tissue mechanical interactions that result in tooth specific shape transformations ., Even though our model accounts only for early tooth morphogenesis , it does so by implementing a set of cell and mechanical processes common to the ensemble of ectodermal organs ., Thus , the dynamics produced by this model leading to early tooth-specific shapes ( or failing to do so ) , may shed light on how other ectodermal organs undergo their specific transformations after bud stage ., For instance , some of the model dynamics may also apply to the formation of epithelial folds surrounding a mesenchymal condensate in the hair follicle after its bud stage 23 ., In a homologous fashion , the formation of these folds may be a result of increased epithelial growth relative to suprabasal growth , whereas a high epithelial-mesenchymal adhesion may account for the surrounding of the mesenchyme by these folds ., In another type of ectodermal organs , such as mammary , salivary glands and lungs , the epithelium folds in order to form branched structures , but never surrounds the mesenchyme 24 ., Ex vivo and in silico studies of mouse lung epithelium suggest that mechanical buckling of the epithelium due to intrinsic growth plus mechanical interactions with the surrounding extracellular matrix is sufficient to account for branch formation 25 ., In addition , it has been shown that the lung mesenchyme also contributes to epithelial folding by mechanically constricting the lung epithelium during branch formation 26 ., Future studies should address whether differential tissue growth and mechanical interactions between epithelium and mesenchyme observed in the development of different ectodermal organs are regulated by a conserved developmental mechanism ., Our model does not consider active cell migration or cell contraction ( although EmbryoMaker can implement these cell processes ) ., Active cell migration over an extracellular matrix substrate and cell intercalation have been shown to generate tissue-scale mechanical stresses 27 , 28 ., It has been shown that mesenchymal cells at the early bud stage actively migrate towards the source of an FGF8 gradient in the bud epithelium 29 ., It has also been shown at the tooth placode stage that perturbation of the Shh pathway altered the width and depth of the placode , suggesting the presence of cell intercalation in the suprabasal layer 22 ., Morita and collaborators also argued that active migration mediated by high F-actin turnover and the LIMK-cofilin pathway is present in the growing regions of the tooth germ epithelium 6 ., Even though we acknowledge that active cell migration and cell intercalation may have a role in tooth development and , perhaps , would improve our model if included , we show that several features of tooth development related to tissue growth , cell movement , and tissue mechanics can already be explained by considering only passive cell movement resulting from cell adhesion and proliferation ., Our new mathematical model of tooth development provides detailed quantitative explanations on how biomechanical processes may drive tooth germ morphology to change the specific way it does in 3D space and over developmental time ., This included explanations on how morphology will change in specific ways when these biomechanical processes are altered and , thus , understanding not only the wild-type but also its variational properties ., To our knowledge no such explanations have been provided for any ectodermal organ , although they are well studied in other aspects of their development ., In spite of the increase in complexity of tooth germ morphology during development , two biomechanical processes seem enough to explain it ., This result highlights how the combination of experimental results with computational models of biomechanical processes can help providing relatively simple explanations for seemingly complex processes such as the development of morphology and its variation ., All animal work was conducted accordingly to the guidelines required by the Finnish authorities ( ESAVI-2984-04 . 10 . 07–2014 , KEK13-020 ) ., Mouse specimens were sacrificed by anaesthetising with CO2 first followed by cervical dislocation ., The following section describes the basics of EmbryoMaker and how we build the tooth-specific model based on it ( see 15 for a more extensive description ) ., Mesenchymal and suprabasal cells are made of spherical bodies , that we call nodes , whereas epithelial cells are made of cylindrical bodies consisting of two nodes ( one basal and one apical bound by an elastic link ) ( S1A and S1B Fig ) ., The movement of nodes follows an overdamped Langevin equation of motion ,, ∂r→i∂t=∑j=1j=nvfAiju^ij, ( 1 ), where ri is the position vector of node i , nv is the number of nodes in the neighborhood of node i , t is time , fAij is the modulus of the force acting between node i and j and uij is the unit vector connecting i and j ( see S1A–S1D Fig ) ., The modulus and sign of the force is dependent on the distance between the two nodes ,, {fAij= ( piREC+pjREC ) ( dij− ( piEQD+pjEQD ) ) ifdij< ( piEQD+pjEQD ) fAij=kijADH ( dij− ( piEQD+pjEQD ) ) if ( piEQD+pjEQD ) ≤dij≤ ( piADD+pjADD ) fAij=0if ( piADD+pjADD ) >dij, ( 2 ), When the distance between nodes i and j ( dij ) is shorter than the sum of their radii at equilibrium ( pEQD ) , there is a repulsive force proportional to the sum of the pREC of each node ( this coefficient determines their incompressibility ) ., When this distance is longer than the equilibrium distance but shorter than the sum of the maximum radii of i and j ( pADD ) , there is an attractive force between nodes i and, j . This force is proportional to kijADH ,, kijADH=gimgjnbmn, ( 3 ), where gim is the amount of adhesion molecule m expressed in node i and bmn is the adh

Introduction, Results, Discussion, Materials and methods

From gastrulation to late organogenesis animal development involves many genetic and bio-mechanical interactions between epithelial and mesenchymal tissues ., Ectodermal organs , such as hairs , feathers and teeth are well studied examples of organs whose development is based on epithelial-mesenchymal interactions ., These develop from a similar primordium through an epithelial folding and its interaction with the mesenchyme ., Despite extensive knowledge on the molecular pathways involved , little is known about the role of bio-mechanical processes in the morphogenesis of these organs ., We propose a simple computational model for the biomechanics of one such organ , the tooth , and contrast its predictions against cell-tracking experiments , mechanical relaxation experiments and the observed tooth shape changes over developmental time ., We found that two biomechanical processes , differential tissue growth and differential cell adhesion , were enough , in the model , for the development of the 3D morphology of the early tooth germ ., This was largely determined by the length and direction of growth of the cervical loops , lateral folds of the enamel epithelium ., The formation of these cervical loops was found to require accelerated epithelial growth relative to other tissues and their direction of growth depended on specific differential adhesion between the three tooth tissues ., These two processes and geometrical constraints in early tooth bud also explained the shape asymmetry between the lateral cervical loops and those forming in the anterior and posterior of the tooth ., By performing mechanical perturbations ex vivo and in silico we inferred the distribution and direction of tensile stresses in the mesenchyme that restricted cervical loop lateral growth and forced them to grow downwards ., Overall our study suggests detailed quantitative explanations for how bio-mechanical processes lead to specific morphological 3D changes over developmental time .

The genes and signalling pathways involved in ectodermal organ development ( teeth , hair , mammary glands , etc . ) are relatively well studied ., However , the bio-mechanical processes by which these organs grow and change shape from an early primordium ( an epithelial bud similar among different organs ) is far less understood , especially in mammalian development ., This study combines simple experiments and a multi-scale , cell-based computational model to understand these processes for one ectodermal organ: the tooth ., Our model implements the different mechanical properties of the different cell types involved in morphogenesis ( i . e . epithelial and mesencymal ) and their interactions ., By exploring model behaviour and contrasting it with experimental data on tissue growth rates and mechanics we found that , in spite of their relative complexity , the shape changes occurring in early tooth development , the overall sharpness of the tooth and its variation can largely be explained by the two most simple bio-mechanical processes: differential growth and differential adhesion between tooth tissues ., These results suggest that simple mechanical interactions between cells and tissues may underlie the complex tissue deformations observed in the morphogenesis of several ectodermal organs .

medicine and health sciences, classical mechanics, cell cycle and cell division, cell processes, mechanical stress, epithelial cells, damage mechanics, digestive system, dentition, animal cells, deformation, biological tissue, head, physics, teeth, anatomy, cell biology, physiology, jaw, epithelium, biology and life sciences, cellular types, physical sciences, digestive physiology, molars

journal.pcbi.1005052

A Model of the Spatio-temporal Dynamics of Drosophila Eye Disc Development

During early development most tissues undergo fast changes involving cell growth , proliferation , differentiation and patterning ., In order to develop into a functional tissue or organ , both patterning and growth have to be tightly controlled and coordinated ., How this regulation is achieved is an extraordinarily complex problem ., As is the case with many fundamental mechanisms , also the interplay between growth and patterning has been most widely investigated in Drosophila 1–4 ., One tissue of particular interest in Drosophila is the eye imaginal primordium ( commonly called eye “imaginal disc” ) , that develops into the highly organized compound eye of adult flies ., During the two first larval stages ( or “instars” ) the eye disc comprises undifferentiated , proliferative eye progenitors ., Pattern formation in the eye disc starts during early third instar with the appearance of the morphogenetic furrow ( MF ) , a straight epithelial indentation that runs along the dorsoventral ( DV ) axis of the Drosophila eye disc and that emerges at the posterior margin of the disc 5 ., The MF is a moving signaling centre that separates the proliferating ( anterior to the MF ) and the differentiating ( posterior to the MF ) zones in the disc ( Fig 1A ) ., As the MF sweeps across the disc from its posterior to its anterior side , undifferentiated proliferating cells cease to proliferate at the MF and start differentiating into retina cells behind it 5 ., The velocity of MF movement thus determines the time for which cells on the anterior side of the disc can proliferate ., At a molecular level , many signals that are involved in the initiation and progression of the morphogenetic furrow have been described 6 ., Initiation of MF movement requires the production of the diffusible morphogen Hedgehog ( Hh ) at the posterior eye disc margin ., Hh is known to induce the expression of decapentaplegic ( dpp ) in the MF , another morphogen of the BMP2/4 type , and to promote differentiation of the proliferating cells into retinal cells ., Furthermore , it is known that both Hh and Dpp signaling lead to the downregulation of the transcription factor homothorax ( hth ) 7 ., hth is expressed on the anterior side of the eye disc and was shown to promote cell proliferation and to block the expression of later-acting transcription factors 7 , 8 ., Behind the MF , the newly differentiated photoreceptor cells express hh and the delayed mutual activating loop between Hh and Dpp is able to set the MF in motion ., Recent measurements describe this movement of the MF as linear in time 9 ., However , it remains unclear how the signaling network determines the speed of the MF ., By driving the progression of the MF , the signaling network is indirectly regulating proliferation ., In addition to driving MF movement and cell differentiation , Dpp signaling affects growth 10–12 ., Recently , it was reported that the gradients of the Dpp signaling targets pMad and hairy scale with the anterior length of the eye disc , and the authors suggested that the relative temporal changes in the concentration of the moving Dpp gradient control the proliferation rate and are therefore responsible for the control of growth and its termination 9 ., However , the authors also note that the growth rate is not altered when the only Dpp signalling mediator mad and its downstream target brk are removed from cells ., In order to explore how the spatio-temporal signaling patterns affect the movement of the MF and impact on eye disc growth we translated the signaling network into a spatio-temporal model ( Fig 1A ) ., Patterning and growth are intricately linked during eye disc development , and we therefore solved the model on a growing domain ., As in previous models of imaginal disc growth 13 , we modeled the epithelium as an incompressible Newtonian fluid with a source that reflects cell proliferation ., In order to parameterize the model we measured two key parameters of the model , the degradation rate of Hth as well as the diffusion coefficient of Hh , by Fluorescent Recovery After Photobleaching ( FRAP ) ., We show that our model can reproduce the linear movement and speed of the MF ., Our model shows the observed growth termination and can reproduce several mutant phenotypes that influence the speed of the MF ., We furthermore analyze the impact of parameter perturbations on the linearity and speed of the MF as well as on the final size of the eye disc ., Importantly , the model fails to reproduce the scaling of the Dpp gradient with the anterior length of the tissue , suggesting that there must be additional mechanisms in place to ensure scaling ., While many open questions remain , our model serves as an important step towards an integrated model for patterning and growth control during development ., We aimed at developing a parsimonious model for eye disc growth and early patterning and thus sought to keep the regulatory interactions as simple as possible while reproducing the measurements ., As components of the model we will consider Hh , Dpp , pMad ( the active form of Mad which transduces the Dpp signal to the nucleus 14 ) , Eya , a gene expressed and required for retinal specification and differentiation 15 and Hth , a protein that prevents premature differentiation anterior to the MF ( Fig 1A ) ., Representative confocal sections of similar late third instar eye-antennal discs stained for several proteins that are incorporated in the model are shown from different views in Fig 1B–1D ., We will focus on the differentiation process beginning with the initiation of the morphogenetic furrow ( MF ) in larvae during early third instar ., In front of the MF , progenitor cells proliferate ( Fig 1A; arrow ( A ) 1 ) , while behind the MF cells differentiate and eventually form the ommatidia ., Experiments show that hh is expressed at the posterior margin before MF initiation and in differentiated cells , labeled Φ , posterior to the MF during disc development 16 ( Fig 1A; A2 ) ., The production in the margin is incorporated via the boundary condition for Hh ( Eq 19 , see Methods ) ., Given the lack of evidence for any relevant regulation of the production of hh by other members in the model , we assume a constant production rate , restricted to the differentiated cells , thus writing pHh ⋅ Φ ., Hh induces the expression of dpp in MF cells , labeled Θ 17 ( Fig 1A; A3 ) ., In order to keep the number of parameters small , we use the simplest function that allows us to fit the data , a linear relationship ., Accordingly we write pDpp ⋅ cHh ⋅ Θ as a production term , with cHh being Hh concentration ., We note that also a Hill function would have allowed us to reproduce the observed data ., However , this would have introduced two additional parameters ., Dpp signaling is mediated by phosphorylation of Mad to pMad 14 ( Fig 1A; A4 ) ., The rate of Mad phosphorylation thus depends on the Dpp concentration , and we have ppMad ⋅ σDpp as a production term for pMad ( see definition for σ in Eq 3 ) ., Expression of eya is enhanced by pMad-mediated Dpp signaling 18 ( Fig 1A; A5 ) ., As the expression of eya is also induced by Hh 19 we incorporated a Hh-dependent term ( Fig 1A; A6 ) and a pMad-dependent production term , such that the presence of either pMad or Hh is sufficient to induce eya expression , i . e . pEya ⋅ ( σpMad + σHh ) ., We notice that we could also have reproduced the mutant behavior if we substituted the Hh-dependent term by a positive feedback of eya on itself or by a direct link of the photoreceptor cells ., hth can be expressed in all cells , but is repressed by pMad-mediated Dpp signaling ( Fig 1A; A4 , A7 ) , and by Hh signaling ( Fig 1A; A8 ) 20 , 21 ., We therefore describe Hth production by pHth⋅σ¯pMad⋅σ¯Hh , such that the presence of either pMad or Hh is sufficient to repress hth expression ( compare Eq 3 ) ., Hth is required to maintain the progenitor population in a proliferative and undifferentiated state ( Fig 1A; A9 ) 7 , while Hh is required for the proper differentiation of cells behind the MF into photoreceptor cells ( Fig 1A; A10 ) ., Furthermore , forced maintenance of Hth is known to cause severe delays in MF movement and blocks retinal differentiation 22 , 23 ., Downregulation of hth expression therefore allows MF movement 20 , 22 , 23 ., The simplest way to incorporate these known concentration-dependent cell type transitions is to introduce an Hth concentration threshold , ΘHth , below which proliferating cells are becoming MF cells and an Hh concentration threshold , ΘHth , above which MF cells are differentiating into differentiated cells ( see also Eq 4 ) ., All extracellular molecules can diffuse within the domain , albeit at different speeds ., We therefore formulate the model as advection-reaction-dispersion equations for a component i with concentration ci , diffusion coefficient Di and reaction terms Ri ., The external velocity field is denoted by u:, ∂ci∂t+∇ ( uci ) =Di∇2ci+Ri, ( 1 ), The reaction terms Ri of the components describe the regulatory interactions based on information from the literature and our own experiments as discussed above , and are given by:, RHh=pHh⋅Φ−δHh⋅cHh, ( 2 ), RDpp=pDpp⋅cHh⋅Θ−δDpp⋅cDpp, RpMad=ppMad⋅σDpp−δpMad⋅cpMad, REya=pEya⋅ ( σpMad+σHh ) −δEya⋅cEya, RHth=pHth⋅σ¯pMad⋅σ¯Hh−δHth⋅cHth, In the absence of contrary data we use the simplest model for decay , linear decay at rate δi ⋅ ci for all signaling factors i ., We use Hill functions to describe regulatory influences ., To describe activating influences of a component i we write, σi=cinicini+Kini, ( 3 ), and we use σ¯i=1−σi to describe inhibitory impacts of ci ., Ki is the Hill constant which specifies the concentration of ci where half-maximal activity is observed , and the Hill coefficient ni defines the steepness of the response ., The different cell types , i . e . differentiated cells Φ , cells in the MF Θ and proliferating cells Π , are defined as, Π=H ( cHth−θHth ), ( 4 ), Θ= ( 1−H ( cHth−θHth ) ) ⋅ ( 1−H ( cHh−θHh ) ), Φ= ( 1−H ( cHth−θHth ) ) ⋅H ( cHh−θHh ), where H ( ci − θi ) for component i and threshold θi is the Heaviside function , which is defined according to:, H ( ci−θi ) ={0ifci≤θi1ifci>θi, ( 5 ), Finally , we need to define the boundary and initial conditions ., Hh is expressed in the margin from where it diffuses into the eye disc ., Accordingly , we use as boundary condition for Hh, DHh∇cHh=η⋅Λ ( x ) ⋅τ ( t ), ( 6 ), where η is a constant , Λ ( x ) defines the spatial location of the Hh producing margin and τ ( t ) a time-dependent function ( see Methods for details ) ., We use zero flux boundary conditions for all other signaling molecules , transcription factors and cell types , i . e . We use zero initial conditions for Hh , Dpp , pMad , and Eya in the entire eye disc domain; MF initiation happens in response to the influx of Hh at the posterior margin ( see Methods ) ., Before the initiation of the MF , hth is expressed in all cells of the eye primordium 23 ., We therefore use the steady state concentration as initial concentration , i . e . cHth ( 0 ) =pδHth ., The presence of Hth prevents premature cell differentiation ., In summary , the initial conditions are:, cHth ( 0 ) =pδHth, ( 8 ), cHh ( 0 ) =cDpp ( 0 ) =cpMad ( 0 ) =cEya ( 0 ) =0, As previously established , the eye disc can be approximated by a 2D ellipse ( Fig 1B and 1C ) 24 ., On long time scales , embryonic tissue can often be described by a viscous fluid 25 , 26 ., Accordingly , we model the mechanical behaviour of the eye disc as an incompressible Newtonian fluid with density ρ , dynamic viscosity μ and local source S . This approach has previously been used in simulations of early vertebrate limb development 27 and , in an extended anisotropic formulation , to Drosophila imaginal disc development 13 ., The Navier-Stokes equation is given as:, ρ ( ∂u∂t+ ( ∇⋅u ) u ) =−∇ρ+μ ( ∇2u+13∇ ( ∇⋅u ) ), ( 9 ), ∇⋅u=S=Π⋅k0⋅exp ( −δPL⋅PL ), where u denotes the external velocity field used in Eq 1 , S , from definition above , denotes the local growth rate and PL denotes the posterior length , i . e . the length from the posterior margin to the MF ( corresponding to the differentiating region ) which is a good surrogate of developmental time given the linear progression of the MF with time 9 , 24 ., k0 is the initial area growth rate and has been previously estimated from experimental data 24 ., We assume that growth is caused by proliferation of undifferentiated cells only ( Fig 1A; A1 ) and we have previously found that the measured growth rate can be described well by a function that declines exponentially with PL , with δPL = 0 . 0107 μm-1 24 ., Different mechanisms could , in principle , give rise to this measured decline 24 ., However , in the absence of a confirmed growth-controlling mechanism , we decided to use the functional relation that most accurately describes the growth dynamics in the Drosophila eye disc 24 without making any statement about how growth may be regulated mechanistically ., We also assume the same growth rule in both x and y directions ., This is in agreement with experimental observations: the growth anisotropy parameter was previously determined as ϵ=∂yu∂xu≈1 9 ., In most mathematical models the parametrization is crucial for its capabilities to correctly reflect the modeled phenomena ., In our model we have three classes of parameters: Production rates and coefficients of the Hill terms , diffusion coefficients and degradation rates ., As the absolute protein concentrations are unknown , the production rates can be set to arbitrary values and the Hill coefficients must then be adjusted to reproduce the experimentally observed protein gradients and gene expression boundaries ., We used quantitative confocal microscopy of stained eye discs to detect the spatio-temporal dynamics of the core proteins pMad , Eya , and Hth ., Fig 1B–1D shows representative confocal sections of a late third instar eye-antennal disc ., For profile quantification , z-stacks of disc strips were acquired ., A single ( x , y ) confocal section of one of these strips is shown ( Fig 1B and 1C ) ., Fig 1D shows a magnified section of a similar eye disc ( top ) and a z-section through that section ( bottom ) ., pMad or merged channels ( Hth: green , Eya: red; pMad: white ) are shown ., As the Dpp-producing MF moves , the concentration profiles of pMad , Eya , and Hth also move towards the anterior side ., For easier comparison of the concentration profiles we plot these relative to the MF ( S1A–S1C Fig ) ., The regulatory concentration thresholds could be adjusted such that the model reproduced the shapes of the concentration profiles of pMad , Eya , and Hth ( S1A–S1C Fig , red lines ) ., In this way , all production rates and Hill coefficients could be determined ( S1 Table ) ., With respect to the diffusion coefficients , we note that Hth , Eya , and pMad are intracellular proteins and therefore their diffusion across the tissue is negligible ., In the simulations we could reflect this by setting D = 0 for all three species ., However , we opted for a very low effective diffusion coefficient for numerical stability ( D = 0 . 00025 μm2 s-1 ) , which may also reflect intracellular diffusion ., Regarding Dpp kinetic parameters , there are no experimental measures performed in the eye disc ., However , in the wing disc , different groups have measured distinct properties regarding Dpp transport , reporting values for Dpp free extracellular diffusion 28 , effective diffusion coefficient 28 , 29 and the length of the Dpp gradient 29 ., As our model considers effective parameters we choose for the Dpp diffusion constant DDpp = 0 . 1 μm2 s-1 and , based on the Dpp gradient length of the wing disc , deduced the Dpp degradation rate as δDpp = 2 . 5 × 10−4 s-1 29 ., This Dpp decay rate would correspond to a half-life of 45 min ., We note that subsequent measurements showed that the Dpp gradient lengthens over time in the wing disc 4 ., Based on this observation , it has been proposed that the Dpp degradation rate would decline over time 4 ., However , we have since shown that the data can be fully explained with a constant degradation rate if the dynamic pre-steady state nature of the patterning process is taken into account 30 ., While we estimated the Dpp half-life to be longer than 10 hours in the wing imaginal disc 30 , we are here focused on the Dpp removal rate from the extracellular space , and the rate of Dpp internalization is fast ., Therefore , we will use as degradation constant δDpp = 0 . 00025 s-1 ., Three crucial parameters have not been previously measured: the diffusion coefficient and the degradation rate of Hh and the degradation rate of Hth ., Since the characteristic length of the Hh gradient has been determined , we focused on measuring the effective Hh diffusion coefficient and the Hth degradation rate ., Both experiments were performed using FRAP and were obtained in the wing disc , as this disc is larger and flatter than the eye disc , thus facilitating the experiments and assuming the same dynamics in both disc types ., Fig 2 shows the FRAP experiment for determining the Hth degradation rate ., The degradation rate can be calculated by linearly fitting the time series for the bleach-chase analysis of Hth ( Fig 2B ) 31 ., The slope of the fitted line yields the degradation rate , δHth = ( 6 . 97 ± 5 . 00 ) 10−5 s-1 , which corresponds to a Hth protein half-life of τ1/2 = 2 . 77 h ( see details in Materials ) ., In order to determine the Hh effective diffusion coefficient , wing discs were dissected from larvae in which UAS-GFP:Hh 32 was driven in the hh-expression domain by a hh-GAL4 driver ., In the FRAP experiment , the region of interest ( ROI ) ( solid circle in Fig 3A ) was photobleached and the recovery was observed ( Fig 3B–3E ) ., The bleaching does not only happen in the ROI , but also in the adjacent area ( Fig 3F ) ., From the FRAP profile we calculated the mean half recovery time τ1/2 = 7 . 12 min ( Fig 3G ) ., This corresponds to a mean diffusion coefficient of Hh of DHh = 0 . 033 ± 0 . 006 μm2 s-1 ( see details in Materials ) ., This value is similar to previous measurements for Wg in the wing disc 29 , and Wg and Hh have previously been noticed to bear important similarities in their extracellular transport in the wing disc 33 ., The characteristic length for Hh has been determined in the wing disc as 7 μm 34 ., From the diffusion constant DHh = 0 . 033 μm2 s-1 that we determined , the Hh decay rate then follows as approximately δHh = 6 . 7 × 10−4 s-1 ., We note that the Hh gradient has been shown to be dynamic during wing disc and ocellar complex development 35 , 36 , something that we will ignore in this model as the effect can be expected to be minor for our model predictions , and it would require a major complication of the model as the Hh’s receptor Patched ( Ptc ) would need to be included explicitly ., In agreement with previous measurements 9 , the MF progresses linearly with time from the posterior towards the anterior side of the domain ( Fig 4A ) ., Moreover , the speed of MF progression agrees quantitatively ., Here , we note that the experimental measurements report the MF position as the average posterior length at a given time point across the disc , while we monitor the MF position as the maximal posterior length at the dorso-ventral boundary to be able to use the previously determined eye disc growth rate in Eq 9 9 ., We have previously shown that the experimentally determined speed of 3 . 1 μm h-1 9 then corresponds to 3 . 4 μm h-1 24 , as observed in our numerical simulations of the eye disc model ., The model also reproduces the observed growth dynamics ( Fig 4B–4D ) ., Thus , our simulations show an initial linear increase of the total area followed by a plateau phase ( Fig 4B ) ., During the plateau phase the anterior area is barely increasing and finally declines in parallel and at a similar rate as the posterior area is increasing due to the differentiation caused by the progression of the MF ( Fig 4B–4D ) ., As a result of this , the MF reaches at some point the anterior end of the eye disc which leads to growth termination due to the exhaustion of anterior progenitors ., In our numerical simulations , the nonlinearity and speed of the MF movement ( measured by the root-mean square error and the slope of a linear fit of the posterior length over time ) as well as the final eye disc size heavily depend on the choice of parameters ., In order to quantify the effect of parameter changes in the model we increased ( Fig 5 , red boxes ) or decreased ( Fig 5 , blue boxes ) single parameters by 1% ., An increased impact of Hh , either achieved by a higher production rate pHh , a lower degradation rate δHh or a lower concentration threshold for differentiation θHh , clearly has the strongest effect in speeding up the MF ( Fig 5A and 5B ) ., At the same time , an increased impact of Hh also increased the linearity of the MF movement ( Fig 5C and 5D ) ., As a result of the increased MF speed , the anterior tissue has less time to proliferate and therefore the final eye disc area is smaller ( Fig 5E and 5F ) ., In contrast to this , an increased impact of Hth ( high production rate pHth , low degradation rate δHth or lower concentration threshold for differentiation θHth ) decreases the MF speed and increases its linearity ( Fig 5A–5D ) ., As a result of the slower MF movement the anterior area has more time to proliferate and therefore the final total area increases ( Fig 5E and 5F ) ., An increased impact of both Dpp and pMad has generally a smaller effect but nevertheless leads to an increase in the MF speed and in the nonlinearity of the MF movement and to a decrease in the final area ( Fig 5 ) ., Experiments show that the speed of the MF and/or the size of the adult eye are severely affected in Hh , Dpp and Hth mutants ., We were therefore interested to see to what extent our eye disc model is able to explain these observed effects ., Mutations that reduce Hh activity in the eye disc result in a severe slowdown or stop of MF progression and eye size reduction 17 , 37–39 ., In our simulations , reduction of the Hh production rate indeed results in a decreasing speed and an eventual stop of MF movement ( Fig 6A ) ., Furthermore we observe that the predicted total area of the eye disc overgrows ( Fig 6B ) , because the differentiation rate of the proliferating area caused by the furrow is much smaller ., This excess of undifferentiated progenitors do not make into the adult head , as hh mutants show smaller eyes but no abnormal overgrowths ., This suggests that there must be some additional control of the anterior area that is not included in the eye disc model , e . g . downregulation of the growth rate or increased cell death in the absence of Hh signaling ., The latter is supported by the observation that there is abundant cell death in hh-mutant discs anterior to the MF 16 , 17 ., Furthermore , it was shown that large hh signaling-mutant clones in the eye disc showed a disrupted organization of photoreceptors towards the center of the clone 17 ., Interestingly , we can see that also in simulations of these clones the Hh concentration in the center is not sufficient to differentiate these cells ( Fig 6C ) ., It is well known that Hh is required for the initiation of the MF , and removal of hh expression in the margin thus prevents MF initiation and eye formation 40 , 41 ., Complete removal of Hh signaling in the simulations precludes production of Dpp and since hth can no longer be downregulated , no MF starts ( Fig 6D , zero influx ) ., On the other hand , a small reduction in the Hh influx compared to wild type increases eye size because cells have more time to proliferate before the MF is initiated ( Fig 6D ) ., Similar to the phenotype of discs with hypomorphic hh alleles , eye discs harboring a hypomorphic dpp allele have very small adult eyes ( dpp blk: 100–200 ommatidia develop instead of 700–800 in the wild type ) 37 ., In contrast to this , our simulations predict an overgrowth of the total area ( Fig 6E , red line ) ., Again this is because in our simulations the MF moves more slowly ( Fig 6F ) , thus leaving more time to anterior cells to proliferate , which finally results in larger eyes ., The predicted effect of dpp on furrow movement is supported by experiments that show that the MF slows down in clones mutant for the dpp signal transduction pathway 42–44 ., The difference in the final size of the eye disc between model and experimental data can be explained by the fact that we did not include any effect of dpp on cell survival in the model and therefore it is not able to capture the experimentally observed excessive cell death in the ventral regions of the mutant eye discs 37 ., Experiments show that hth-overexpressing clones slow down MF movement , something that we can also observe in our numerical simulations ( Fig 6G ) ., It has also been shown that downregulation of Hth levels mediated through the expression of an RNAi construct in the eye primordium ( using an eyeless driver ) leads to a reduction in the adult eye size 20 ., In our simulations , a decrease of the Hth production rate indeed leads to smaller eyes ( Fig 6H , red line ) ., However , we observe overgrowth in the total eye area if we increase the hth production rate , because proliferating cells are delayed in differentiating ( Fig 6H , blue line ) ., Eye overgrowth in response to hth overexpression has not been experimentally observed and therefore this result might suggest that a fundamental link between patterning and growth , such as increased cell death in the progenitor population in mutant eye discs , is not included in the model ., Furthermore , it is known that excessive Hth , which is not bound to the protein Extradenticle ( Exd ) , is degraded 45 ., Therefore , contrary to the assumption of the model , the overexpression of Hth might not have an impact on the total Hth concentration if the steady state Hth concentration is close to its maximum ., Recently , it was reported that the gradients of the Dpp targets pMad and hairy scale with the length of the anterior domain 9 ., As the anterior length is initially increasing and then decreasing , the Dpp gradient would have to initially expand and later retract ., In order to compare this observation with the results of our model we normalized the simulated Dpp and pMad gradients from three time points with respect to the anterior domain length ., Indeed the relative profiles at 20 hours and 40 hours overlapped almost perfectly , while the relative profile at 60 hours was expanded ( Fig 7A–7C ) ., However , in our model , the overlap is not a result of an adaptation of the gradient to the anterior length , but the result of a constant decay length of the gradient and very similar anterior domain length at 20 and 40 hours , such that a constant gradient also overlaps after normalization ( Fig 7C ) ., At 60 hours the anterior length is much shorter and the gradient therefore appears more expanded ( Fig 7C ) ., This cannot be explained by a change in the amplitude , since for both Dpp as well as pMad the maximal concentration is relatively stable over time in the simulations ( Fig 7A and 7B ) ., We conclude that our model cannot explain the observed scaling of the pMad and hairy gradients ., How growth and differentiation are integrated to ensure the proper development of tissues is still an open question ., Here we developed a model for the Drosophila eye imaginal disc on a 2D growing domain that is based on a simple reaction network ., We measured the two key unknown parameters , the Hh diffusion coefficient and the Hth degradation rate ., We then investigated the behavior of our model and found that it can reproduce the initiation and linear progression of the MF ., The linearity and speed of the MF movement are an important property for growth control because they determine the time for which progenitor cells on the anterior domain of the eye disc can proliferate ., Interestingly , our model can qualitatively explain mutant phenotypes that are linked to changes in MF speed , which suggests that a very simple reaction network is sufficient to control and orchestrate the movement of the morphogenetic furrow ., The reason for this is that the system is irreversibly bistable and that Hh and Dpp can diffuse ., Both properties taken together generate a travelling wave , which in this case can be observed as the MF ., However , we can also see that our model cannot explain some of the mutant phenotypes linked to the final eye size , which suggests that the differentiation wave caused by the movement of the MF is insufficient to explain growth control in the eye disc and that there must be additional layers of regulation between patterning and growth ., One such layer of control might be the compensatory increase in cell death in response to a lack of differentiation or proper MF progression ., It was shown that dppd-blk discs , in which dpp production at the MF is lacking and the MF halts , show an increment of cell death in the ventral half of the eye disc 37 ., The same effect is not limited to dpp , but can also be observed in hth-overexpressing discs and hh mutant discs ., However , this effect seems to be limited to situations where the whole disc is mutant and is not observed in mutant clones , suggesting that Hh and Dpp might act as survival factors and are crucial in determining the probability of cell death in the progenitor cells ., In fact , it has been described that Dpp is used as a survival signal in the wing disc 46 ., The Dpp gradient has been suggested as an additional level of control ., In particular , it has been proposed that the relative temporal change in the concentration levels of Dpp has a direct influence on growth and ensures non-uniform growth and growth termination in the eye disc 9 , and uniform growth and growth termination in the wing disc 9 , 37 , 47 ., However , the relevance of Dpp on Drosophila wing disc growth is currently being debated ., Dpp has been reported to have no major impact on growth in the lateral regions of the Drosophila wing disc 48 , 49 , and recent results even suggest that Dpp has a minor impact on wing disc growth during third instar 50 ., Finally , we investigated the scaling of the Dpp gradient in the anterior side ., In contrast to experimental reports 9 , we do not observe scaling in our model ., Scaling in the eye disc is particularly interesting because at later stages the anterior length ( i . e . the width of remaining progenitor area ) is decreasing and the gradient would therefore have to shrink in order to scale ., Such shrinkage cannot be explained by previous scaling models from the wing imaginal disc 4 , 30 , 51 ., A novel scaling mechanism would thus need to be in place that ensures scaling of the Dpp gradient in the eye disc ., To define such scaling mechanism , it will be important to also obtain quantitative measurements of the Dpp gradient itself , in particular in late stages of eye disc development when the Dpp gradient is supposed to shorten ., Our model for the Drosophila eye disc is a first step on the way to mechanistically understand the interplay between patterning and growth during development ., Our model explains many key observations but also highlights important gaps in our understanding ., In particular , our results show that our model is currently lacking important regulatory cues on both cell survival and cell proliferation ., Additionally , the model mainly focuses on the major players along the anterior-posterior axis and neglects some factors acting along the DV axis , such as the prominent antagonist of eye differentiation , the Drosophila WNT-1 homologue Wingless ( Wg ) ., Wg is produced from the anterior dorsal and ventral margins of the disc to restrict MF initiation at the posterior margin 38 ., It is known that

Introduction, Results, Discussion, Methods

Patterning and growth are linked during early development and have to be tightly controlled to result in a functional tissue or organ ., During the development of the Drosophila eye , this linkage is particularly clear: the growth of the eye primordium mainly results from proliferating cells ahead of the morphogenetic furrow ( MF ) , a moving signaling wave that sweeps across the tissue from the posterior to the anterior side , that induces proliferating cells anterior to it to differentiate and become cell cycle quiescent in its wake ., Therefore , final eye disc size depends on the proliferation rate of undifferentiated cells and on the speed with which the MF sweeps across the eye disc ., We developed a spatio-temporal model of the growing eye disc based on the regulatory interactions controlled by the signals Decapentaplegic ( Dpp ) , Hedgehog ( Hh ) and the transcription factor Homothorax ( Hth ) and explored how the signaling patterns affect the movement of the MF and impact on eye disc growth ., We used published and new quantitative data to parameterize the model ., In particular , two crucial parameter values , the degradation rate of Hth and the diffusion coefficient of Hh , were measured ., The model is able to reproduce the linear movement of the MF and the termination of growth of the primordium ., We further show that the model can explain several mutant phenotypes , but fails to reproduce the previously observed scaling of the Dpp gradient in the anterior compartment .

Patterning and growth of a tissue are linked during early development and have to be tightly controlled ., During the development of the Drosophila eye , this linkage is particularly clear: A moving signaling wave sweeps across the tissue that will eventually develop into the eye of the fly ., This wave is responsible for the transition from cells undergoing cell divisions in front of the wave into differentiated , specialized cells that are not dividing anymore and that eventually develop into the many individual eye units of the compound eye ., Therefore , the final size of this tissue depends on how fast cells in front of the wave are growing and dividing and on the speed with which the signaling wave sweeps across the tissue ., We developed a computational model based on regulatory interactions that have been experimentally determined in order to explore how the signaling patterns affect the movement of the signaling wave and impact on tissue growth ., The model captures the movement of the signaling wave at a constant speed and the growth termination of the developing tissue ., We further show that the model can explain the abnormal size of the eye that can be observed in several genetically modified fly strains .

invertebrates, medicine and health sciences, light microscopy, neuroscience, animals, cell differentiation, animal models, developmental biology, drosophila melanogaster, model organisms, microscopy, eyes, drosophila, research and analysis methods, sensory physiology, chemistry, dpp signaling cascade, head, fluorescence recovery after photobleaching, insects, hedgehog signaling, arthropoda, physics, visual system, signal transduction, mass diffusivity, eye movements, anatomy, cell biology, physiology, biology and life sciences, ocular system, sensory systems, physical sciences, chemical physics, cell signaling, organisms, signaling cascades

journal.pntd.0001348

Genetic Manipulation of Schistosoma haematobium, the Neglected Schistosome

More people are infected with Schistosoma haematobium than with the other schistosomes combined ., Of >110 million cases of S . haematobium infection in sub-Saharan Africa , 70 million are associated with hematuria , 18 million with major bladder wall pathology , and 10 million with hydronephrosis leading to severe kidney disease 1 , 2 , 3 ., In many patients , chronic inflammation in response to S . haematobium ova leads to squamous cell carcinoma of the bladder 4 , 5 ., S . haematobium is classified as a Group 1 carcinogen by the World Health Organizations International Agency for Research on Cancer 6 , 7 although the cellular and/or molecular mechanisms linking S . haematobium infection with cancer formation have yet to be defined 8 ., One quarter to three quarters of women infected with S . haematobium suffer from female genital schistosomiasis ( FGS ) of the lower genital tract 1 ., FGS results from deposition of the schistosome eggs in the uterus , cervix , vagina and/or vulva , with ensuing host inflammatory responses comprised of granulomas , fibrosis , and pathological localized blood vessel formation 9 ., FGS increases susceptibility to HIV/AIDS 10 , 11 , 12 , and decreases female fertility 13 ., Given the enormous numbers of people infected with S . haematobium , and the pathogenesis of S . haematobium infection , including its association with bladder cancer and HIV/AIDS , there is a pressing need for new approaches to control including the development of a vaccine to prevent infection with S . haematobium ., With regard to fundamental aspects of the host-parasite relationship , research on S . haematobium is in its infancy compared to S . mansoni and S . japonicum 14 ., There have been massive recent advances in genomic , transcriptomic , and proteomic datasets for both S . japonicum and S . mansoni 15 , 16 , 17 ., There now is an urgent need to establish similar datasets for S . haematobium , and in addition to establish tools and approaches to determine the function and importance of these schistosome genes - including S . haematobium-specific genes 14 ., Here we cultured several developmental stages of S . haematobium and applied several functional genomics approaches to this species ., We report that this schistosome , like S . mansoni and S . japonicum , is amenable to transformation with nucleic acid probes ., Notably , the findings indicated the presence of an intact , active RNA interference pathway in S . haematobium , the neglected schistosome ., Eggs of an Egyptian strain of S . haematobium were isolated from either small intestines , that had been thoroughly rinsed in 1× PBS to remove the gut contents , or livers of experimentally infected Syrian golden hamsters 18 following a protocol optimized for isolating eggs of S . mansoni from livers of mice 19 ., In brief , three to five livers or two to three washed small intestines were chopped finely with a scalpel blade , and then blended to a smooth consistency in 50 ml of phosphate-buffered saline , pH 7 . 4 ( PBS ) , 5 ml of 0 . 5% clostridial collagenase ( Sigma ) and 500 µl of polymyxin B ( Sigma ) ., Digests were incubated with gentle shaking at 37°C overnight , after which the contents were subjected to centrifugation at 400×g for 5 min ., The supernatant was removed and the pellet resuspended in 50 ml PBS ., This wash procedure was repeated twice more , with the exception that after the final centrifugation the pellet was resuspended into 25 ml of PBS ., The resuspended mixture from liver was passed sequentially through 250 and 150 µm sieves ., No passes through sieves were performed with the gut mixture ., The liver mixture filtrate or the gut mixture were centrifuged at 400×g for 5 min , the supernatant discarded and the pellet resuspended in 3 ml of PBS ., This was applied to a column of Percoll , prepared by mixing 8 ml of Percoll ( GE Healthcare Bio-Science AB ) with 32 ml of 0 . 25 M sucrose in a 50 ml tube ., The tube was centrifuged at 800×g for 10 min ., Liver or intestinal cells and debris that remained on the top of the Percoll were removed with a Pasteur pipette ., The schistosome eggs , which pelleted tightly at the bottom of the tube , were washed three times with PBS and any residual host cells were removed by discarding the supernatant ., Further purification of eggs was achieved by resuspension in 0 . 5 ml of PBS and application on to a second Percoll column , prepared by mixing 2 . 5 ml of Percoll with 7 . 5 ml of 0 . 25 M sucrose in a 15 ml polypropylene tube ., The eggs were pelleted and then washed as before ., Some eggs were snap frozen and stored at −80°C until use for extraction of total RNA ., For other aliquots , the eggs were resuspended in 6 ml of complete culture medium - Dulbeccos modified Eagles medium ( DMEM ) with 10% fetal bovine serum ( FBS ) and 100 U of penicillin and streptomycin ( Invitrogen , Carlsbad , CA ) , split into 2 ml aliquots in a six-well plate and cultured at 37°C under 5% CO2 ., S . haematobium schistosomula were obtained by mechanical transformation of cercariae released from infected Bulinus truncatus truncatus snails and cultured at 37°C in modified Baschs medium under 5% CO2 in air as described for S . mansoni schistosomula 20 ., Mixed sex adults of S . haematobium were obtained by portal perfusion of infected hamsters followed by mesenteric vessel dissection and manual removal of adult worms using forceps under a magnification glass 18 ., The adults were rinsed several times in PBS and cultured in complete culture medium ., S . haematobium eggs were either electroporated and soaked in non-coding Cy3-labeled siRNAs ( Silencer Cy3-Labeled Negative Control siRNA , Ambion , Austin , TX ) at 50 ng/µl with conditions as described 21 ., Briefly , eggs were washed in DMEM supplemented with 200 U/ml penicillin G sulfate , 200 mg/ml streptomycin sulfate , 500 ng/ml amphotericin B , 10 mM HEPES ( wash medium ) and transfected in 100 µl of the same medium in 4 mm gap cuvettes with an ElectroSquarePorator ECM830 ( BTX , San Diego , CA ) using a single square wave pulse of 125 volts of 20 milliseconds duration ., After electroporation , eggs were washed in PBS three times to remove the unincorporated Cy3-labeled siRNA ., Subsequently , eggs were transferred into complete DMEM at 37°C for three hours ., Other eggs were soaked for three hours in Cy3-siRNA , then washed in PBS three times in order to remove the unincorporated Cy3-labeled siRNAs ., The Cy3-siRNA exposed eggs , with or without electroporation , were examined under bright and fluorescent light ( below ) using a Zeiss Axio Observer A . 1 inverted microscope fitted with a digital camera ( AxioCam ICc3 , Zeiss ) ., Manipulation of digital images was undertaken with the AxioVision release 4 . 6 . 3 software ( Zeiss ) ., These manipulations were limited to insertion of scale bars , adjustments of brightness and contrast , cropping and the like; image enhancement algorithms were applied in linear fashion across the entire image and not to selected aspects ., To synthesize firefly luciferase mRNAs ( mLuc ) , in vitro transcriptions of capped RNAs from PCR DNA templates were accomplished using the mMachine T7 Ultra kit ( Ambion ) as described 22 , 23 ., Subsequently , RNAs were precipitated with ammonium acetate , dissolved in nuclease-free water and quantified by spectrophotometry ( NanoDrop Technologies , Wilmington , DE ) ., The dsRNAs were generated by in vitro transcription using , as templates , PCR products amplified with gene specific primers tailed with the T7 promoter sequence ., A luciferase dsRNA ( dsLuc ) template encoding the full length 1 , 672 kb was amplified from the pGL3-basic plasmid ( Promega , Madison , WI ) , ( F: 5′TAATACGACTCACTATAGGGTGCGCCCGCGAACGACATTTA-3′; R: 5′- TAATACGACTCACTATAGGGGCAACCGCTTCCCCGACTTCCTTA-3′ ) ., The siRNAs were designed with the assistance of the BLOCK-iT™ RNAi Designer Tool , https://rnaidesigner . invitrogen . com/rnaiexpress/index . jsp ., Block-iT™ siRNA of 19 nt in length named siShTSP 2 ( 5′-GGA AUC CUG UUU CAA AGA U-3′ ) , specific for residues 159–177 of the extracellular loop 2 of S . haematobium tetraspanin 2 ( Sh-tsp-2 ) and an irrelevant siRNA ( control ) termed siScrambled , 5′-GGA GUC CCU UUA AAU AGA U-3′ , the sequence of which included the same residues of siSh-tsp-2 but in which the order of the residues had been randomly mixed , were purchased from Invitrogen ., S . haematobium eggs were maintained for one day after isolation from hamsters , then subjected to electroporation in the presence of mLuc at 150 ng/µl 21 ., Briefly , ∼2 , 000 eggs were subjected to the square wave electroporation in 4 mm gap pathway cuvettes ( BTX ) in 100 µl wash medium , as above ., A group electroporated in the absence of mLuc was included as a mock-treated control ., Thereafter the eggs were kept in culture for 3 or 20 hours , harvested and stored at −80°C ., For RNAi approaches , one group of eggs was incubated with 30 µg of dsLuc , and other two groups were incubated without dsLuc ., After 10 min at 23°C , 15 µg of mLuc was added to eggs in wash medium , except to a mock control group , i . e . a group of eggs not treated with exogenous nucleic acids ., The eggs were subjected to square wave electroporation ( above ) , transferred to pre-warmed culture medium and harvested three hours later ., Schistosomula of S . haematobium were removed from culture three hours after cercarial transformation , washed and resuspended in 100 µl of wash medium containing 30 µg of dsLuc ., Two other groups of schistosomules were incubated in the absence of dsLuc , in 4 mm gap cuvettes ., After 10 min incubation at 23°C , 15 µg of mLuc was added to the wash medium in each group , except to a mock control group ., Thereafter the schistosomules were subjected to square wave electroporation , 125 V , 20 ms , transferred to prewarmed Baschs medium and harvested three hours later ., We have recently determined that dicing adult schistosomes into several fragments results in more reporter gene activity than in similar numbers of intact worms 24 ., Accordingly , ∼50 mixed sex adults of S . haematobium were removed from culture 24 hours after perfusion from hamsters , washed , diced into three or four fragments using a sterile blade ., Intact or fragmented S . haematobium worms were placed into 4 mm gap pathway cuvettes in the presence of 15 µg of mLuc resuspended in 100 µl of wash medium and subjected to square wave electroporation , 125 V , 20 ms , one pulse ., After electroporation , the worms and fragments were transferred into pre-warmed complete culture medium , incubated at 37°C under 5% CO2 in air , and harvested 3 hours later ., For RNAi approaches targeting the luciferase reporter gene , the worms were diced into three or four fragments using a sterile blade , washed three times in wash medium and transferred to 4 mm gap cuvettes containing 100 µl wash medium ., One group of diced adult worms was incubated with 30 µg of dsLuc , and the other two were incubated in the absence of dsLuc ., Following incubation at 23°C for 10 min , 15 µg of mLuc was added to each group , except to the mock control group after which the parasites were subjected to a single pulse of square wave electroporation , 125 V , 20 ms . Subsequently , the diced worms were transferred to complete medium and maintained in culture; the worm fragments remained active ( displaying movements ) during the study ., For RNAi targeting an endogenous S . haematobium gene , intact adult worms were electroporated in the presence of 10 µg of siSh-tsp-2 or 10 µg of siScrambled in 100 µl ( 16 . 5 µM ) of wash medium ., We targeted intact worms for this experiment , dealing with silencing of an endogenous gene , with the aim of determining whether a gross phenotype might accompany gene knockdown ., After electroporation , worms were transferred to complete medium for 48 h , then stored at −80°C ., Developmental stages of S . haematobium were harvested three hours after electroporation unless otherwise indicated , washed three times with wash medium and stored as wet pellets at −80°C ., Luciferase activity in extracts of parasites was determined using Promegas luciferase assay reagent system and a tube luminometer ( Sirius , Berthold , Pforzheim , Germany ) 22 ., In brief , pellets of parasites were subjected to sonication ( 3×5 s bursts for schistosomula and adults and 5×5 s bursts for eggs , output cycle 4 , Misonix Sonicator 3000 , Newtown , CT ) in 300 µl 1× CCLR lysis buffer ( Promega ) ., The sonicate was clarified by centrifugation at 20 , 800 g , 15 min , 4°C and the supernatant , containing the soluble fraction , analyzed for luciferase activity ., Aliquots of 100 µl of soluble fraction were injected into 100 µl luciferin at 23°C , mixed , and relative light units ( RLUs ) determined 10 s later by the luminometer ., Replicate samples were measured , with results presented as the average of the readings per mg of protein ., The protein concentration in the soluble fraction of the schistosome extract was determined using the bicinchoninic acid assay ( Pierce , Rockford , IL ) ., Recombinant luciferase ( Promega ) was included as a positive control ., Expression of Sh-tsp-2 mRNA was analyzed in adults of S . haematobium harvested 48 hours after RNAi treatment ., Total RNA was extracted from the worms using the RNAqueus®-4PCR Kit ( Ambion ) ., Any residual DNA remaining in the RNA was removed by DNase digestion using TurboDNase ( Ambion ) and cDNA was synthesized from 100 ng of total RNA using the iScript cDNA Synthesis Kit ( Bio-Rad , Hercules , CA ) ., Primers and TaqMan probes were designed with the assistance of Beacon Designer ( Premier Biosoft International , Palo Alto , CA ) to obtain probes targeting Sh-tsp-2 and S . haematobium tropomyosin ( ShTrop ) ( GenBank L76202 . 1 ) genes , as follows: for ShTSP 2 , forward primer: 5′-GAT GCA TTA AGA GAA TTC GTA A- 3′; reverse primer: 5′-TGG TGG AGT GAC ATA ATC-3′; probe: 5′-/56-FAM/TGA AGA ATC AGC ACC ACA GCA TTG/3IABlk_FQ/-3′; for ShTrop , forward primer: 5′-ATC CGA GAT TTA ACA GAA C-3′; reverse primer: 5′-CGC TAA GAG CTT TGT ATC-3′; probe: 5′-/56-FAM/TTC TCA GCC AGT AAG TCA TCT TCC AA/3IABlk_FQ/-3′ ., Quantitative PCRs were performed in triplicate , using 96-well plates ( Bio-Rad ) , with an initial denaturation step at 95°C for 3 minutes followed by 40 cycles of 30 sec at 95°C and 30 sec at 50°C , using a thermal cycler ( iCycler , Bio-Rad ) and a Bio-Rad iQ5 detector to scan the plates in real time ., Reactions were carried out in 20 µl volumes with primer-probe sets ( Sh-tsp-2 , ShTrop ) and Perfecta qPCR FastMix , UNG ( Quanta Bioscience , Gaithersburg , MD ) ., The relative quantification assay 2−ΔΔCt method 25 was employed , using ShTrop as the reference gene ., Results were plotted as Sh-tsp-2 gene expression level relative to the reference gene considering 1\u200a=\u200aSh-tsp-2 relative expression level measured in the irrelevant control group ., Adult flukes were fixed in 4% paraformaldehyde overnight , rinsed with PBS , then incubated in propidium iodine ( PI ) diluted 1∶1000 for one day ., The PI-stained worms were placed on polylysine coated 50 mm Petri dishes , covered with PBS , and examined using a Carl Zeiss LSM 710 confocal system ., This system includes a Zeiss Axio Examiner Z1 upright microscope equipped with a 20×/1 . 0 water dipping objective lens , deployment of which seemed prudent for imaging entire schistosomes since this objective does not require a coverslip ( which markedly diminishes spherical aberrations ) ., Confocal images were captured using a Qasar 32-channel spectral detector ., Briefly , the worms were simultaneously scanned with 488 and 561 nm laser lines ( multiline argon and diode laser , respectively ) , while the backward light was registered in 1024×1024 lambda-stack images taken simultaneously at a spectral resolution of 9 . 6 nm ., Thus , for each single optical section , 32 images were recorded covering the visible spectrum from 423–721 nm , allowing the analysis of each of the ( 1 ) reflected light , ( 2 ) autofluorescence , and ( 3 ) characteristic emission at 617 nm from PI ., To detect the reflected laser light , we utilized a T80/R20 beamsplitter , which only partially attenuates the laser lines in the backward direction ., Confocal stacks for three-dimensional ( 3D ) rendering were taken at z-scaling of 1 . 7 µm , which matched the pinhole opening ( 34 µm ) ., Pixel resolution was 0 . 59 µm ., After completion of the online acquisition , a linear spectral unmixing protocol was applied to the lambda-stacks to generate two three-channel confocal stacks ., To generate reliable spectral unmixed channels , various sites from the worm were tested and representative for the 488-line reflection , autofluorescence and PI were selected and used as reference for unmixing ., Thus , the resulting images , encoded in three channels reflected light , autofluorescence and PI signals from the nuclei ., Unmixed , confocal stacks were imported to Volocity ( v . 5 . 5 , Perkin Elmer/Improvision ) for further three-dimensional rendering and analysis ., Male LVG hamsters were purchased from Charles River ( Wilmington , MA ) and maintained in the Biomedical Research Institutes ( BRI ) animal facility , which is accredited by the American Association for Accreditation of Laboratory Animal Care ( AAALAC; #000779 ) , is a USDA registered animal facility ( 51-R-0050 ) , and has an Animal Welfare Assurance on file with the National Institutes of Health , Office of Laboratory Animal Welfare ( OLAW ) , A3080-01 ., Maintenance of the hamsters , exposure to S . haematobium cercariae , and subsequent harvesting of tissues were approved by the BRI Institutional Animal Care and Use Committee ( protocol approval number 09-03 ) ., All procedures employed were consistent with the Guide for the Care and Use of Laboratory Animals ., Given the scarcity of reports on in vitro culture techniques focused on S . haematobium we adapted protocols from studies with S . mansoni 20 , to maintain some developmental stages in culture ., Thus , eggs isolated either from small intestines or livers of hamsters , schistosomula mechanically transformed from cercariae released by experimentally infected B . t ., truncatus snails , and mixed sex adults from portal perfusion and mesenteric vessel dissection of hamsters were cultured in the indicated medium at 37°C , 5% CO2 ., No differences in gross appearance were evident between the eggs isolated from intestines ( Figure 1A and B ) or liver ( Figure 1C and D ) ., The eggs were cultured in complete medium ( Dulbeccos modified Eagles medium ( DMEM ) with 10% fetal bovine serum ( FBS ) and 100 U of penicillin and streptomycin ( Invitrogen , Carlsbad , CA ) , for up to seven days ( not shown ) ., Mixed sex adults were cultured in complete medium ( above ) for up to five days ( Figure 2A ) ., Notably , at higher magnification , longitudinal orientation of the eggs within the uterus of the female schistosome was apparent ( Figure 2B ) which is in marked contrast to the transverse disposition in utero of S . mansoni eggs , e . g . 26 , 27 ., Schistosomula of S . haematobium , obtained by cercarial transformation as described above , were cultured in modified Baschs medium ( Figure 2C and 2D ) ., To investigate whether macromolecules could be introduced into S . haematobium eggs , cultures of eggs were incubated in a Cy3- siRNA ( 13 . 8 kDa ) with or without concomitant square wave electroporation ., Three hours after exposure to Cy3-siRNA , eggs were examined by fluorescence microscopy ., Surprisingly , strong fluorescence including foci of intense fluorescence was revealed in Cy3-siRNA soaked eggs in contrast to those subjected to electroporation ( Figure 3 and S1 ) ., More than 80% of the treated eggs emitted fluorescence as revealed at low magnification ( Figure S1 ) ., These results indicated that it is possible to introduce Cy3-siRNA into S . haematobium eggs by simple soaking and that electroporation was not essential for this reporter probe ., ( However , with some stages , electroporation is more efficient: it mobilizes dsRNA or mRNA into the target worms quickly , which is advantageous when working with RNAs that are labile . ), Although the structure of the S . haematobium eggshell is not well described , pores are present in the eggshell of S . mansoni eggs – the eggshell has been described as cribriform 28 ., To ascertain if transgene mRNAs could penetrate schistosome eggs and be translated into an active protein , we electroporated cultured eggs in the presence of firefly luciferase mRNA ( mLuc ) ( 512 kDa ) ., More specifically , 48 hours after isolation 1 , 500–2 , 500 eggs were subjected to electroporation in the presence of 150 ng/µl of mLuc , and collected three and 20 hours later ., Luciferase activity was detected in the mLuc electroporated group compared with untreated control at 3 h , and even higher luciferase activity was measured in eggs harvested at 20 h after electroporation ( Figure 4A ) ., ( A signal of ∼100–150 RLUs/sec/mg was measured in the mock control group , which represents the background baseline of this assay . ), We electroporated intact and fragmented adult worms in the presence of 15 µg of luciferase mRNA , and measured the luciferase activity 3 hours later ., Several fold ( ∼3 . 5 times ) more activity was detected in fragmented than intact worms ( Figure 4B ) , in like fashion to S . mansoni 24 ., Collectively , these findings indicated that square wave electroporation efficiently delivered exogenous nucleic acids into the eggs and adults of S . haematobium and that reporter luciferase was functionally translated from this exogenous mRNA ., We have reported that it is feasible to knock down an exogenous reporter transgene by dsRNA in order to detect an active RNAi pathway in flukes 23 , 29 ., Given that S . haematobium can be productively transformed with mRNA by square wave electroporation , we proceeded to investigate silencing of expression of the exogenous reporter transcript ( mLuc ) ., About 2 , 000 eggs were removed from culture four days after isolation , washed and subjected to electroporation in the presence of both mLuc and dsLuc ( mLuc+dsLuc group ) ., Control eggs electroporated in the absence of exogenous RNAs ( mock control ) and positive control eggs electroporated in the presence of mLuc ( mLuc group ) were included ( Figure 5A ) ., Reduced luciferase activity was evident in the mLuc+dsLuc group in comparison with the mLuc group , even though the luciferase activity in terms of absolute RLUs/sec/mg measured in eggs at three hours after electroporation was relatively low in comparison to the other developmental stages ( Figure 5B , left panel ) ., ( It appears to be more difficult to introduce mRNA into eggs than other developmental stages , likely because of the presence of the eggshell . ), The experiment with eggs was repeated three times; knock down was apparent in two of the three trials ., Fragmented adults were also examined; >75% knockdown of luciferase was observed ( Figure 5B , center panel ) ., The experiment was repeated; knock down was obvious on each occasion ., Furthermore , three hour old schistosomula were co-transfected with messenger RNA encoding luciferase ( mLuc ) and dsRNA targeting the luciferase transcript ( dsLuc ) by electroporation ( mLuc+dsLuc group ) , along with controls ( experimental design shown in Figure 5A ) ., At 3 h after electroporation , luciferase activity of 14 , 080 RLU/sec/mg was evident in lysates of the positive control schistosomules transfected with mLuc ., By contrast , luciferase activity in the schistosomules exposed to both dsLuc and mLuc was significantly lower , 6073 RLUs/sec/mg , representing 43% of the positive mLuc control ( Figure 5B , right panel ) ., In review , a similar trend was apparent in each of these developmental stages: it is feasible to knock down the reporter luciferase gene in eggs , schistosomules and adults of S . haematobium ., In addition to reporter luciferase , we introduced siRNA specific for Sh-tsp-2 , an orthologue of a S . mansoni membrane protein critical for tegument formation , the tetraspanin Sm-TSP-2 30 into intact S . haematobium mixed sex adults ., At 48 hours after electroporation of siRNAs – siSh-tsp-2 and a control siRNA , we observed a significant knock-down of levels of the Sh-tsp-2 transcript ( Figure 6 ) ., This experiment was repeated three times; knock down was seen on each occasion , and on two of these three occasions the knock-down was >75% ., Notably , no gross phenotypic differences among the adult worms were evident by light microscopy ( not shown ) ., In addition to the images of cultured stages of S . haematobium , adult worms were fixed in 4% paraformaldehyde and stained with PI ., Spectral confocal microscopy was used to image the entire volume of the paraformaldehyde fixed male and female worms at high resolution ( Figure 7A ) ., We employed A T80/R20 beamsplitter to image the flukes , using backward scattered laser light ., The reflected light is registered on the lambda stack as a dual-peak at the wavelength of the laser used for excitation ., In this case , the 488 nm laser line produced a large reflection response , from which images of the surface of the schistosomes were assembled ( Figure 7B and F ) ., The approach also recorded in consistent and reproducible manner , autofluorescence deriving from the gut and , dramatically , eggs in utero ( Figure 7D and E ) ., The autofluorescence registered on the lambda stack displayed a broad spectrum - peak ∼560 nm , range 500–650 nm ( overlapping with numerous widely employed dyes and fluorescent proteins ) ., The signals from nuclei stained with PI ( Figure 7C and G ) registered as a spectral curve ( peak 617 nm ) that partially overlapped with the red-shifted slope of the autofluorescence ., Thus , we could select discrete sites on worms representing reflected light , autofluorescence and PI fluorescence that served as references for linear unmixing 31 ., The three-channel confocal stacks , derived after linear unmixing , comprised channels representing the reflection , autofluorescence and PI signal at high signal to noise ratio ., Figures 7E and 7H show three-dimensional images assembled from the merged reflected light , autofluorescence and the PI fluorescence signals ., Using S . haematobium eggs from livers and intestines of experimentally infected hamsters , adult worms perfused from the hamsters and cercariae from B . t ., truncatus snails , and using similar approaches to those for S . mansoni 20 , we were able to culture eggs , schistosomules , and adults of S . haematobium and to subject these developmental stages to genetic manipulation ., We transformed eggs of S . haematobium with a small nucleic acid probe , Cy3-siRNA ., Experience with the other two major schistosomes has revealed that the schistosome egg represents an attractive developmental stage at which to target transgenes because it is readily obtained from experimentally-infected rodents or naturally infected people , is easily maintained in vitro , has a high ratio of germ to somatic cells and contains a miracidium that can be employed to infect snails to propagate the life cycle 21 , 32 , 33 ., Furthermore , from the clinical perspective , the egg represents the major source of pathogenesis in human schistosomiasis haematobia ., We observed that exogenous macromolecules penetrate into cultured eggs , and we speculate that small macromolecules such as Cy3-Silencer siRNA ( 13 . 8 kDa ) enter eggs through the pores that likely anastomose throughout the eggshell and which provide access from sub-shell envelope and the developing miracidium to the exterior , in like fashion to the egg of S . mansoni 29 , 34 , 35 ., Others and we have described the utility of firefly luciferase as a transgene probe in S . mansoni and the liver fluke Fasciola hepatica 21 , 22 , 23 , 29 , 36 ., We have also reported the utility of luciferase as a model target to identify the presence of an active RNA interference pathway in less well studied helminth parasites , especially where genome sequences are unavailable 23 ., Using this strategy , we now present findings that indicate for the first time the presence of an intact RNAi pathway in S . haematobium ., In each of three developmental stages investigated – eggs , schistosomula , and mixed sex adults , co-introduction of dsRNA spanning the transcript of firefly luciferase and of mRNA encoding firefly luciferase resulted in robust knockdown of the exogenous mRNA ., Luminometric measurement of luciferase activity provided a direct demonstration of gene silencing at the protein level ., In S . mansoni , comparative studies indicate that efficiency of RNAi efficiency following electroporation is superior to passive soaking 37 ., Here we employed square wave electroporation to introduce the dsRNA and luciferase mRNA into developmental stages of S . haematobium ., Eggs , schistosomula and adults of S . haematobium were amenable to transfection with foreign nucleic acids using this technique ., Given that these stages tolerated the electro-transfection conditions well , we anticipate that this technique can be optimized for genetic analysis and genomic manipulation of S . haematobium ., Whereas soaking performed better than electroporation alone for eggs of S . haematobium , it will be worthwhile to employ electroporation followed by soaking of the transfected eggs , a combination that is superior to soaking alone in eggs of S . mansoni 21 ., Although little is known about the protein encoding genes of S . haematobium , we obtained the sequence of Sh-tsp-2 , an apparent orthologue of Sm-tsp-2 which encodes a lead vaccine antigen for schistosomiasis mansoni 30 ., By targeting the sequence encoding the extracellular loop 2 domain of this protein with a 19 nt siRNA , we observed strong knockdown of the Sh-tsp-2 transcript in adult worms ., Thorough studies targeting this gene are warranted given its performance as a vaccine antigen for S . mansoni infection and because of the integral role that Sm-TSP-2 plays in development , maturation or stability of the tegument 30 ., We deployed laser scanning confocal microscopy to view the adult stage of S . haematobium ., In addition to facilitating views of the entire worms ( ≥1 cm in length ) , the approach circumvents barriers to reliable fluorescence imaging of schistosomes , including the notion of autofluorescence of schistosome eggs , e . g . 38 ., The provenance of signals from the eggs ( and gut ) aside , the phenomenon deserves deeper exploration since it has the potential to chart predictable anatomical landmarks of the schistosome that can facilitate microanalysis of schistosome organs and tissues ., Future characterization of native fluorescence signals of schistosomes can be expected to be of interest ., Unlike fixed worms , evaluation of the living worms critically depends on minimizing invasive approaches ., Also , fluorescence labels suitable for living cells generally cause some perturbation of normal functions ., Thus , a library of spectrally distinctive signals – including the signal from eggs reported here - can be expected to facilitate microscopic imaging of viable schistosomes ., Collectively , spectral confocal imaging provided the technological capacity to document eggs in utero of this neglected schistosome by extracting their emission of autofluorescence ., Also , we imaged adult S . haematobium worms by staining tegumental nuclei with propidium iodide , which allowed the assembly of the three dimensional structure of the blood fluke ., The spectral confocal microscopy approaches allowed differentiation of a fluorochrome from natural signals , e . g . PI versus autofluorescence , and portends its likely utility for monitoring reporter genes such as green fluorescent protein in transgenic schistosomes ., In conclusion , this is the first report of genetic manipulation of S . haematobium ., The procedures described here are expec

Introduction, Materials and Methods, Results, Discussion

Minimal information on the genome and proteome of Schistosoma haematobium is available , in marked contrast to the situation with the other major species of human schistosomes for which draft genome sequences have been reported ., Accordingly , little is known about functional genomics in S . haematobium , including the utility or not of RNA interference techniques that , if available , promise to guide development of new interventions for schistosomiasis haematobia ., Here we isolated and cultured developmental stages of S . haematobium , derived from experimentally infected hamsters ., Targeting different developmental stages , we investigated the utility of soaking and/or square wave electroporation in order to transfect S . haematobium with nucleic acid reporters including Cy3-labeled small RNAs , messenger RNA encoding firefly luciferase , and short interfering RNAs ( siRNAs ) ., Three hours after incubation of S . haematobium eggs in 50 ng/µl Cy3-labeled siRNA , fluorescent foci were evident indicating that labeled siRNA had penetrated into miracidia developing within the egg shell ., Firefly luciferase activity was detected three hours after square wave electroporation of the schistosome eggs and adult worms in 150 ng/µl of mRNA ., RNA interference knockdown ( silencing ) of reporter luciferase activity was seen following the introduction of dsRNA specific for luciferase mRNA in eggs , schistosomules and mixed sex adults ., Moreover , introduction of an endogenous gene-specific siRNA into adult schistosomes silenced transcription of tetraspanin 2 ( Sh-tsp-2 ) , the apparent orthologue of the Schistosoma mansoni gene Sm-tsp-2 which encodes the surface localized structural and signaling protein Sm-TSP-2 ., Together , knockdown of reporter luciferase and Sh-tsp-2 indicated the presence of an intact RNAi pathway in S . haematobium ., Also , we employed laser scanning confocal microscopy to view the adult stages of S . haematobium ., These findings and approaches should facilitate analysis of gene function in S . haematobium , which in turn could facilitate the characterization of prospective intervention targets for this neglected tropical disease pathogen .

More people are infected with Schistosoma haematobium than other major human schistosomes yet it has been less studied because of difficulty in maintaining the life cycle in the laboratory ., S . haematobium might be considered the ‘neglected schistosome’ since minimal information on the genome and proteome of S . haematobium is available , in marked contrast to the other major schistosomes ., In this report we describe tools and protocols to investigate the genome and genetics of this neglected schistosome ., We cultured developmental stages of S . haematobium , and investigated the utility of introducing gene probes into the parasites to silence two model genes ., One of these , firefly luciferase , was a reporter gene whereas the second was a schistosome gene encoding a surface protein , termed Sh-tsp-2 ., We observed that both genes could be silenced – a phenomenon known as experimental RNA interference ( RNAi ) ., These findings indicated that the genome of S . haematobium will be amenable to genetic manipulation investigations designed to determine the function and importance of genes of this schistosome and to investigate for novel anti-parasite treatments .

medicine, biology

journal.pbio.2004015

Manipulating the revision of reward value during the intertrial interval increases sign tracking and dopamine release

Lesaint and colleagues 1 recently proposed a new computational model—the “STGT model” ( for sign tracking and goal tracking ) —which accounts for a large set of behavioral , physiological , and pharmacological data obtained from studies investigating individual variation in Pavlovian conditioned approach behavior 2–8 ., Most notably , the model can account for recent work by Flagel and colleagues ( 2011 ) that has shown that phasic dopamine ( DA ) release does not always correspond to a reward prediction error ( RPE ) signal arising from a classical model-free ( MF ) system 9 ., In their experiments , Flagel and colleagues trained rats on a classical autoshaping procedure , in which the presentation of a retractable-lever conditioned stimulus ( CS; 8 s ) was followed immediately by delivery of a food pellet ( unconditioned stimulus US ) into an adjacent food cup ., In procedures like this , some rats , known as sign trackers ( STs ) , learn to rapidly approach and engage the CS lever , whereas other rats , known as goal trackers ( GTs ) , learn to approach and enter the food cup upon presentation of the CS lever ., Although both sign and goal trackers learn the CS-US relationship equally well , it was elegantly shown that phasic DA release in the nucleus accumbens core ( NAc ) matched RPE signals only in STs 4 ., Specifically , during learning in ST rats , DA release to reward decreased , while DA release to the CS increased ., In contrast , even though GTs acquired a Pavlovian conditioned approach response , DA release to reward did not decline , and CS-evoked DA was weaker ., Furthermore , administration of a DA antagonist blocked acquisition of the ST conditioned response but did not impact the GT conditioned response 4 , 10 ., Several computational propositions have argued that these data could be interpreted in terms of different contributions of model-based ( MB ) —with an explicit internal model of the consequences of actions in the task—and MF—without any internal model—reinforcement learning ( RL ) in GTs and STs during conditioning 1 , 11 ., Nevertheless , only the STGT model predicted that manipulating the intertrial interval ( ITI ) should change DA signaling in these animals: the model suggests that GTs revise the food cup value multiple times during and in between trials during the 90-s ITI ., During the trial , the food cup gains value because reward is delivered; however , visits to the food cup during the ITI do not produce reward , thus reducing the value assigned to the food cup ., This mechanism prevents the progressive transfer of reward value signal in the model from US time to CS time and hence explains the absence of DA RPE pattern in goal trackers ., This aspect of the model predicts that decreasing the ITI should reduce the amplitude of US DA burst ( i . e . , less time to negatively revise the value of the food cup and reduce the size of the RPE ) and that higher food cup value should lead to an increase in the tendency to GT in the overall population ., In contrast , increasing the ITI should have the opposite effect ., That is , lengthening the ITI and therefore increasing the number of nonrewarded food cup entries should increase the amplitude of US DA burst ( i . e . , more time to negatively revise the value of the food cup during the ITI and increase the size of the RPE ) and lower the value of the food cup , leading to a decreased tendency to GT and an increase tendency to ST . The latter would be accompanied by a large phasic DA response to the highly salient lever CS , as previously observed in STs 4 ., Here , we tested these predictions by recording DA release in the NAc using fast-scan cyclic voltammetry ( FSCV ) during 10 d of Pavlovian conditioning in rats that had either a short ITI of 60 s or a long ITI of 120 s ., DA release was recorded from NAc ( S1B–S1E Fig ) during a standard Pavlovian conditioned approach behavior task ( S1A Fig ) for 10 d ., Each trial began with the presentation of a lever ( CS ) located to the left or right side of a food cup ( counterbalanced ) for 8 s ., Upon the lever’s retraction , a 45-mg sucrose pellet was delivered into the food cup , independent of any interaction with the lever ., Each behavioral session consisted of 25 trials presented at a random time interval of either 60 s ( n = 7 rats ) or 120 s ( n = 12 rats ) ., To quantify the degree to which rats engaged in sign- versus goal-tracking behavior , we used the Pavlovian Conditioned Approach ( PCA ) index 12 , which comprised the average of three ratios: ( 1 ) the response bias , which is ( Lever Presses − Food Cup Entries ) / ( Lever Presses + Food Cup Entries ) , ( 2 ) the probability ( P ) difference , which is ( Plever − Preceptacle ) , and ( 3 ) the latency index , which is ( x¯ Cup Entry Latency − x¯ Lever Press Latency ) / 8 ., All of these ratios range from −1 . 0 to +1 . 0 ( similarly for PCA index ) and are more positive and negative for animals that sign track and goal track , respectively ., All behavioral indices were derived from sessions during which DA was recorded ., For the initial analysis described in this section , behavior and DA were examined across all sessions; the development of behavior and DA over training is examined in later sections ., The distributions of behavioral session scores are shown in Fig 1A–1D for each group ., As predicted , rats with the 120-s ITI tended to sign track more , whereas rats with the 60-s ITI tended to goal track more ., Across all behavioral indices ( i . e . , response bias , probability , latency , PCA ) , the mean distributions were positive ( biased toward sign tracking ) and significant from sessions for rats in the 120-s ITI group ( Fig 1A–1D , left; Wilcoxon; μ’s > 0 . 17 , p < 0 . 05 ) ., Opposite trends were observed in the 60-s ITI group in that all distributions were negatively shifted from zero ( Fig 1A–1D , right; Wilcoxon; response bias: μ = −0 . 06 , p = 0 . 06; lever probability: μ = −0 . 03 , p = 0 . 58; PCA index: μ = −0 . 11 , p = 0 . 097 ) ; however , only the shift in the latency difference distribution reached significance ( Fig 1C , right; Wilcoxon; μ = −0 . 10; p < 0 . 05 ) ., Direct comparisons between 60-s and 120-s ITI groups produced significant differences across all four measures ( Wilcoxons; p < 0 . 01 ) ., Thus , we conclude that lengthening the ITI increased sign-tracking behavior , as predicted by the STGT model 1 , 13 ., Notably , the degree of sign/goal tracking within the 60-s ITI group was highly dependent on when behavior was examined during the 8-s CS period ., This is illustrated in Fig 1G and Fig 1H , which show percent beam breaks in the food cup ( solid lines ) and lever pressing ( dashed lines ) over the time of the trial ., Consistent with the ratio analysis described above ( Fig 1A–1D ) , rats in the 120-s ITI group ( red ) showed sustained pressing ( red dashed ) that started shortly after lever extension and persisted throughout the 8-s CS period , while showing no increase in food cup entries ( red solid ) after CS presentation ( Fig 1G , red solid versus dashed ) ., Although it is clear that rats in the 120-s ITI group sign track more than goal track during the CS period , the relationship between lever pressing and food cup entry was far more dynamic during sessions with 60-s ITIs ( Fig 1G; blue ) ., During 60-s ITI sessions , rats would briefly enter the food cup for approximately 2 s immediately upon CS presentation ( Fig 1G , solid blue ) , before engaging with the lever ( Fig 1G , dashed blue ) ., As a result , lever pressing was delayed in the 60-s ITI group relative to the 120-s ITI group ( Fig 1G and 1H; blue versus red dashed ) ., This suggests that the goal-tracking tendencies described above during the entire 8-s CS period were largely due to the distribution of behaviors observed early in the CS period ., To quantify this observation , we recomputed the PCA index using either the first or the last 4 s of the 8-s CS period ., For the 120-s ITI group , the PCA index was significantly shifted in the positive direction during both the first and last 4 s of the cue period ( i . e . , more sign tracking; Fig 1E and 1F , left; Wilcoxon; μ’s > 0 . 16; p < 0 . 05 ) ., For the 60-s ITI group , the PCA index was significantly shifted in the negative direction during the first 4 s ( i . e . , more goal tracking; Fig 1E , right; Wilcoxon; μ = −0 . 16; p < 0 . 05 ) but not significantly shifted during the last 4 s ( Fig 1F , Wilcoxon; μ = 0 . 01; p = 0 . 81 ) ., Interestingly , this part of the results goes beyond the STGT model , which simplifies time by considering a single behavior/action during that period ., To further demonstrate sign- and goal-tracking tendencies over the 8-s cue period and the differences between groups , we simply subtracted 60-s ITI lever pressing and food cup entries from 120-s ITI lever pressing ( Fig 1I; orange ) and food cup entries ( Fig 1I; green ) , respectively ., Shortly after cue onset , the green line representing the difference between 120-s and 60-s ITI food cup entries dropped significantly below zero ., Throughout the cue period ( 8 s ) , there were more contacts with the food cup in sessions with a 60-s ITI compared with the 120-s ITI group ( green tick marks represent differences between 120-s and 60-s ITI across sliding 100-ms bins; t test; p < 0 . 05 ) ., For lever pressing ( orange ) , values were constantly higher shortly after the cue for the first half of the cue period ( orange tick marks represent differences between 120-s and 60-s ITI across sliding 100-ms bins; t test; p < 0 . 05 ) , indicating that there were more contacts with the lever in sessions with a 120-s ITI compared with the 60-s ITI group early in the cue period ., The behavioral data described above globally support model predictions that increasing and decreasing the ITI would produce more and less sign tracking , respectively ., Nevertheless , they also pave the way for improvements of the model by showing a rich temporal dynamic of behavior during the trial , rather than the single behavioral response per trial simulated in the model ., By plotting lever presses and food cup entries over time , we see that sometimes rats initially go to the lever and then go to the food cup , or vice versa ., In contrast , the model was designed to account only for the initial action performed by rats ., This was sufficient to account for the main results of the present study ., Nevertheless , it would be interesting to extend the model to enable it to account for different decisions made sequentially by the same animal during a given trial ., Next , we tested the prediction that longer ITIs would elevate DA release to the US , while shorter ITIs would reduce DA release to the US ., The average DA release over all sessions for the 60-s and 120-s groups is shown in Fig 2A ., Rats in the 120-s ITI group exhibited significantly higher DA release to the CS and the US relative to rats in the 60-s ITI group ( CS t test: t = 2 . 99 , df = 178 , p < 0 . 05; US t test: t = 3 . 07 , df = 178 , p < 0 . 05 ) ., In the 120-s ITI group , DA release to both the CS and the US was significantly higher than baseline ( CS t test: t = 14 . 77 , df = 119 , p < 0 . 05; US t test: t = 4 . 79 , df = 119 , p < 0 . 05 ) ; however , in the 60-s ITI group , this was only true during CS presentation ( t test: t = 7 . 34 , df = 59 , p < 0 . 05 ) ; DA release at the US was not different than baseline ( t test: t = 0 . 99 , df = 59 , p = 0 . 33 ) ., Similar results were obtained when averaging across sessions within each rat and then averaging across rats ( Fig 2B ) ; DA release was higher during the CS and US for rats in the 120-s ITI group ( CS t test: t = 1 . 87 , df = 17 , p < 0 . 05; US t test: t = 1 . 83 , df = 17 , p < 0 . 05 ) and was higher than baseline for both periods ( CS t test: t = 6 . 15 , df = 11 , p < 0 . 05; US t test: t = 2 . 16 , df = 11 , p < 0 . 05 ) , whereas DA release was only significantly higher during the CS period for rats in the 60-s ITI group ( CS t test: t = 6 . 68 , df = 6 , p < 0 . 05; US t test: t = 0 . 70 , df = 6 , p = 0 . 26 ) ., These results are in line with the STGT model , which predicted that reducing ITI duration would prevent the downward revision of the food cup value and hence would permit the high predictive value associated with the food cup to produce a DA response at CS but not US , consistent with the DA RPE hypothesis 9 ., Conversely and also consistent with model predictions , DA release during sessions with the longer ITI was significantly higher during US delivery because there were more positive RPEs , which may result from the positive surprise associated with being rewarded in a food cup whose value has been more strongly decreased during multiple visits to the food cup during long ITIs ., Nevertheless , at the CS time , the increased DA burst at CS indicates an even more complex process that goes beyond model predictions ., All of this suggests that DA release should be positively correlated with the time spent breaking the beam in the food cup during the ITI ., To test this hypothesis , we computed how much time was spent in the food cup during the ITI for each session ., This was done by determining the total number of beam breaks within each ITI ( 10-ms resolution ) and then averaging over trials to determine each session mean ., Importantly , the ITI time did not vary across sessions within each group , and the analysis was performed separately for the two groups ( 60-s group and 120-s group ) ., Thus , any correlation between DA and food cup interaction time during the ITI cannot reflect a correlation between DA and ITI time ., As expected , rats in the 120-s ITI group spent significantly more time in the food cup than did rats in the 60-s ITI group ( 120-s ITI group = 15 . 1 s; 60-s ITI group = 6 . 8 s; t test: t = 4 . 91 , df = 178 , p < 0 . 05 ) ., For both groups , there was a significant positive correlation between average time spent in the food cup during the ITI and DA release during the reward period ( Fig 2C , 120-s ITI: r2 = 0 . 12 , p < 0 . 05; Fig 2D , 60-s ITI: r2 = 0 . 08 , p < 0 . 05 ) ., During the cue period for the 120-s ITI group , but not the 60-s ITI group , there was also positive correlation ( Fig 2E , 120-s ITI: r2 = 0 . 04 , p < 0 . 05; Fig 2F , 60-s ITI: r2 = 0 . 01 , p = 0 . 36 ) ., Finally , when examining with data collapsed across both groups , there was a significant positive correlation during both cue and reward epochs ( Cue: r2 = 0 . 05 , p < 0 . 05; Reward: r2 = 0 . 14 , p < 0 . 05 ) ., Thus , we conclude that DA release to the CS and US tended to be higher the longer rats visited the food cup during the ITI ., In the analysis above , we averaged DA release and behavior from all recording sessions ., Next , we asked how behavior and DA release patterns evolved with training ., As a first step to addressing this issue , we recomputed the PCA analysis for the first and last 5 d of training ., For the 60-s ITI group , the PCA index distribution was significantly shifted in the negative direction ( i . e . , goal tracking ) during the first five sessions ( Wilcoxon; μ = −0 . 38 , p < 0 . 05 ) but not in the last five sessions ( Wilcoxon; μ = 0 . 15 , p = 0 . 07 ) ., Thus , early in training , rats with the 60-s ITI exhibited goal tracking more than sign tracking but did not fully transition to sign tracking , at least when we averaged over the last five sessions ., For the 120-s ITI group , the PCA index was significantly shifted in the positive direction ( i . e . , sign tracking ) during the last five sessions ( Wilcoxon; μ = 0 . 28 , p < 0 . 05 ) but was not during the first five sessions ( Wilcoxon; μ = 0 . 10 , p = 0 . 11 ) ., Thus , when the ITI was long ( 120 s ) , rats sign and goal tracked in roughly equal proportions during the first five sessions but tended to sign track significantly more during later sessions ., To more accurately pinpoint when during training rats in the 120-s group shift toward sign tracking , we examined the four distributions individually for each session ., Sign tracking became apparent during session 4 , when the latency and lever probability distributions first became significant ( Wilcoxon; latency: μ = 0 . 28 , p < 0 . 05; lever probability: μ = 0 . 40 , p < 0 . 05 ) ., To visualize changes in behavior and DA release that occurred before and after session 4 , we plotted food cup beam breaks , lever pressing , and DA release averaged across the first 3 d of training and across days 4–10 ( Fig 3; for visualization of behavior during each of the 10 sessions , please see S4 Fig ) ., Consistent with the distributions of behavioral indices described above , the 120-s ITI group showed roughly equal food cup entries and lever pressing during the CS period in the first 3 d of training ( Fig 3A , thin pink solid versus thin pink dashed ) , whereas later in training ( days 4–10; red ) , there was a strong preference for the lever ( Fig 3A; thick red dashed versus thick red solid ) ., Indeed , the distribution of PCA indices averaged during days 4–10 were significantly shifted in the positive direction ( Wilcoxon; μ = 0 . 27 , p < 0 . 05 ) ., These results suggest that in sessions in which the ITI was set at 120 s , sign-tracking tendencies developed relatively quickly during the first several recording sessions ( Fig 3A and 3C ) ., This is consistent with the STGT model , which predicted that increasing the ITI duration would increase the global tendency to sign track within the population and would thus speed up the acquisition of lever pressing behavior 1 , 13 ., In contrast , the model also predicted that reducing the ITI duration would increase the global tendency to goal track and would thus slow down the acquisition of lever pressing behavior ., Interestingly , the behavior of the 60-s ITI group was far more complicated than behavior of the 120-s group , with changes in goal and sign tracking occurring over training and CS presentation time ., Early in training , rats in the 60-s ITI group clearly visited the food cup ( Fig 3B , solid turquoise ) more than they pressed the lever ( Fig 3B , dashed turquoise ) ; food cup entries increased shortly after presentation of the CS and continued throughout the CS period ( Fig 3B , solid turquoise ) ., During later sessions ( i . e . , 4–10 ) , rats in the 60-s ITI group still entered the food cup upon CS presentation—which corresponds to the goal-tracking behavior predicted by the model in this case—but this only lasted about 2 s , at which point they transitioned to the lever ( Fig 3B and 3D ) ., In sessions 4–10 , none of the distributions of behavioral indices were significantly shifted from zero when examining the CS period as a whole ( Wilcoxons; Response bias: μ = 0 . 27 , p = 0 . 83; Latency: μ = −0 . 05 , p = 0 . 13; Probability: μ = 0 . 08 , p = 0 . 16; PCA: μ = 0 . 02 , p = 0 . 82 ) or during the first half of the CS period ( Response bias: μ = −0 . 11 , p = 0 . 027; Probability: μ = −0 . 04 , p = 0 . 18; PCA: μ = −0 . 07 , p = 0 . 25 ) ; however , when examining the last 4 s of the CS period , distributions were significantly shifted in the positive direction ( Wilcoxons; Response bias: μ = 0 . 32 , p < 0 . 05; Probability: μ = 0 . 28 , p < 0 . 05; PCA: μ = 0 . 24 , p < 0 . 05 ) ., Together , this suggests that rats in the 60-s groups were largely goal tracking early in training and that over the course of training , goal-tracking tendencies did not disappear but became focused to early portions of the CS period , while sign-tracking behavior developed toward the end of the CS period , later in training ( Fig 3B and 3D; S4 Fig ) ., Interestingly , these results go again beyond the computational model and suggest that it should be extended to account for within-trial behavioral variations ., Behavioral analyses clearly demonstrate that manipulation of the ITI impacts sign- and goal-tracking behavior and that both groups learned that the CS predicted reward ( Fig 3; S4 Fig ) ., Next , we determined how DA patterns changed during training ., Fig 3E and 3F illustrate DA release averaged across the first 3 d and days 4–10 of sessions with 120-s and 60-s ITIs , respectively , and DA release for each session is plotted in Fig 3G and 3H ., As shown previously , both groups started with modest DA release to both the CS and US during the first session ( Fig 3G and 3H; trial 1 ) ., For the 120-s ITI group , DA release was significantly higher to CS presentation later ( red ) compared to earlier ( pink ) in learning ( Fig 3E; t test: t = 2 . 51 , df = 119 , p < 0 . 05 ) ., DA release during US delivery did not significantly differ between early and late phases of training ( t test: t = 1 . 27 , df = 119 , p = 0 . 21 ) ., Hence , similarly to the sign trackers in the original study of Flagel and colleagues ( 2011 ) , the increase of DA response to the CS is consistent with the RPE hypothesis ., The difference is that here , the increase in the time available to down-regulate the value associated with the food cup during the ITI may have resulted in a remaining positive surprise at the time of reward delivery , hence preventing the progressive decrease of response to the US across training , in accordance with the model predictions ., In the 60-s ITI group ( Fig 3F and 3H ) , DA release to the US was initially high during the first 3 d ( turquoise ) but declined during days 4–10 ( blue ) ., Directly comparing DA release during the first 3 d with the remaining days revealed significant differences during the US period ( t test: t = 1 . 14 , df = 59 , p < 0 . 05 ) but not the CS period ( t test: t = 0 . 08 , df = 59 , p = 0 . 93 ) ., As a consequence , their post-training DA pattern—with a high response to the CS but no response to the US ( Fig 3F , blue ) —now resembles the traditional RPE pattern ( i . e . , high CS DA and low US DA after learning ) ., This is a clear demonstration that the DA RPE signal can be observed in goal trackers with a manipulation of the ITI , as predicted by the STGT model ., In a final analysis , we examined DA patterns during pure sign and goal tracking within each ITI group ., For this analysis , we examined only sessions during which either the lever was pressed or the food cup was entered during the cue period ., As shown previously 4 , phasic DA responses were apparent during both the CS and US during sessions with goal tracking ( Fig 3I and 3J , GT = orange ) ., In addition to replicating previous results , the figure also illustrates modulation of the DA pattern in line with model predictions ., Specifically , it shows that the DA response to the US was higher in the 120-s group than in the 60-s group during both sign- and goal-tracking sessions ( sign-tracking: t test , t = 3 . 66 , df = 25 , p < 0 . 05; goal-tracking: t test , t = 1 . 44 , df = 29 , p = 0 . 16 ) and that the DA response to the US was significantly lower than the DA response to the CS in GTs of the 60-s group ( t = 3 . 87 , df = 17 , p < 0 . 05 ) , suggesting that even though there is still a DA response to the US , shortening the ITI reduced the US-evoked DA response compared with what has been previously reported 4 ., The results reported here support the STGT model’s predictions that manipulating the ITI would impact the proportion of sign-tracking ( STs ) and goal-tracking ( GTs ) behaviors as well as DA release ., It predicted that shortening the ITI would result in fewer negative revisions of the food cup value and reduce the US DA burst ., It also predicted that the resulting higher food cup value would lead to an increase in the tendency to GT across sessions 1 , 13 , which it did ., The model also predicted that lengthening the ITI would have the opposite effect ., We found that there were significantly more food cup entries during the ITI for the 120-s ITI group and that they showed an increased tendency to sign track ., Furthermore , we show that the time spent in the food cup during the ITI was positively correlated with the amplitude of the CS and US DA bursts for the 120-s ITI group , which is consistent with the hypothesis that lengthening the ITI to allow for more time to decrease the value of the food cup would result in stronger positive RPEs during the trial ., Consistent with the model , we claim that increased sign tracking and DA release result from the additional time spent in the food cup during the ITI ., Indeed , these were positively correlated ., Importantly , this impact of ITI manipulations had not been predicted by other computational models of sign trackers and goal trackers 11 , 14 ., However , several alternative explanations should be considered , which may have also contributed to observed changes in behavior and DA release ., For example , it has been shown that rewards delivered after longer delays yield higher DA responses to the US 15–17 and that uncertain reward increases sign tracking 18 ., Although the reward was highly predictable in our study ( i . e . , always delivered 8 s after cue onset ) , it is possible that uncertainty associated with US delivery impacted behavior and DA release ., Notably , it is likely that these factors are intertwined in that manipulating delays and certainty impact the number of visits to the food cup that are not rewarded , thus leading to a negative revision of the food cup , as predicted by the model ., Future work that modifies food cup entries without manipulating ITI length and rewards uncertainty is necessary to determine the unique contributions that these factors play in goal-/sign-tracking behavior and associated DA release ., Another explanation for increased sign tracking and DA release in the rats in the 120-s ITI group is the possibility that they learned faster than rats in the 60-s ITI group because of differing ratios between US presentations and the interval between the CS and US in that , the shorter the CS-US interval relative to the ITI , the faster the learning 19 ., In the context of our study it is difficult to determine which group learned faster ., Although rats in the 120-s ITI group did lever press more often early in training , rats in the 60-s ITI group made more anticipatory food cup entries during the cue period prior to reward delivery ., Furthermore , both food cup entries and lever pressing were present in the first behavioral session ( S4 Fig ) ., Thus , both groups appear to learn the CS-US relationship at similar speeds , but it is just that the behavior readout of learning differs across groups , making it difficult to determine which group learned the association faster ., In our opinion , our results suggest that rats in both groups learned at similar rates , much like sign and goal trackers do; however , future experiments and iterations of the model are necessary to determine what role the US-US/CS-US ratio plays in sign/goal tracking and corresponding DA release ., Standard RL 20 is a widely used normative framework for modelling learning experiments 21 , 22 ., To account for a variety of observations suggesting that multiple valuation processes coexist within the brain , two main classes of models have been proposed: MB and MF models 23 , 24 ., MB systems employ an explicit , although approximate , internal model of the consequences of actions , which makes it possible to evaluate situations by forward inference ., Such systems best explain goal-directed behaviors and rapid adaptation to novel or changing environments 25–28 ., In contrast , MF systems do not rely on internal models but directly associate stored ( cached ) values with actions or states based on experience , such that higher valued situations are favored ., Such systems best explain habits and persistent behaviors 28–30 ., Learning in MF systems relies on a computed reinforcement signal , the RPE ( actual minus predicted reward ) ., This signal has been shown to correlate with the phasic response of midbrain DA neurons that increase and decrease firing to unexpected appetitive and aversive events , respectively 9 , 31 ., Recent work by Flagel and colleagues 4 has questioned the validity of classical MF RL methods in Pavlovian conditioning experiments ., Their autoshaping procedure reported in that article was nearly identical to the one presented here in that a retractable-lever CS was presented for 8 s , followed immediately by delivery of a food pellet into an adjacent food cup ., The only major difference was that the length of the ITI in their study was 90 s ., In their study , they showed that in STs , phasic DA release in the NAc matched RPE signaling ., That is , the DA burst to reward that was present early in learning transferred to the cue after learning ., They also showed that DA transmission was necessary for the acquisition of sign tracking ., In contrast , despite the fact that GTs acquired a Pavlovian conditioned approach response , this was not accompanied by the expected RPE-like DA signal , nor was the acquisition of the goal-tracking conditioned response blocked by administration of a DA antagonist ( see also Danna and Elmer 10 ) ., To account for these and other results , Khamassi and colleagues 1 proposed a new computational model—the STGT model—that explains a large set of behavioral , physiological , and pharmacological data obtained from studies on individual variation in Pavlovian conditioned approach 2–8 ., Importantly , the model can reproduce previous experimental data by postulating that both MF and MB learning mechanisms occur during behavior , with simulated interindividual variability resulting from a different weight associated with the contribution of each system ., The model accounts for the full spectrum of observed behaviors ranging from one extreme—from sign tracking associated with a small contribution of the MB system in the model—to the other—goal tracking associated with a high contribution of the MB system in the model 12 ., Above all , by allowing the MF system to learn different values associated with different stimuli , depending on the level of interaction with those stimuli , the model potentially explains why the lever CS and the food cup might acquire different motivational values in different individuals , even when they undergo the same training in the same task 26 ., The STGT model explains why the RPE-like dopaminergic response was observed in STs but not GTs—the proposition being that GTs would focus on the reward-predictive value of the food cup , which would have been down-regulated during the ITI ., Furthermore , the STGT explains why inactivating DA in the core of the nucleus accumbens or in the entire brain results in blocking specific components and not others ., Here , the model proposes that learning in GTs relies more heavily on the DA-independent MB system , and thus DA blockade would not impair learning in these individuals 4 , 8 ., More importantly , the model has led to a series of new experimentally testable predictions that assess and strengthen the proposed computational theory and allow for a better understanding of the DA-dependent and DA-independent mechanisms underlying interindividual differences in learning 1 , 13 ., The key computational mechanism in the model is that both the approach and the consumption-like engagement observed in sign trackers ( STs ) on the lever and in goal trackers ( GTs ) on the food cup result from the acquisition of incentive salience by these reward-predicting stimuli ., Acquired incentive salience is stimulus specific: stimuli most predictive of reward will be the most “wanted” by the animal ., The MF system attributes accumulating salience to the lever or the food cup as a function of the simulated DA phasic signals ., In the model simulations , because the food cup is accessible but not rewarding during the ITI , a simulated negative DA error signal occurs each time the animal visits the food cup and does not find a reward ., The food cup therefore acquires less incentive salience compared with the lever , which is only presented prior to reward delivery ., In simulated STs , behavior is highly subject to incentive salience because of a higher weight attributed to the MF system than to the MB

Introduction, Results, Discussion, Materials and methods

Recent computational models of sign tracking ( ST ) and goal tracking ( GT ) have accounted for observations that dopamine ( DA ) is not necessary for all forms of learning and have provided a set of predictions to further their validity ., Among these , a central prediction is that manipulating the intertrial interval ( ITI ) during autoshaping should change the relative ST-GT proportion as well as DA phasic responses ., Here , we tested these predictions and found that lengthening the ITI increased ST , i . e . , behavioral engagement with conditioned stimuli ( CS ) and cue-induced phasic DA release ., Importantly , DA release was also present at the time of reward delivery , even after learning , and DA release was correlated with time spent in the food cup during the ITI ., During conditioning with shorter ITIs , GT was prominent ( i . e . , engagement with food cup ) , and DA release responded to the CS while being absent at the time of reward delivery after learning ., Hence , shorter ITIs restored the classical DA reward prediction error ( RPE ) pattern ., These results validate the computational hypotheses , opening new perspectives on the understanding of individual differences in Pavlovian conditioning and DA signaling .

In classical or Pavlovian conditioning , subjects learn to associate a previously neutral stimulus ( called “conditioned” stimulus; for example , a bell ) with a biologically potent stimulus ( called “unconditioned” stimulus; for example , a food reward ) ., In some animals , the incentive salience of the conditioned stimuli is so strong that the conditioned response is to engage the conditioned stimuli instead of immediately approaching the food cup , where the predicted food will be delivered ., These animals are referred to as “sign trackers . ”, Other animals , referred to as “goal trackers , ” proceed directly to the food cup upon presentation of the conditioned stimulus to obtain reward ., Understanding the mechanisms by which these divergent behaviors develop under identical environmental conditions will provide powerful insight into the neurobiological substrates underlying learning ., Here , we test predictions made by a recent computational model that accounts for a large set of studies examining goal-/sign-tracking behavior and the role that dopamine plays in learning ., We show that increasing the duration of the time between trials leads more to the development of a sign-tracking response and to the release of dopamine in the nucleus accumbens ., During conditioning with shorter intertrial intervals , goal tracking was more prominent , and dopamine was released upon presentation of the conditioned stimulus but not during the time of reward delivery after training ., Thus , shorter intertrial intervals restored the classical dopamine reward prediction error pattern ., Our results validate the computational hypothesis and open the door for understanding individual differences to classical conditioning .

learning, medicine and health sciences, neurochemistry, chemical compounds, classical conditioning, vertebrates, social sciences, conditioned response, neuroscience, animals, mammals, learning and memory, organic compounds, hormones, animal models, surgical and invasive medical procedures, model organisms, cognitive psychology, mathematics, functional electrical stimulation, probability distribution, membrane electrophysiology, experimental organism systems, amines, neurotransmitters, bioassays and physiological analysis, catecholamines, dopamine, research and analysis methods, animal studies, behavior, chemistry, electrophysiological techniques, short reports, probability theory, biochemistry, behavioral conditioning, rodents, psychology, eukaryota, electrode recording, organic chemistry, biogenic amines, biology and life sciences, physical sciences, cognitive science, amniotes, organisms, rats

journal.pcbi.1005657

β-adrenergic signaling broadly contributes to LTP induction

Synaptic plasticity is one of the cellular mechanisms underlying learning and memory ., In the hippocampus , long-term potentiation ( LTP ) has been implicated not only in acquisition , consolidation and retrieval of spatial memories , but also contextual fear extinction 1–4 ., Several neuromodulatory systems contribute to both synaptic plasticity and fear memory 5 , including pathological memory retention such as post-traumatic stress disorder ( PTSD ) ., One of the most potent regulatory systems is the noradrenergic system , which is activated by arousal , emotion and stress ., Experimental evidence shows that norepinephrine is elevated in the hippocampus in mouse models of PTSD 6 , 7; however , its contribution to long term plasticity is unclear and this lack of knowledge hinders the development of treatments for fear memory disorders ., Numerous experiments investigating long-lasting LTP have revealed the requirement for a plethora of signaling molecules ( reviewed in 5 , 8 ) ., Experimental protocols that induce long-lasting LTP activate diverse signaling pathways , which may interact competitively or cooperatively ., For example , long-lasting LTP evoked by multiple trains of high-frequency electric stimulation requires protein kinase A ( PKA ) only if the inter-train interval is greater than 60 sec 9 , 10 ., These networks of signaling pathways may converge on common targets , such as extra cellular regulated kinase ( ERK ) , which is required for most forms of long-lasting LTP 11–14 ., Alternatively , some components of those signaling pathways are location specific and function in restricted spatial compartments such as spines or dendritic submembrane ., Those observations pose the key question of whether this diversity of mechanisms can be explained by collectively considering the combined molecular network ., Another type of unexplained diversity of mechanisms underlying induction of long-lasting LTP is introduced by neuromodulation ., To date , β-adrenergic receptor ( βAR ) activation has been considered essential for only a subset of experimental protocols , usually for weak electric stimulation ., Conversely , commonly used βAR antagonists , such as propranolol , do not affect long-lasting LTP elicited by strong electric stimulation ., The idea that βAR activation is not essential for long-lasting forms of LTP was undermined by recent experiments suggesting that conventional βAR antagonists do not block all downstream signaling pathways ., Though βARs typically are coupled with stimulatory G protein ( Gs ) , phosphorylated βARs decouple from Gs and couple with inhibitory G protein ( Gi ) ., Both Gs-activated and βAR coupled to Gi-activated signaling pathways converge on a common target , ERK 15–17 , which is required for long-lasting LTP ., The ability of propranolol to recruit ERK 18 , suggests that long-lasting LTP evoked by strong stimulation with or without propranolol might require βAR signaling to ERK ., This hypothesis is supported by recent experiments showing that a complete βAR antagonist blocks long-lasting LTP induced by strong electric stimulation 19 ., Therefore , βAR activation might play a pivotal role for many forms of long-lasting LTP ., To investigate whether the diverse experimental evidence can be unified by considering activation of multiple signaling cascades and address the role of βAR activation in occurrence of long-lasting LTP , we develop a spatial , mechanistic model of signaling pathways underlying induction of long-lasting forms of LTP ., We evaluate spatio-temporal dynamics of key kinases that activate molecular pathways reported to play an essential role in long-lasting forms of LTP ., We show that the combined elevation of several molecules in the spine and in the dendrite can predict the induction of long-lasting LTP , and our results suggest that activation of the βARs may be essential for all forms of LTP ., These findings may help unravel the contribution of the noradrenergic system to learning and memory and help with the development of treatments for fear and anxiety disorders ., Different forms of LTP are evoked by different stimulation patterns 36; thus , we performed simulations using seven , well characterized , stimulation protocols ( Table 1 ) ., Four of them experimentally elicit L-LTP , one results in an early form of LTP ( E-LTP ) and the remaining two stimulation protocols do not produce LTP , though one ( LFS ) elicits brief depression ., Electric stimulation of Schaeffer collaterals results in activation of post-synaptic NMDA receptors and action potentials , thus each stimulation pulse was simulated in the model as calcium injection both into the spine to represent NMDA receptors , and into the dendrite to represent activation of voltage dependent calcium channels ., Electric stimulation in the hippocampus is accompanied by norepinephrine release 37 , which was modeled as ligand influx ., Bath applied isoproterenol ( ISO ) was simulated by injecting sufficient ISO to produce a 1 μM concentration ., We started stimulation after 300 sec of simulation to ensure the model had reached equilibrium ., Steady state was confirmed by running simulations for 900 sec in the absence of stimulation and visually assessing that activity of each molecular specie was stationary ., Several data sources were used to adjust calcium pulse amplitudes for all stimulation protocols ., To stimulate calcium influx during 100 Hz trains of electric stimulation ( HFS ) , we used release probabilities from 38 which provides changes in the amplitudes of calcium pulses in the spine during high frequency trains ., We assumed that amplitudes of consecutive calcium pulses in the dendrites are uniform , because they result from full height action potentials ., To calculate absolute amplitudes of calcium pulses , we constrained calcium concentration in the spine and in the dendrite to match experimental data 39: 10 μM in the spine and 2 μM in the dendrite ., This pattern of calcium pulses was used in all stimulation protocols using trains of HFS: 1 train of 100 Hz ( HFS ) , four trains of 100 Hz given 3 sec apart ( 4xHFS-3s ) , four trains of 100 Hz given 80 sec apart ( 4xHFS-80s ) and bath applied ISO followed by 1 train of 100 Hz ( ISO+HFS ) ., For the 5 Hz ( LFS ) stimulation protocol , spine calcium pulses were of the same amplitude , and equal to the amplitude of the first pulse of the HFS train 39 ., In order to estimate the temporal pattern and amplitude of neuromodulation elicited by electric stimulation , we used a model ( Eq, 1 ) describing vesicle release 40 ., This model assumes that synaptic resources can be found in three states: inactive ( I ) , recovered ( R ) and effective ( E; released ) ., u represents release probability , which decays with a time constant τf and increases with each action potential ( AP ) by a fraction of USE ., After the arrival of the AP a fraction of recovered resources ( uR ) becomes effective ( E ) i . e . gets released ., Effective resources , E , become inactive with a time constant τi ., Inactive resources , I , recover with a time constant τr ., The Dirac delta function is denoted as δ ( t − tAP ) and has value 1 at t = tAP and 0 otherwise ., ASE is the absolute synaptic efficacy ( response amplitude produced by complete release of all the neurotransmitter ) ., We tuned the vesicle release model on experimental data to voltammetric measurements of norepinephrine release in the rat Ventral Bed Nucleus Stria Terminalis following electric stimulation of noradrenergic projection pathways 41 ( S6 Table ) ., Using this model we estimated norepinephrine release for stimulation patterns in Table 1 ., The spatial distribution of norepinephrine during and following release was in agreement with a spatial gradient of neuromodulators 42 , namely a high concentration in the spine release site ( 1 μM ) and lower at the dendrite ., PKA phosphorylates NMDA receptors , which increases the amplitude of calcium influx through these receptors 43 , 44 ., This enhancement of NMDA mediated calcium influx has been observed with bath application of ISO ., Thus , for the case of ISO+LFS , calcium influx was increased by 50% 45 ., We modeled propranolol ( 1 μM; 46 ) ICI-118 , 551 ( 100 nM; 19 ) and carvedilol ( 10 μM 47 ) by allowing it to bind the β2AR 48 ( S1 Table ) , and then both propranolol- and carvedilol-bound β2AR were able to bind with Gi and form a target representing ERK activation ., Binding affinity was constrained so that carvedilol produces one third the Gi bound β2AR compared to that of isoproterenol as has been measured experimentally 18 ., We used a stochastic simulation technique , as many molecular populations are small ., In such case activations fluctuate greatly about the mean within such small compartments 49 , 50 ., Similarly , diffusion of second messenger molecules out of the spines and along the thin dendrites is subject to random variation ., The model was implemented using an efficient mesoscopic stochastic reaction-diffusion simulator NeuroRD 51 , version 2 . 1 . 10 , because the large numbers of molecules in the morphology described ( Fig, 2 ) made tracking individual molecules in microscopic stochastic simulators computationally expensive ., This simulator uses reflective boundary conditions ( molecules attempting to diffuse out of the morphology were reflected back into the morphology ) ., Model simulations used a time step of 2 . 9 μs ., A single simulation of 900 sec ( of the dendrite with 1 spine ) takes 4 . 5 days on a Intel Xeon CPU E5-2620 2 . 00GHz processor ., Based on results from our prior studies , simulations were repeated four or eight times using a different random seed ., Eight simulations were used for stimulation protocols whose signature exhibited a large standard deviation relative to the mean ., To determine whether the combination of stimulation and βAR ligand would induce L-LTP or not , we analyzed the duration of combined molecular activations ( signatures ) in the spine and in the dendrite above their respective thresholds ., The statistical analysis used SAS ( version 9 . 4 , SAS Institute , NC ) ., Student’s T test ( SAS procedure TTEST ) was applied to each condition to evaluate whether the duration above threshold was significantly greater than the duration threshold of 10 sec ., For the multi-spine simulations , we used the SAS procedure GLM to perform a two-way analysis of variance using condition ( adjacent or separate ) and stimulation ( spine was stimulated or not ) as factors ., All model simulation files are available from modelDB ( https://senselab . med . yale . edu/ModelDB/showModel . cshtml ? model=190304 ) ., We validated the model by comparing activity of AMPA receptor ( AMPAR ) phosphorylation and PKA-mediated Gs-Gi switching with independent , published experimental results ., To validate the PKA-mediated Gs-Gi switching we simulated bath application of 1 μM of isoproterenol in a model over-expressing Epac ( 8 time the amount used in other simulations ) ., The model’s Epac activity was compared with the response of genetically encoded Epac-sh150 ( monitoring cAMP activity in hippocampal CA1 neurons ) to 1 uM ISO 52 ., Fig 3 shows the model’s Epac activity and fluorescence traces of distal dendrites in response to bath application of 1 μM of isoproterenol ( ISO ) and confirms that the model accurately captures the decay in cAMP activity while isoproterenol is still present due to phosphorylation of βARs ( and phosphodiesterases ) ., For comparison we chose fluorescence traces of small , tertiary dendrites , which had similar diameter to the dendrite diameter used in the model ., Next we validated the model of AMPAR phosphorylation by comparing phosphorylation AMPAR at Serine 845 and Serine 831 to experimentally measured values ., In the model bath application of 1 μM of ISO yields 200% increase in phosphorylation of Serine 845 and no discernible phosphorylation of Serine 831 , which is in agreement with values reported in hippocampal CA1 neurons after bath applying 1 μM of ISO 53 , 54 ., These comparisons confirm the parameters describing inactivation mechanisms ( both Gs-Gi switching and PDE4 phosphorylation ) of cAMP and PKA activity for AMPAR ., We quantified the spatio-temporal dynamics of molecular species that are known to play a role in the induction of long-lasting forms of LTP , including PKA 9 , 12 , 55 , 60 , calcium-calmodulin-dependent protein kinase II ( CaMKII ) 58 , 61–63 and exchange protein directly activated by cAMP ( Epac ) 14 ., These molecules were activated either by calcium pathways or by the βAR coupling either to Gs or Gi ., We empirically determined two equations that we called ‘signatures’ to predict the occurrence of long-lasting LTP ., The first one summed normalized activity of key molecular species in the spine , the second one summed normalized activity of key molecular species in the dendrite ., We assumed that if the experimental protocol enhanced activity of key molecular species in the spine , then spine specific changes would be induced and , similarly , if the experimental protocol enhanced activity of key molecular species in the dendrite , then dendrite specific changes would be induced ., To evoke long-lasting forms of LTP both spine specific and dendrite specific changes needed to be induced ., The spine molecular signature trace ( referred to as the spine signature ) evaluates the initiation of plasticity processes in the spine by calculating time dependent increases in CaMKII , Epac , and PKA activity in the spine:, S spine ( t ) = Δ pCaMKII ( t ) max Δ pCaMKII + Δ Epac ( t ) max Δ Epac + Δ PKA ( t ) max Δ PKA ( 2 ), where ΔEpac ( t ) is the fold increase in cAMP bound Epac , ΔpCaMKII ( t ) is fold increase in phosphorylated CaMKII , ΔPKA ( t ) is the fold increase in phosphorylation of PKA targets ., Max ΔX is a normalization value equal to the maximum activation of molecular specie X among the seven control protocols , where the maximum activation was calculated as the mean ( over trials ) of the peaks ( for each trial ) ., If the spine signature exceeds its threshold for more than 10 sec , spine-specific changes are induced ., The dendritic signature represents spatially non-specific plasticity processes , and takes into account molecular species: PKA , Epac and CaMKII:, S dendrite ( t ) = Δ Epac ( t ) max Δ Epac + Δ pCaMKII ( t ) max Δ pCaMKII + Δ ( pInhibitor 1 ( t ) + pPDE 4 ( t ) ) max Δ ( pInhibitor 1 + pPDE 4 ) + Δ G i ( t ) max Δ G i , ( 3 ), For the dendritic signature , the PKA activity is subdivided into two terms: inhibitory G protein ( Gi ( t ) ) which represents phosphorylated β2AR , and other phosphorylated PKA targets: Inhibitor-1 and PDE4 ., We have subdivided the PKA activity into these two parts to evaluate the role of Gs-Gi switching ( and β-arrestin ) in synaptic plasticity , and also to evaluate the role of novel β2AR antagonists ., Δ ( pInhibitor1 ( t ) + pPDE4 ) represents PKA phosphorylation of other phosphoproteins included in the model for LTP induction ., If the dendritic signature exceeds its amplitude threshold for more than the 10 sec duration threshold , dendrite-specific changes are induced ., We chose a relatively short duration threshold as it has been shown that the temporal window of CaMKII activation required for synaptic plasticity and learning is narrow 64 , less than 1 minute ., To induce long-lasting forms of LTP , both the spine- and dendrite-specific changes must be induced ., The spatial approach allowed us to monitor changes in the phosphorylation of the AMPA receptor subunit GluA1 ( AMPAR ) in the PSD ( Fig 2 ) ., We monitored AMPAR phosphorylation ( pAMPAR ) because it is correlated with E-LTP 70 ., To evaluate induction of E-LTP for the seven control protocols , the only additional parameter added was a threshold on AMPA receptor phosphorylation ., HFS , ISO+HFS , 4xHFS-3s , 4xHFS-80s and ISO+LFS each cause three-fold increases in phosphorylation of AMPA receptors resembling E-LTP ( Fig 10A ) , whereas ISO causes a smaller increase in phosphorylation of AMPA receptors ( Fig 10B ) , which is in agreement with 71 ., Thus , though explaining E-LTP was not a goal of the model , an emergent property was that the model correctly predicts the development of E-LTP ., Because only a single additional parameter was added to evaluate the outcomes of seven stimulation protocols , these results are considered an additional validation of the model ., A question of major importance for information processing is which events triggered by synaptic plasticity are spatially specific ., Recent experiments using glutamate uncaging at single spines suggest that uncaging induced structural plasticity is spine specific 72 ., On the other hand , some molecules , such as Ras , can diffuse into nearby spines , reducing the threshold for LTP at those spines 73 , 74 ., In addition to spatial specificity , other experiments suggest that stimulation of multiple spines may either cooperate with each other 75 or compete for resources 74 ., Thus , the next set of simulations investigated whether electrically induced synaptic plasticity exhibits spatial specificity , i . e . , what is the extent of diffusion of key molecules to adjacent spines ., We used a 20 μM dendrite with 8 dendritic spines , applied 4xHFS-80s and evaluated stimulation of two adjacent spines ( 1 . 5 μm apart ) and two non-adjacent ( 8 μm apart ) , i . e . separated , spines ., Because the model is intrinsically a spatial model , extension of the morphology to a larger dendrite with additional spines requires no changes to reaction rates , molecule concentrations and surface densities , or the equation and thresholds for the signatures ., Both stimulation of separated and adjacent spines produce spine and dendritic signatures that exceed the threshold , and thus are able to induce L-LTP ., Fig 11C shows that the dendritic signature exceeds the threshold throughout the dendritic branch ., In contrast , Fig 11B reveals some degree of spatial specificity in the spine signature ., Statistical analysis shows that for both adjacent and separated spine stimulation , molecular signatures of stimulated spines is greater than molecular signature of unstimulated spines ( GLM , stimulus spacing and stimulation as factors , F ( 2 , 61 ) = 163 , F > . 0001; factor stimulation:P < 0 . 0001 , factor spacing: P = 0 . 623 ., For both adjacent and separated spine stimulation , the duration of the spine signature above threshold of stimulated spines is significantly greater than the duration threshold 10 sec , ( t-test , T ( 7 ) < 0 . 0001 for both adjacent and separated spine stimulation ) ., In contrast , spine signatures of unstimulated spines are not above threshold for greater than 10 sec ( t-test , T ( 7 ) = 0 . 9 for upper threshold , 0 . 06 for lower threshold for separated spine stimulation; T ( 7 ) = 0 . 79 for upper threshold , 0 . 016 for lower threshold for adjacent spine stimulation ) ., For both adjacent and separated stimulation protocols , the CaMKII and Epac of the non-stimulated spines is lower than that of the stimulated spine , which is consistent with the gradients observed experimentally 76 ., The ability to predict long-lasting forms of LTP does not depend on the precise details of the molecular signatures; instead the LTP predictions are similar for a range of thresholds , and for slight variations in the signature equations ., The kinase-to-phosphatase balance , evaluated by molecular signatures , is thought to control direction of synaptic plasticity 36 ., There are at least two ways of assessing this balance: either measuring the quantity of phosphorylated targets of kinases and phosphatases Eq ( 2 ) , or assessing a ratio of kinase activity to phosphatase activity ., Importantly , LTP predictions of our model are similar when the spine molecular signature evaluates the ratio of kinases ( CaMKII and PKA ) to phosphatases ( PP1 and PP2B ) ( S7 Table ) ., The figures show a threshold range to demonstrate that the model makes the same predictions for any threshold value between the upper and lower thresholds , and does not require a precisely set threshold ., To further assess robustness of our results , we evaluated individual simulations ( realizations of protocols ) , that were executed with different random seeds ., Note that the stochastic simulation includes a variation in injected quantity , which propagates ( in some pathways with amplification ) to yield as much as 30% variation in quantity of molecule activation ., Tables 2 and 3 show that , despite variability in the time-course , the signatures for each realization of the long-lasting LTP eliciting protocols cross their thresholds for more than 10 sec uninterrupted ., Further analysis ( Tables 2 and 3 ) shows that these results are statistically significant ., In addition increasing the time the spine signature remains over the threshold to 15 sec , does not significantly change the number of individual simulations that exceed the spine threshold ( S8 Table ) ., To further evaluate robustness of the results , we repeated simulations with variations of two sets of parameters ., The first set of parameter variations lowered both AC and PDE4 concentration by 30% ., The second set of parameter variations increased AC concentration by 30% and PDE4 concentration by 20% ., In both cases , AC and PDE4 quantities were varied together to maintain a 30 nM basal cAMP concentration ., Fig 12 shows the mean duration that the spine or dendritic signatures remained above their respective thresholds ., Though the signatures varied significantly with parameter variation and trial ( as shown by the standard error of the mean ) , in all cases both signatures were exceeded only for those stimulation protocols that experimentally yield LTP ., It is also worth noting that simulations of models with higher AC levels were more noisy because of competition for calmodulin ., To predict long-lasting forms of LTP we developed a stochastic reaction-diffusion model of a dendrite with spines ., We looked at activity of the key molecular species during the first 10 min following plasticity induction , because long-lasting LTP is blocked by protein kinase inhibitors applied during or immediately after induction of LTP 57 , 77 ., A relatively short duration above the threshold is in agreement with 64 , showing that temporal window of CaMKII activation required for synaptic plasticity and learning is narrow ., We devised a set of molecular signatures: one in the spine and one in the dendrite , that predict induction of long-lasting forms of LTP ., We demonstrated that two molecular signatures can explain the results of a large number of experimental protocols ., Additional simulations suggested the complex role of the βAR activation in long-lasting forms of LTP ., The spatial aspect of these simulations was critical , as a single molecular signature that calculated a spatial average of molecular activity was unable to predict the induction of all forms of long-lasting forms of LTP ., Fig 12 clearly shows that the relationship between the dendritic signature and the spine signature depends on the stimulation protocol ., Separate molecular signatures in the spine and in the dendrite represent distinct phenomena ., Two signatures can be viewed as corresponding to synaptic tagging and capture 63 , 65 , a theory explaining how signaling molecules in different spatial compartments play different roles in L-LTP ., Synaptic tagging involves labeling of specific dendritic spines that are to undergo long term plasticity , and capture implies that a spatially non-specific signal induces synthesis of plasticity related proteins ( PRPs ) , and in some cases , initiates transcription 78 ., PRPs are synthesized locally or trafficked up the dendrite and captured by tagged spines to stabilize synaptic strength ., Crossing the threshold by the spine molecular signature can be viewed as setting the tag and crossing the threshold by the dendritic molecular signature corresponds to sending the signal initiating the synthesis of PRPs ., In constructing the spine molecular signature , we evaluated molecules that are implicated in synaptic tagging , AMPA receptor insertion , actin remodeling and structural plasticity 72 , 79–82 ( Fig 13 ) ., Blocking CaMKII activity 61–63 has been shown to block tagging , and CaMKII also is implicated in the actin remodeling underlying structural plasticity 83–85 by triggering SynGAP dispersion from synaptic spines 86 ., PKA is required for synaptic tagging 56 , 66 , 87 , 88 and is implicated in structural plasticity ., PKA modulates the activity of LIM kinase 89 , 90 , which phosphorylates ( and inhibits ) cofilin allowing for actin polymerization ., Cofilin-mediated actin dynamics regulates spine morphology and AMPAR trafficking during synaptic plasticity 91 , 92 ., Epac anchors in the PSD 93 and triggers changes to spine cytoskeleton via Rap1 activation 94 ., Interestingly , synapses stimulated by HFS while blocking PKA activity fail to be tagged 88 , whereas ISO+HFS stimulation while blocking PKA still yields L-LTP 14 ., Our simulations suggest that this seemingly contradictory result arises from the difference between the amount of Epac provided by HFS alone versus ISO+HFS ., The plausibility of the spine signature is evident from its time course , which is comparable to the dynamics of molecular activation measured using live cell imaging 80 ., The molecular signature in the dendrite takes into account molecules that play a role in synthesis of PRPs ( Fig 13 ) ., Both PKA and Epac activate ERK via Rap1 regulation 95–98 ., Also , PKA phosphorylation of β2AR can produce ERK activation by switching the β2AR coupling from Gs to Gi 15–17 , though this has not been directly demonstrated in neurons ., ERK has been shown to be critical in L-LTP 12 , 13 , 55 , 99–101 and the synthesis of PRPs 61 ., Both PKA and ERK can phosphorylate CREB , a molecule directly implicated in transcription ., CaMKII is required for regulation of protein synthesis via phosphorylation of cytoplasmic polyadenylation element binding protein 102 , 103 in hippocampal plasticity , but see 61 , 62 ., Though both spine and dendritic signatures incorporated the same molecules , they have different downstream targets in the spine and in the dendrite ., Thus the two molecular signatures set the stage for future models that incorporate control of actin dynamics in the spine and ERK activation in the dendrite ., Several other models have evaluated molecular dependence and temporal sensitivity of L-LTP induction ., The most comprehensive model of signaling pathways leading to transcription of mRNA 104 demonstrated that different temporal stimulation patterns could recruit different mRNAs ., In agreement with their results , our simulations showed that different stimulation patterns produced different patterns of elevation of various kinases ., It would be quite interesting to couple our dendritic model to downstream modules of the model presented in 104 to evaluate control of transcription by L-LTP stimulation patterns ., Several other models investigated synaptic tagging and capture 105–107 at hippocampal CA3-CA1 synapses ., All of these models were able to predict various aspects of the synaptic tagging and capture hypothesis ., Nonetheless , these models used simplified and abstract equations for activation of key kinases and phosphatases; thus it is not clear how well they could extrapolate to alternative stimulation patterns ., Another model 108 also used streamlined equations for activation of key kinases and phosphatases , but included a model of histone deacetylation , which regulates transcription 109 ., That model suggested that promoting histone acetylation while simultaneously slowing cAMP degradation could help in restoring L-LTP , which is impaired in mouse models of Rubinstein-Taybi syndrome , a condition resulting in lower levels of CREB binding protein , which reduces transcription ., Our simulations of a dendrite with multiple spines are consistent with the spatial specificity of homo- and heterosynaptic plasticity suggested by imaging of spine morphological plasticity ., Stimulation of two spines on the same branch produces a dendritic signature that crosses the threshold along the entire branch , regardless of the spatial configuration of those stimulated spines ., This result is consistent with 75 , showing that one train of 5 Hz stimulation applied to two spines on the same branch saturates ERK activation in that branch ., During these simulations , spine signatures of the unstimulated spines are elevated , although lower than those of the stimulated spines ., This observation is consistent with the gradients observed experimentally 76 ., Furthermore , the increase in signature of non-stimulated spines is consistent with the observation of a reduced LTP threshold heterosynaptically 73 ., It is , however , also possible that not all spines will exhibit potentiation due to competition for resources , as in 74 ., Our model does not take into account this competition , but such a model would allow only the spines with the highest signatures to capture PRPs , and thus non-stimulated spines with lower signatures would not exhibit LTP ., The agreement between these simulations and experiments suggests the model could be used to predict the spatial pattern of LTP in response to in vivo like stimulation patterns ., We evaluated AMPAR phosphorylation by CaMKII and PKA as an indicator of E-LTP , and found agreement between our simulations and experimental results 70 , 110 , 111 ., The brief duration of the AMPAR phosphorylation in our model is likely due to absence of AMPAR re-cycling mechanisms 112 ., Previous work has shown AMPAR recycling contributes to bistability 113 , and insertion of a phosphorylated AMPAR may protect it from dephosphorylation ., Alternatively , AMPAR phosphorylation may only be a trigger for insertion , and the time course of E-LTP may reflect the removal of AMPARs in the synapse ., Induction of long-lasting LTP initiates a cascade of complex molecular interactions; therefore signaling pathway modeling is a useful approach to facilitate understanding of this complexity ., In addition to confirming the plasticity outcome and molecular dependence for numerous LTP induction protocols , our model makes several experimentally testable predictions ., Our model suggests that βAR signaling through non-conventional pathways is necessary in the dendrite , therefore ICI-118 , 551 , a complete βAR antagonist , will likely block long-lasting LTP induced with 4xHFS-80s , a model prediction that needs to be tested experimentally ., Moreover , the model suggests that both conventional ( Gs-activated ) and non-conventional ( Gi-activated ) pathways are required for ISO+LFS and ISO+HFS to produce long-lasting LTP , therefore we predict that bath application of carvedilol , which blocks norepinephrine binding but allows Gi recruitment , will not induce long-lasting LTP ., Simulations of bath application of carvedilol followed by one , two and three trains of HFS shows that high enough calcium might substitute for Gs activation in L-LTP induction , but that both Gs and Gi might be necessary for L-LTP induction using LFS ., Though our model focuses on βAR signaling , CA1 neurons express dopamine receptors , which have been implicated in some forms of long-lasting LTP 114 ., If such receptors are shown to undergo switching of Gs to Gi coupling , then these receptors also may contribute to a plethora of long-lasting forms of LTP ., In summary , our model suggests that the non-linearity of signaling pathway interactions may explain why experimentally blocking any of the molecules included in our signature can disrupt long-lasting LTP .

Introduction, Materials and methods, Results, Discussion

Long-lasting forms of long-term potentiation ( LTP ) represent one of the major cellular mechanisms underlying learning and memory ., One of the fundamental questions in the field of LTP is why different molecules are critical for long-lasting forms of LTP induced by diverse experimental protocols ., Further complexity stems from spatial aspects of signaling networks , such that some molecules function in the dendrite and some are critical in the spine ., We investigated whether the diverse experimental evidence can be unified by creating a spatial , mechanistic model of multiple signaling pathways in hippocampal CA1 neurons ., Our results show that the combination of activity of several key kinases can predict the occurrence of long-lasting forms of LTP for multiple experimental protocols ., Specifically Ca2+/calmodulin activated kinase II , protein kinase A and exchange protein activated by cAMP ( Epac ) together predict the occurrence of LTP in response to strong stimulation ( multiple trains of 100 Hz ) or weak stimulation augmented by isoproterenol ., Furthermore , our analysis suggests that activation of the β-adrenergic receptor either via canonical ( Gs-coupled ) or non-canonical ( Gi-coupled ) pathways underpins most forms of long-lasting LTP ., Simulations make the experimentally testable prediction that a complete antagonist of the β-adrenergic receptor will likely block long-lasting LTP in response to strong stimulation ., Collectively these results suggest that converging molecular mechanisms allow CA1 neurons to flexibly utilize signaling mechanisms best tuned to temporal pattern of synaptic input to achieve long-lasting LTP and memory storage .

Long-term potentiation of the strength of synaptic connections is a mechanism of learning and memory storage ., One of the most confusing aspects of hippocampal synaptic potentiation is that numerous experiments have revealed the requirement for a plethora of signaling molecules ., Furthermore the degree to which molecules activated by the stress response modify hippocampal synaptic potentiation and memory is still unclear ., We used a computational model to demonstrate that this molecular diversity can be explained by considering a combination of several key molecules ., We also show that activation of β-adrenergic receptors by the stress response appears to be involved in most forms of synaptic potentiation , though in some cases unconventional mechanisms are utilized ., This suggests that novel treatments for stress-related disorders may have more success if they target unconventional mechanisms activated by β-adrenergic receptors .

phosphorylation, medicine and health sciences, neurochemistry, chemical compounds, enzymes, enzymology, neuroscience, organic compounds, surgical and invasive medical procedures, hormones, phosphatases, synaptic plasticity, functional electrical stimulation, amines, neurotransmitters, catecholamines, calcium signaling, neuronal dendrites, norepinephrine, developmental neuroscience, animal cells, proteins, chemistry, biochemistry, signal transduction, cellular neuroscience, cell biology, post-translational modification, organic chemistry, neurons, biogenic amines, biology and life sciences, cellular types, physical sciences, cell signaling

journal.pcbi.1006545

Prediction and classification in equation-free collective motion dynamics

First , we applied the basic DMD to a simple model of schooling ( see Materials and Methods and Fig 1 ) to verify that our approach can extract and discriminate the global dynamics of collective motions from observed data without the prior knowledge about the labeled motions ., Although the model only employs the simple and explicit local interaction rules of repulsion , alignment , and attraction 2 , complex collective motion emerges because of the resultant many-body interactions ., For example , changes in the parameter interaction rule ro , which is the radius of the zone of orientation between the repulsion and attraction zones ( detail in Materials and Methods ) , can determine whether the collective motion takes the form of a swarm , torus , or parallel behavior ( Fig 2 left and S1–S3 videos: ro increasing in this order ) ., For generating the three distinct behavioral shapes , we adopted the simple model 2 only based on the specific distance called metric framework , rather than more realistic model based on the specific number of agents called topological framework 39 , 40 or the mixture model both with the metric and topological framework 41 ., Note that the particles moved at a constant speed ( 4 m/s with an individual variance ) ., This indicates that the particles are not being driven by Brownian motion ., DMD can extract spatially coherent ( global ) dynamic modes and also can estimate dynamic properties of the local interactions among the agents in a group ., We show the results using a distance-matrix time series between individuals and sorted by nearest neighbors ( Fig 2 middle ) at each time indicating three distinct dynamical properties ( the results inputting the distance with fixed individuals and the raw Cartesian coordinates in S4 Fig do not show the distinct properties ) ., It should be noted that input matrix sorted by nearest neighbors can more stably compute the DMD than the unsorted matrix ( see S4 Fig ) , because the temporal change in the distance matrices are more stable in the sorted matrix than the unsorted one ( see S1 Fig ) ., In the sorted matrix , we consider that the simulated agents do not discriminate each other ( the orders of the component of the matrix change over time ) as well as the metric ( and topological ) framework ., Moreover , noted that also in the metric framework , the neighbor agents move similarly in the specific zone ( i . e . , zone of orientation ) as well as in the topological framework ., In case of the nearest sorted distance matrix , the results in temporal DMD mode exhibited a relatively wide and strong spectrum , a wide and weak spectrum , and a narrow but strong spectrum for the swarm , torus , and parallel behaviors , respectively ( Fig 2A , 2D and 2G ) ., For the spectrum of the parallel behavior , low frequency ( 0 . 5–1 . 5 Hz ) peaks may indicate alignments and transient interactions resulting from collision with the wall , because the additional simulations without a boundary condition caused the high-frequency peak to vanish ( S3 Fig ) ., The power spectra of the spatial DMD modes 24 averaged by the DMD modes within the above frequency interval also show the distinct spatial properties for the three different behavioral shapes in the low ( 0 . 5–1 . 5 Hz ) and high frequency ( 2–3 Hz ) intervals ., As the temporal frequency spectra , the power spectra of the spatial DMD modes for the swarm ( Fig 2B and 2C ) , parallel ( Fig 2H and 2I ) , and torus behavior ( Fig 2E and 2F ) were stronger in this order ., Especially , the power spectra of the spatial DMD modes of the torus behavior were weak near the diagonal elements , i . e . , nearest agents to each other ., Note that all the DMD reconstructions were performed sufficiently ( see S2 Note and S5 Fig ) ., From a more general perspective , we examined wave properties of wavenumber and frequency using the longitudinal and transverse dynamic structure factors 26 , 29 which is a Fourier transform of the density-density correlation function in both space and time , and can separate into longitudinal and transverse modes 27 ( see Materials and Methods ) ., This analysis revealed that the longitudinal dynamic structure factor had shifting peaks for each wavenumber under the three types of behavior ( Fig 3A–3C ) , and the dispersion relations between wavenumber and frequency in spectrum peaks were almost linear and equal among all collective motions ( Fig 3D ) ., This finding indicates that the three motions had pseudo-acoustic mode dynamics or sound-like propagation modes 42 ., Similarly , for the transverse dynamic structure factor , the dispersion relations were nearly linear ( S6 Fig ) , suggesting that the collective motion also shared properties with transverse waves , which did not occur ( or was damped ) in a normal fluid ., Thus , the collective motions in this study are similar to the motion of a viscoelastic fluid ., These dispersion relations were independent of the type of the emergent motion ( even without a boundary , as in S7 Fig ) and suggest that the wave properties in multiple spatiotemporal scales cannot discriminate the type of motion that emerges ., Instead , DMD analysis can extract the properties of dynamic interaction for the different collective motions ., Additional discrimination results including the existing specific parameters such as polarization and angular momentum 2 are shown in S10 Fig and S2 Note ., It should be noted that for the discrimination , the advantage of our method is that we do not need the prior knowledge about the labeled group behaviors ., As an application to actual complex collective behavior in a low-dimension input space , we used player-tracking data from actual basketball games ., Using DMD with reproducing kernels , we extracted the dynamic interaction properties , and predicted the probability of a shot ( Fig 4 ) ., Position data was comprised of the horizontal Cartesian positions of every player and the ball on the court , recorded at 25 frames per second ., We defined an attack segment as the period beginning when all players enter the attacking side of the court and ending before a shot was made ( 77 shots were successful out of 192 attack segments ) ., We focused on the most effective attacker-defender distances ( previously shown in 9 ) , which were temporally and spatially corrected to predict the success or failure of the shot ( Fig 4 left ) ., Although all of the distances were expressed in 25 dimensions ( five attackers and defenders ) , we previously reduced such data to four dimensions 9: ball-mark distance ( i . e . , the maximum distance between the attackers with the ball and the nearest defenders ) , ball-help distance ( i . e . the secondary maximum distance of the ball-mark distance ) , pass-mark distance ( i . e . , the maximum distance between the attackers without the ball and the nearest defenders ) , and pass-help distance ( i . e . the secondary maximum distance of the pass-mark distance ) ( Fig 4 left ) ., To apply DMD with reproducing kernels and verify its predictive performance , we used nine input matrices: ( i-iv ) one- to four-dimensional critical distances as described above ., For additional verification , ( v-vii ) 9 , 16 , 25 distances in Fig 4 left and, ( viii ) 25-dimensional Euclidean distance matrices without spatiotemporal corrections were calculated ., We also used, ( ix ) the Cartesian positions ( total 20 dimensions ) of all ten players ( typical time series are shown in S8 Fig ) ., Note that reconstructions from the DMD with reproducing kernel outperformed those from the original DMD ( S3 Note and S9 Fig ) ., For predicting the outcome of a team’s attacking movement ( i . e . , shot ) , we computed the probability of the shot outcome because the shot outcome is probabilistic rather than deterministic ., Thus , we used classifiers outputting the posterior probability such as a naive Bayes classifier ( results with other classifiers are described in S3 Note and S11 Fig ) ., The feature vectors inputting the classifier were created by Koopman spectral kernels 32 ( See Materials and Methods ) to express the similarities between the attack segments with various input distances ., For comparison , the kernel of the basic DMD and the simple feature vector using the maximum adjusted distances in the previous study 9 were also computed ., Fig 5 shows the results of applying a naive Bayes classifier ., The horizontal axis represents the nine input matrices and the vertical axis the classification error ( this is the median value of 5-fold cross-validation error ) ., Overall , the Koopman spectral kernels produced better classifications than the kernels of the DMD and the feature vector using the maximum distance ( and Cartesian coordinates ) ., If the decomposition methods cannot extract the dynamics ( i . e . , the result had a low expressiveness ) , the classification error is near the chance level ( 0 . 5 ) such as when DMD using 25 distances and DMD with reproducing kernels using the Cartesian coordinates ., Especially , the Koopman kernel principal angles derived by inputting four important distances exhibited the minimum classification error of 35 . 9% ., Additionally , we examined the error analysis of all 192 attack-segments of the best classifier: the numbers of true positives , true negatives , false positives , and false negatives were 22 , 97 , 18 , and 55 , respectively ( Typical examples are shown in S5–S8 Videos ) ., Note that the classification error was computed as the median values of the five test sets and then did not correspond to the computation from the above values ., Our best classifier tended to predict non-score ( 152: true and false negatives ) more than the actual ( 115 ) ., Fig 4 right shows embedding via multidimensional scale with the distance matrix of the Koopman spectral kernels , contoured by frequencies of success and failure of the shot ., Figs ( III ) A-G correspond to the results inputting the distances in ( i-vii ) of Fig 4 ( I ) ., Fig 4H and 4J show the results inputting the Euclidean distance and Cartesian coordinates , respectively ., For example , the best case of the four important attacker-defender distances ( Fig 4D ) showed high expressiveness for scoring probability due to the plot’s wide distribution ., In contrast , the plots were less widely distributed when only a single distance ( Fig 4A ) or the Cartesian coordinates of all players ( Fig 4J ) were used ., The kernels of the basic DMD showed even less distribution and less expressiveness ( S12 Fig ) ., Our objectives in this study were to verify the application of DMD to simple collective motion models and develop the method for more complex and low-dimensional actual motions ., First , the DMD applied to a simulation of an explicit model of collective motion extracted different temporal frequency modes with spatial interaction coherence among three emergent motions ( Fig 2 ) ., These wave properties at multiple spatiotemporal scales showed similar dispersion relations ( Fig 3 ) ., The three schooling behavior differed only by one parameter , the radius of the orientation zone , which explains why the wave properties were invariant ., Analysis of the dynamic structure factor revealed that schooling motions have viscoelastic properties 27 , and this quality was independent of the type of the emergent motion ., However , the visual appearances of the three emergent motions are distinct , and these differences are generated by dynamic changes in local interactions in response to changing circumstances , including other agents ., DMD extracted the dynamic properties to explain the difference among the three global behaviors ., In general , DMD can extract interactive coherence spectra with various temporal frequency modes for an active matter or non-equilibrium many-body system as long as interaction rules are consistent ., However , note that transient behaviors such as occurs with actual organism motion in a small group are difficult to reconstruct with the original DMD method 22 ., We also developed DMD with reproducing kernels 30 , 32 for applications to small group behavior with transient behavioral rules ., We mapped the latent dynamic characteristics to the feature space with high expressiveness , and obtained intuitively reasonable outcomes by successfully predicting the achievement of the group objective ., Compared with the original DMD , the advantages of DMD with reproducing kernels were that it can be applied to low-dimensional data and transient behaviors ., Competitive collective motion among small groups that can dynamically change their strategy according to the situation is just such a situation ., Specifically for a dataset from basketball games , the highest prediction performance was achieved using a limited set of relevant player distances ( Figs 4 and 5 ) ., The scoring outcome can be predicted by discarding extra information so that the remaining data is more semantically important ., The vector series reflects four characteristics: the scoring probability of a player in the, ( i ) shot ,, ( ii ) dribble to goal , and, ( iii ) pass , and, ( iv ) the scoring probability of a dribbler after the pass ., The proposed kernels reflected the time series of all interactions between players and were more effective for classification than the kernels based on the information only about the shot itself or the raw Cartesian positions ., Well-trained ballgame teams aim to create scoring opportunities by continuously selecting strategic passes and dribbles or by improvising when no shooting opportunity is available ., However , our proposed method has some drawbacks ., In original DMD for the fish-schooling model , we could not extract the dynamic interaction modes for centripetal motion in the torus behavior ., Theoretically , when motion deviates greatly from the ideal oscillator , the mode may not be extracted by DMD ., Also in DMD with reproducing kernels applied to the basketball data , even the best classification was not very accurate , with only 64 . 1% accuracy ., Our framework may have neglected two factors ., The first is the existence of local interactions between players , such as local competitive and cooperative play by the attackers and defenders 9 , 43 which should be examined in higher spatial resolution than was available in our data ., A better model would reflect the hierarchical dependencies of global and local dynamics ., The second is the limitation of the input matrices of two-agent distances ., To achieve more accurate classifiers , hand-made input vector series such as Cartesian coordinates or specific movement parameters should be used in addition to the most important input factor of inter-player distances ., Overall , we developed a method to predict and classify collective motion dynamics without equations by decomposing data into several temporal frequency modes with coherence among spatial interactions ., This algorithm can , in general , be applied to the analysis of the complex dynamics in groups of living organisms or artificial agents , which currently eludes formulation ., This method can provide us to predict the outcome of unknown behavior from collective movement data in non-trivial dominance law such as active matters ., From a different viewpoint , the rule-based fish-schooling model and human group in a sport used in this study can be considered as the examples in which the communication is likely to be measured in a physical space ., Therefore , if we can measure the outputs of the communication and suppose that there are underlying dynamics behind the obtained data , our approach can deal with various means of communication between the agents ., Practically , in various material and life sciences or human community , supervisors ( experimenters , coaches or teachers ) spend considerable amounts of time analyzing the collective motion in the domain ., Application of a system , such as the one presented here , may create useful plans that are currently derived only from their implicit experience ., The schooling model we used in this study was a unit-vector based ( rule-based ) model , which accounts for the relative positions and direction vectors of other fish agents , such that each fish tends to align its own direction vector with those of the members ., We used an existent model 44 based on the previous work 2 ., The specific parameters are shown in S1 Table ., In this model , N = 64 agents are described by a two-dimensional vector with a constant velocity ( 4 m/s ) in a boundary circle ( radius: 25 m ) as follows:, ri= ( xiyi ) ,, ( 10 ), vi ( t ) =|vi|di ,, ( 11 ), where di is an unit vector ., At each time step , a member will change direction according to the positions of λ neighbors ., In this study , to generate the three distinct behaviors ( the swarm , torus , and parallel behaviors ) , we simply used the metric framework , i . e . , the agents change direction according to the positions of the neighbors ., The space around an individual is divided into three zones with each modifying the unit vector of the velocity The first region , called the repulsion zone with a radius rr , corresponds to the “personal” space of the particle ., Individuals within each other’s repulsion zones will try to avoid each other by swimming in opposite directions ., The second region is called the orientation zone , in which members try to move in the same direction ( radius ro ) ., We changed the parameter ro to generate the three behavioral shapes ., Next is the attractive zone ( radius ra ) , in which agents swim towards each other and tend to cluster , while any agents beyond that radius have no influence ., Let λr , λo , and λa be the numbers in the zones of repulsion , orientation and attraction respectively ., For λr ≠ 0 , the unit vector of an individual at each time step τ is given by:, di ( t+τ , λr≠0 ) =− ( ∑j=1λrrij ( t ) |rij ( t ) | ) ,, ( 12 ), where |rij| = |rj − ri| ., The direction vector points away from neighbors within this zone to prevent collisions ., This zone is given the highest priority; if and only if λr = 0 , the remaining zones are considered ., The unit vector in this case is given by:, d→i ( t+τ , λr=0 ) =−12 ( ∑j≠iλod→i ( t ) +∑j≠1λar→ij ( t ) |r→ij ( t ) | ) ,, ( 13 ), The first term corresponds to the orientation zone while the second term corresponds to the attraction zone ., The above equation contains a factor of 1/2 which normalizes the unit vector in the case that both zones have non-zero neighbors ., If no agents are found near any zone , then the individual maintains constant velocity at each time step ., In addition to the above , we constrain the angle by which a member can change its unit vector at each time step to a maximum of β ., This condition was imposed to facilitate rigid body dynamics ., Because we assumed point-like members , all information about the physical dimensions of the actual fish is lost , which leaves the unit vector free to rotate at any angle ., In reality , however , conservation of angular momentum will limit the ability of the fish to turn angle θ as follows:, Ifθ>β , di ( t+τ ) ⋅di ( t ) =cos ( β ) Otherwisedi ( t+τ ) ⋅di ( t ) =cos ( θ ) ., ( 14 ), If the above condition is not met , the angle of the desired direction at the next time step is rescaled to θ = β ., In this way , any un-physical behavior such as having a 180° rotation of the velocity vector in a single time step , is prevented ., Initial conditions were set so that the particles would generate the torus motion , though all three motions emerge from the same initial conditions ., The initial positions of the particles were arranged using a uniformly random number on a circle with a uniformly random radius between 6 m and 16 m ( the original point is the center of the circle ) ., The initial velocity was set to be perpendicular to the initial position vector ., We modeled schooling behavior with and without circular boundary conditions ( the main results in Fig 2 used a circular boundary of 25 m radius ) ., The average values of the control parameter ro were set to 2 , 10 , and 13 to generate the swarm , torus , and parallel behaviors , respectively ., Although the previous study of the model 2 examined the effect of the noise added to various parameters , we simply added noise to the constant velocities among the agents ( but constant within a particle ) with a standard deviation of σ to generate the three distinct behavioral shapes with a certain variability ., If the noise is close to zero , the group behavior has less variability and if the noise increases , the group behavior might fragment; thus we set σ to 0 . 05 according to the previous settings 44 ., The time step in the simulation was set to 10−2 s ., We simulated 15 trials for each parameter ro in 10 s intervals ( 1000 frames ) ., The analysis start times were varied depending on the behavior type to avoid calculating the transition period ( torus: 10 s , swarm and parallel: 30 s after the simulation start ) ., We customized freely available MATLAB code for the simulation 44 ., We calculated the longitudinal and transverse dynamical structure factor 27 , SL ( q , ω ) and ST ( q , ω ) , respectively , given as, Sα ( q , ω ) =12πN∫dt〈jα ( q , t ) ⋅jα ( −q , 0 ) 〉exp ( iωt ) ,, ( 15 ), where α is L and T , and q is the wave vector , ω is the angular frequency , and jα is the density-density correlation function in both space and time 26 , 29:, jL ( q , t ) =∑i ( vi ( t ) ⋅q^ ) q^exp ( iq⋅ri ( t ) ) , jT ( q , t ) =∑i ( vi ( t ) − ( vi ( t ) ⋅q^ ) ⋅q^ ) exp ( iq⋅ri ( t ) ) ,, ( 16 ), where q^=q/|q| and ri is the two-dimensional position vector ., Results revealed that the longitudinal dynamic structure factor had a shifted peak among the wavenumbers ( Fig 3A–3C ) ., These peak positions ω shift followed a Brillouin-like dispersion relation:, ωshift=c|q| ,, ( 17 ), where c is a coefficient which has the dimension of velocity , and represents the soundwave velocity in conventional applications of Brillouin peaks ., We can , therefore , say that these systems possess pseudo-acoustic mode dynamics or a soundlike propagation mode for agent density ., The sound velocity of this pseudo-acoustic mode can be calculated from the dispersion relation , shown in the Fig 3D ., Results revealed that the longitudinal dynamic structure factor had shifted peaks among wavenumbers ( Fig 3A–3C ) , and the dispersion relations between wavenumber and frequency at those spectral peaks were almost linear and equal among all collective motion types ( Fig 3D ) ., Similarly , for the transverse dynamic structure factor , the dispersion relations were also almost linear , although it was not relatively clearer than the longitudinal one ( S6 Fig ) ., These dispersion relations were independent of the type of the emergent motions ( even without the boundary: S7 Fig ) ., We used player-tracking data from two actual international basketball games in 2015 collected by the STATS SportVU system ., The total playing time was 80 min , and the total score of the two teams was 276 ., Positional data comprised the Cartesian position of every player and the ball on the court , recorded at 25 frames per second ., We eliminated transitions to attacking to automatically extract the time periods to be analyzed ( called an attack-segment ) ., We defined an attack-segment as the period starting when all attacking players enter the active half of the court and ending 1 s before a shot on goal was attempted ., We analyzed a total of 192 attack-segments , 77 of which ended in a successful shot ., We focused on the effective attacker-defender distances ( previously shown by 9 ) , which were temporally and spatially corrected to predict the success or failure of the shot ., Although all of the distances comprise 25 dimensions of data , we reduced the dimensions to four dimensions in previous work 9: ball-mark distance , ball-help distance , pass-mark distance ( i . e . , the maximal distance between the attacker without the ball and the nearest defenders ) , and pass-help distance ( i . e . , the second maximal distance of pass-mark distance ) ( Fig 4 left ) ., For applying DMD with reproducing kernels and its verification , we used nine input matrices: ( i-iv ) one to four-dimensional critical distance of the above , and for verification ,, ( v ) total distance and, ( vi ) 25-dimensional Euclidean distances without spatiotemporal correction were calculated ., We also used, ( vii ) the Cartesian coordinates ( total 20 dimensions ) of all the ten players ( typical time series are shown in S8 Fig ) ., In performing the DMD with reproducing kernel , we used the Gaussian kernel or radial basis function:, k ( yi , yj ) =exp ( −‖yi−yj‖22σ′2 ) ,, ( 18 ), where i and j are the time point of the observation data , and σ′2 is the kernel width set as the median of the distances from a data matrix 30 , 32 ., For example , the Gram matrix Gyy in the main text of this Gaussian kernel can be defined as follows:, Gyy= ( k ( y1 , y1 ) …k ( yτ−1 , y1 ) ⋮⋱⋮k ( y1 , yτ−1 ) …k ( yτ−1 , yτ−1 ) ) ., ( 19 ), Selection of an appropriate representation of the data is a fundamental issue in pattern recognition ., The important point is to design features ( i . e . , kernels ) that reflect the structure of the data ., Time-series data is challenging to design kernels for because of difficulties in reflecting the data structure ( including time length ) ., In this paper , a kernel design applicable to dynamical systems was required ., Several methods were proposed , based on the subspace angle with kernel methods such as an auto-regressive moving average ( ARMA ) model 45 ., We generalized to nonlinear dynamics without any specific underlying model , into which the Koopman spectrum of dynamics is incorporated ., We called the kernels Koopman spectral kernels ., For calculating the similarity between the dynamical systems DSi and DSj , we compute Koopman spectral kernels based on the idea of Binet-Cauchy kernels ., In the unifying viewpoint 45 , Binet-Cauchy kernels are a representation including various kernels 46–49 , that serve two strategies ., One is the trace kernel , which directly reflects the properties of the temporal evolution of the dynamical systems , including diffusion kernels 46 and graph kernels 47 ., The second strategy is the determinant kernel , which extracts coefficients of dynamical systems , including the Martin distance 48 and the distance based on the subspace angle 49 ., However , richer information about system trajectories does not necessarily increase the expressiveness for classifications with real-world data ., Therefore , we also expanded the kernel of principal angle 50 to applications with Koopman spectral analysis , which is called the Koopman kernel of principal angle ., The principal angle kernel is theoretically a simple case of the trace kernel 45 , which is defined as the inner product of linear subspaces in this feature space ., In our previous work , the Koopman kernel of principal angle showed the most effective expressiveness in spite of using only Koopman modes for the calculations 32 ., In this paper , therefore , we compute the Koopman kernel of principal angle with the inner product of the Koopman modes , and leave the system trajectory and initial conditions aside ., The kernel of principal angle can be computed using the Koopman modes given by DMD with reproducing kernel ., With respect to DSi , we define the kernel of principal angles as the inner product of the Koopman modes in the feature space: A*A=T^i−1S¯i−1/2B¯i*HGyyiiHB¯iS¯i−1/2T^i , where i is an index of the matrix generated by DMD with reproducing kernels applied to DSi ., If the rank of F^ is ri , A*A is a ri-order square matrix ., Also for DSj , we create a similar matrix B*B ., Furthermore , we define the inner product of the linear subspaces between DSi and DSj as A*B=T^i−1S¯i−1/2B¯i*HGyyijHB¯jS¯j−1/2T^j ., Gyyij is a ni × nj matrix obtained by picking up the upper-right part of the centered Gram matrix obtained by connecting Yi and Yj in series ( ni and nj are the lengths of the time series ) ., Then , using these matrices , we solve the following generalized eigenvalue problem:, ( 0 ( A*B ) *A*B0 ) V=λij ( B*B00A*A ) V ,, ( 20 ), where the size of λij is finally adjusted to rij = min ( ri , rj ) in descending order , and V is a generalized eigenvector ., The eigenvalue λij is the kernel of principal angle ., Note that for DMD without reproducing kernels , A and B can be calculated directly because these matrices are of finite dimensions ., For embedding of the distance matrix with our kernels , components of the distance matrix between dynamical systems in the feature space were obtained using dist ( DSi , DSj ) = k ( Ai , Ai ) + k ( Aj , Aj ) − 2k ( Ai , Aj ) ., We visualized by multidimensional scaling ( MDS ) with the distance matrix ., For predicting the outcome of a team’s attacking movement ( i . e . , shot ) , we computed the probability of the shot outcome because the shot outcome is probabilistic rather than deterministic ., Thus , we used classifiers outputting the posterior probability such as a naive Bayes classifier ., A naive Bayes classifier is a well-known and practical probabilistic classifier and has been employed in many applications ., Since we had a relatively small dataset ( 192 attack-segment in total ) , we evaluated the median error rate , i . e . , the rate of false negatives and false positives by testing at five times in analogous ways of 5-fold cross-validation ., The results from applying other classifiers are shown in S3 Note and S11A and S11B Fig , respectively ., The performances of other methods were inferior to that of the naive Bayes classifier .

Results, Discussion, Materials and methods

Modeling the complex collective behavior is a challenging issue in several material and life sciences ., The collective motion has been usually modeled by simple interaction rules and explained by global statistics ., However , it remains difficult to bridge the gap between the dynamic properties of the complex interaction and the emerging group-level functions ., Here we introduce decomposition methods to directly extract and classify the latent global dynamics of nonlinear dynamical systems in an equation-free manner , even including complex interaction in few data dimensions ., We first verified that the basic decomposition method can extract and discriminate the dynamics of a well-known rule-based fish-schooling ( or bird-flocking ) model ., The method extracted different temporal frequency modes with spatial interaction coherence among three distinct emergent motions , whereas these wave properties in multiple spatiotemporal scales showed similar dispersion relations ., Second , we extended the basic method to map high-dimensional feature space for application to actual small-dimensional systems complexly changing the interaction rules ., Using group sports human data , we classified the dynamics and predicted the group objective achievement ., Our methods have a potential for classifying collective motions in various domains which obey in non-trivial dominance law known as active matters .

Modeling complex collective motions is a challenging problem such as in biology , physics , and human behavior because the rules governing the motion are sometimes unclear ., Then , researchers have usually used simple interaction model and explain global statistics ., However , it remains difficult to bridge the gap between the dynamic properties of the complex interaction and the group-level functions ., This study develops an effective framework to extract the dynamics of collective motion from data by data-driven modeling ., Compared with conventional methods , our method can be applied to cases with the small numbers of group members or transient and complex changes of the behavioral rules ., Our methods successfully discriminated group movements of well-known fish-schooling models and predicted the achievement of a group objective from actual basketball players’ position data ., Our methods have a potential for outcome prediction and classification for various unsolved and complex collective motions such as in biology and physics .

recreation, collective animal behavior, sports, applied mathematics, social sciences, geometry, simulation and modeling, algorithms, systems science, mathematics, animal behavior, zoology, research and analysis methods, cartesian coordinates, computer and information sciences, nonlinear systems, behavior, dynamical systems, nonlinear dynamics, psychology, kernel methods, biology and life sciences, sports science, physical sciences, coordinate geometry

journal.pbio.1002407

Energetic Constraints on Species Coexistence in Birds

The relationship between species richness and energy availability—often described in terms of ecosystem productivity—is widespread yet poorly understood 1–5 ., A link between the flux of energy through an ecosystem and the number of species it contains has long been recognised 6 , but the exact form of the relationship and its scale-dependence have traditionally been the focus of much debate 4 , 7–11 ., Recent analyses have established that , when measured over large geographic and taxonomic scales ( >50 km grain size across continental or global study regions ) , species richness increases strongly with the availability of potential energy ( e . g . , net primary productivity or associated climatic proxies 12 ) , and that this relationship explains much of the spatial variation in biodiversity 13–17 ., Similar patterns are repeated across a variety of life forms and regions with contrasting evolutionary histories , implying that energy availability may offer a universal explanation for Earth’s major gradients in biodiversity 1 , 18 ., However , resolving the processes driving this relationship has proven far more challenging 2 , 3 , 19 , 20 ., The predominant explanation for the positive relationship between energy availability and species richness ( the energy–richness relationship ) is that the amount of energy flowing through an ecosystem places a fundamental constraint on the number of species coexisting at any single point in space ( alpha-diversity ) 2 , 3 , 6 ., Higher energy fluxes and the corresponding expansion in the breadth and availability of useable resources are expected to reduce the incidence of stochastic population extinction by sustaining a larger total number of individual organisms ( the “more individuals” hypothesis ) 18 , 21 and to increase the potential for local niche partitioning required for stable coexistence 22–24 ., However , direct support for these hypotheses is very scant 2 , suggesting that the energy-richness relationship may have arisen through an alternative process 19 , 25 ., For instance , species may have diversified more rapidly in productive environments because higher temperatures 26 , increased solar radiation 27 , larger population sizes 21 , or reduced dispersal ability 28 drive accelerated rates of speciation ( the “speciation rates” hypothesis ) 2 , 29–31 ., Alternatively , if most clades originated in the humid tropics , then species richness may simply have had a longer time to accumulate in regions with high energy availability , while strongly conserved physiological constraints have prevented the expansion of species or clades into colder or drier environments with lower productivity ( the “niche conservatism” hypothesis ) 11 , 14 , 25 , 32–34 ., Thus , rather than reflecting energetic constraints on community assembly , more species may coexist in productive ecosystems for largely historical reasons 2 , 35 ., These contrasting historical explanations have not been ruled out by previous studies because standard methods for testing the relationship between energy availability and limits to species coexistence are largely indirect , including correlations between energy availability and assemblage biomass 36 , 37 , population density 24 , 38 , 39 , and rates of local extinction 40 ., While these correlations are suggestive of a link between energy availability and community assembly dynamics , they do not conclusively establish whether coexistence is subject to energetic constraints , nor whether such constraints drive broad-scale gradients in species richness 2 , 3 ., An alternative approach has been to examine the relationship between energy availability and the richness of entire assemblages , while statistically accounting for differences in regional diversity 14 , 19 , 41–44 ., However , this method is problematic because it lumps together numerous unrelated species spanning a vast array of lifestyles and diets , with only minor ecological overlap , and ignores the possibility that regional species diversification may itself ultimately be regulated by local limits to coexistence 45 , 46 ., Understanding the role of energetic constraints on biodiversity therefore requires an approach that focuses on species with the broadest overlap in ecological niches , while robustly accounting for processes playing out over evolutionary time ., Here , we address these issues using a comparative framework to explore patterns of geographic range overlap among sister species , relatively young lineages for which energetic constraints on coexistence resulting from similarity in resource use are expected to be most pronounced 47 , 48 ., Our analyses include data from 1 , 021 sister pairs , distributed across the avian phylogenetic tree and the world’s major landmasses , and representing 30% of all species for which genetic sequence data are available ( S1 Fig ) 49 ., The breadth of this dataset and the evolutionary context provided by the phylogenetic relationships among lineages allows us to test the extent to which energy availability explains species coexistence in birds as well as the relationship between coexistence and contemporary gradients in species richness ., We first quantify patterns of coexistence on the basis of overlap in the breeding distributions of sister species and test whether energy availability explains the probability of coexistence , both across species pairs and geographic space ., Our analysis accounts for the potentially confounding effects of other abiotic variables and the phylogenetic nonindependence of species ., We then take advantage of the fact that almost all speciation events in birds have involved a phase of geographic isolation 46 , 50 , using estimated divergence times to test how ecosystem productivity regulates the temporal dynamics of coexistence following divergence in allopatry 47 , 51 ., We compare a model in which energy availability predicts either the initial rate at which coexistence is attained , or its temporal duration , to a null model in which variation in the incidence of coexistence is explained purely by differences in the time elapsed since speciation 47 ., Throughout , we account for uncertainty in phylogenetic relationships and estimates of divergence times by fitting our models across multiple trees sampled at random from the posterior distribution 49 ., In a final set of analyses , we assess the relationship between patterns of species coexistence and the total richness of avian assemblages using the geographic distributions of all 9 , 993 bird species ., By combining these approaches , we aim to clarify the role of energetic constraints in both the establishment and maintenance of species coexistence as well as the contribution of these dynamics to global-scale gradients in species richness ., Across our dataset , 28% of sister species coexist , with the rest having either geographically isolated distributions or exhibiting only marginal overlap along narrow contact zones ( area of range overlap <20% of the smaller species range; see Materials and Methods ) ., To examine the incidence of local coexistence and how this varies across geographic space , we quantified the percentage of sister species pairs coexisting within equal area quadrats ( resolution of 110 km x 110 km , ≈ 1° at the equator ) ., Few coexisting sister species are sympatric over the entirety of their geographic range ( mean range overlap = 66% of the smaller species range and 22% of the total geographic range of both sister species combined ) , resulting in a low average incidence of coexistence among sister species at the local scale ( mean percent of sister pairs in a cell where both species are locally present = 7% ) ., However , levels of local coexistence exhibit substantial variation across geographic space ( 0%–34%; Fig 1A ) ., Areas containing a particularly high concentration of coexisting lineages occur throughout the wet tropics , including the eastern slope of the Andes and Amazonia , the Congo basin , New Guinea , the eastern Himalayas , and the Malay Archipelago ., Beyond the tropics , additional regions of high coexistence occur along the eastern coast of Australia and throughout the northern Nearctic ( Fig 1A ) ., We used sister species pairs to assess patterns of coexistence , thus avoiding the pseudoreplication introduced when analysing assemblage level patterns ., The energy available for each sister pair was quantified by averaging mean annual net primary productivity ( NPP; Fig 1B ) across their combined geographic distribution ( for allopatric pairs ) or those grid cells where both species coexist ( for sympatric pairs ) ( Materials and Methods ) ., We found that the probability of coexistence between sister species increases strongly with local NPP ( generalised linear model GLM , slope = 0 . 22 , p < 0 . 01; Fig 2A ) ., This significant positive association was evident regardless of the degree of geographic range overlap used to define coexistence ( 5% to 80% ) , but became increasingly steep when considering more stringent overlap thresholds ( Fig 2B ) ., NPP is the most appropriate metric for testing energetic constraints in heterotrophic organisms , but we also detected the same significant trend using actual evapotranspiration ( AET ) to quantify potential energy ( GLM , slope = 0 . 15 , p < 0 . 05 ) ., In both cases , the effect of energy availability on coexistence was nonlinear , with the inclusion of a positive quadratic term leading to a substantial improvement in model fit ( GLM , slope = 0 . 21 , quadratic = 0 . 21 , p < 0 . 01 , Δ Akaike information criterion AIC = 8 . 2 in favour of a model including a quadratic term ) ., Thus , while it has long been debated whether increased ecosystem productivity may elevate the intensity of competition , thereby reducing coexistence under conditions of high resource availability 4 , 22 , we find the opposite pattern , wherein the probability of coexistence is a positive accelerating function of energy availability ., If the incidence of coexistence between sister species shows a strong phylogenetic signal , then the positive effects of energy availability could be driven by one or a few individual clades ., To evaluate this possibility , we calculated the D statistic , which provides an estimate of the phylogenetic signal in a binary character relative to both a phylogenetically random distribution ( expected D = 1 ) and a Brownian motion model of trait evolution ( expected D = 0 ) 53 ., We found that while the incidence of coexistence was not randomly distributed across the avian tree ( pD = 1 < 0 . 01 ) , phylogenetic signal was low ( D = 0 . 88 ) , indicating that any tendency for coexistence to be clustered in particular clades is weak ( Materials and Methods; S1 Fig ) ., Furthermore , when we included the phylogenetic covariance between species as a random effect in our models , the significant positive association between energy availability and coexistence remained ( Fig 2B and S4 Table ) ., Another possibility is that the relationship between species coexistence and energy availability could arise due to covariation with other environmental factors ., For instance , it has been proposed that resource specialisation leading to coexistence may be precluded in more seasonal environments in which resource abundance undergoes larger short-term temporal fluctuations 2 ., Fluctuations in climate and resources may also play a role over longer time-frames , and it is variously predicted that coexistence could be promoted 54 or reduced 55 in regions covered by ice sheets during recent glacial maxima ., Other physical attributes of the environment are thought to promote coexistence , including topographical heterogeneity 56 and ambient temperature 57 ., To address these possibilities , we assessed the role of energy availability on coexistence relative to a suite of abiotic variables in both single and multipredictor models ( all variables were normalised to allow effect sizes to be directly compared ) ., In addition to the effects of energy availability , our models highlighted a number of significant predictors of species coexistence ( Fig 3 , S1–S4 Tables ) ., When assessed in isolation , we found that coexistence was significantly reduced in areas experiencing greater environmental seasonality ., However , seasonality was not significant in a multipredictor model , suggesting that these effects arise from covariation with other abiotic predictors ., We also detected negative relationships between coexistence and both the change in temperature since the last glacial maximum and topographic heterogeneity ., In both cases , the relationship is distinctly U-shaped , with coexistence first declining and then increasing ., These inconsistent slopes suggest that neither long-term climatic variability nor topographic heterogeneity have a general or direct mechanistic effect on coexistence ., In particular , our results suggest that the effect of topographic heterogeneity may be sensitive to the inaccuracies in broad scale distribution maps in mountainous regions 58 ., This is because , when we used more stringent overlap thresholds to define coexistence , the slope of the relationship between topographic heterogeneity and coexistence shifted to become increasingly negative and monotonic ( Fig 3 , S1–S3 Tables ) ., Finally , contrary to the well-established positive relationship between species richness and temperature in birds 59 , 60 , we found that ambient temperature was unrelated to the probability of coexistence ., This finding suggests that temperature may be associated with richness for historical reasons ( i . e . , tropical niche conservatism ) rather than because of any direct mechanistic link with the maintenance of species diversity ., Importantly , we found that when we statistically accounted for all these additional abiotic variables , the positive effect of NPP on coexistence was strengthened ( Fig 3 , S1–S4 Tables ) ., Because the vast majority of speciation events in birds are thought to require a period of geographic isolation , we can assume that coexistence between species only arises at a later stage following the expansion of species geographic ranges 61 ., The median age of sister species varies geographically , being highest in Australasia and the Old World tropics and decreasing toward both high northern latitudes and the New World ( Fig 1C ) ., Because of variation in the time available for range expansions 47 , 62 , 63 , these gradients in sister species age may contribute to geographical differences in the incidence of coexistence and its environmental correlates ., Furthermore , rates of geographic range expansion may vary across species due to differences in intrinsic dispersal ability ., For instance , recent evidence from New World birds 51 reveals that the rate at which sympatry is attained increases with species hand-wing index ( HWI ) , a measure of wing-shape correlated strongly with long-distance flight ability 64 ., Robustly establishing the role of energy availability in limiting coexistence thus requires accounting for these effects ., To address this , we modelled the probability of coexistence as a function of both species age and HWI in isolation and alongside energy availability and other abiotic predictors ( Materials and Methods ) ., As expected , the probability of coexistence increases strongly with species age because sympatric sister pairs are , on average , >1 million years older ( 4 . 68 million years Ma ) than allopatric pairs ( 3 . 51 Ma ) ( Fig 3 ) ., Having accounted for both species phylogenetic relatedness and abiotic factors , we found evidence that coexistence is promoted by high dispersal ability ( S4 Table ) ., However , this effect was relatively weak and was not significant in our standard nonphylogenetic analysis ( Fig 3 , S1–S3 Tables ) ., Overall , while our analysis highlights the important contribution of dispersal to current patterns of coexistence , this is unlikely to explain our results , because energy availability retained its independent effects even after accounting for these historical factors ( Fig 3 , S1–S4 Tables ) ., We note that all these results were robust to phylogenetic uncertainty in sister species relationships and divergence times and held regardless of whether we fitted our model across either the Bayesian posterior distribution of trees or the single most credible tree ( S1–S4 Tables ) ., To further explore the effects of energy availability , we extended our comparative framework to test whether energy also predicts the geographical patterns of coexistence within species pairs ( S3 Fig; Materials and Methods ) ., This test is more conservative as it controls for differences in time since divergence 65 , 66 , species traits ( e . g . , dispersal ability ) , potential geographic variation in taxonomic practices and species description , and factors that may co-vary with energy availability but that are not easily accounted for in comparisons across species pairs ( e . g . , variation in rates of ecological divergence ) 47 ., Within-species pair analyses confirmed that energy availability is higher in grid cells where both sister species coexist than in cells where only one sister is present ( GLM , slope = 0 . 31 , quadratic = 0 . 33 , p < 0 . 01 , n = 187 , S1 Table ) , a result that was further strengthened when we statistically accounted for other abiotic variables ( Fig 3 and S1 Table ) ., These results strongly reject the possibility that patterns of coexistence arise simply due to historical factors , including differences in species age or rates of niche evolution ., Looking beyond deterministic associations , it is also important to consider whether our results may be explained by stochastic effects ., Although highly controversial 17 , 67 , it has been argued that broad-scale gradients in both species richness and its environmental associations may be driven by random range dynamics within a bounded geographic domain , the so-called “mid-domain effect” 68 , 69 ., However , our findings are inconsistent with this stochastic model because we show that current energy availability predicts not only the broad-scale variation in range overlap , but also the particular locations of coexistence and allopatry within sister pairs ( Fig 3 and S1 Table ) ., These results confirm previous evidence demonstrating that random geographic range expansion cannot explain species distributions of birds 70 ., When viewed from a historical perspective , the positive relationship between coexistence and energy availability may be generated either because more productive ecosystems facilitate the initial transition to sympatry following speciation 46 , 71 , or because they prolong the duration of coexistence by reducing rates of local extinction 40 ., To examine these possible mechanisms , we applied a stochastic approach to model the dynamics of coexistence between species over evolutionary time ( Materials and Methods ) 47 ., In this model , we assumed that sister species are spatially isolated at the time of population divergence and then transition to a state of coexistence at a constant rate , σ ., Because local extinction may result in coexisting species returning to a state of spatial segregation , we incorporate this process in our model by allowing reverse transitions back to allopatry at rate ε ., Based on this model , we obtained the likelihood of observing sister species pairs in their current geographic state ( allopatric/parapatric or sympatric ) given the empirical distribution of sister species ages ., We then used likelihood optimisation to estimate the transition rates to ( σ ) and from ( ε ) coexistence , from which the expected waiting time to sympatry following speciation ( 1/σ ) and the subsequent expected duration of coexistence ( 1/ε ) can be calculated ( Materials and Methods ) ., By comparing AIC scores , we evaluated the relative fit of an “energy-availability dependent” ( EAD ) model , in which either σ , ε , or both are allowed to vary as a log-linear function of NPP , to a null model , in which transition rates between geographic states are equivalent across species ., Using this approach , we were able to provide estimates of coexistence dynamics that are independent of any geographic gradient in sister species ages ( S1 Text and S4 Fig ) ., According to our transition model , the mean expected waiting time to coexistence ( i . e . , 1/σ ) following speciation is 5 . 56 Ma , confirming our previous results highlighting the importance of time in the build-up of species coexistence ( S5 Table ) ., Furthermore , in accordance with our standard statistical models , we found that an EAD model fits the data best , rejecting the null hypothesis that the probability of coexistence depends only on sister species age and , thus , the time available for range expansion ( S5 Table ) ., A model in which ε decreases with ecosystem productivity ( ΔAIC = 7 . 92 in favour of the EAD model ) outperforms a model in which productivity influences σ ( ΔAIC = 5 . 72 relative to the null model ) , and there was no further improvement in model fit when combining the effects of energy availability on both σ and ε ( ΔAIC = 6 . 05 relative to the null model; S5 Table ) ., The effects of energy availability on ε inferred by our model are substantial , with the mean expected duration of coexistence ( i . e . , 1/ε ) being two times longer in high ( 4 . 15 Ma; first NPP quartile ) compared to low energy environments ( 2 . 04 Ma; fourth NPP quartile ) ., To examine whether these inferred coexistence dynamics are consistent with the patterns observed across sister species , we plotted how the probability of coexistence predicted by our model increases as a function of both age and NPP ( Fig 4A and 4B ) ., In contrast to the poor fit of the null model ( Fig 4A ) , we find that an EAD model accounting for the effects of productivity on coexistence duration ( Fig 4B ) better captures the observed variation in the incidence of coexistence across the global gradient in both species age and energy availability ( Fig 4C and 4D; Materials and Methods ) ., In particular , this model explains both the similar levels of coexistence observed among recently diverged species , regardless of local energy availability , and the apparent increase in the effect of energy availability over time as differences in the duration of coexistence are realised ., Thus , our results suggest that the primary effect of energy availability is not brought about by increasing the rate at which coexistence is attained following speciation , but rather by extending the duration of coexistence , thus allowing a greater accumulation of sympatric diversity ., Recent evidence based on the age of sympatric sister lineages of New World birds suggested that sympatry is attained more rapidly at high latitudes compared to the tropics 54 ., This pattern has been explained in terms of the large-scale shifts in habitats following the retreat of Northern Hemisphere ice sheets , with vacant ecological niche space facilitating geographic range expansions 54 ., Given the general decline in ecosystem productivity away from the equator ( Fig 1B ) , such a latitudinal increase in rates of secondary sympatry would appear to be at odds with the strong positive effect of energy availability on coexistence reported here ., To resolve this , we used our analytical approach to fit a “latitudinal dependent” ( LD ) model and examined how the dynamics of coexistence varies with absolute geographic latitude ., We fitted this model both globally ( n = 1 , 021 sister pairs ) and within the New World ( i . e . , the Nearctic and Neotropical realms described by Olson et al . 72; n = 492 sister pairs ) ., Our results reaffirmed a positive effect of latitude on the transition rate to sympatry in New World birds ( ΔAIC = 2 . 19 in favour of the LD model; S6 Table ) , likely contributing to the high levels of coexistence found across the northern Nearctic ( Fig 1A ) ., However , when we extended our analysis globally we found no effect of latitude on the dynamics of coexistence ( ΔAIC = 1 . 63 in favour of the null model; S6 Table ) ., Our results thus indicate that a latitudinal gradient in the rate of secondary sympatry is not a general trend but only a regional phenomenon , and that this does not override the positive effect of energy availability on the duration of coexistence at global scales ., By demonstrating that high energy availability enhances species coexistence , our results provide a long-sought mechanistic link between current environmental conditions and broad-scale gradients in species richness ., However , sister species pairs typically comprise only a fraction of all species within an assemblage , and the extent to which energetic constraints on coexistence contribute to variation in species richness remains unclear ., To explore this , we examined how the incidence of coexistence across grid cells is related to the assemblage richness of the 2 , 042 sister species analysed and to the total richness of all 9 , 993 bird species ( Fig 1D ) ., The results of these approaches revealed that , for a given level of coexistence , species richness is highly variable ., Thus , we found a positive but relatively weak association between coexistence and both sister species richness ( Spearman’s ρ = 0 . 36 , p <0 . 001 ) and total avian richness ( Spearman’s ρ = 0 . 34 , p <0 . 001 ) ( S2 Fig ) ., However , the relationship between richness and coexistence is triangular with a clear upper boundary , so that maximum species richness increases strongly with the percentage of coexisting sister species ( Fig 5 ) ., In other words , while high levels of coexistence can be found regardless of local richness , species-rich locations are uniquely those supporting high levels of coexistence rather than simply a large number of allopatric lineages ( Figs 5 and S2 ) ., To examine this relationship in more detail , we divided the earth’s land surface into six biogeographic realms 72 , each of which has a largely independent evolutionary history and contrasting average levels of species richness ( Fig 5 ) ., We found that the positive relationship between coexistence and richness was replicated within realms ., Nonetheless , there was also evidence of significant interrealm variation in both model slopes and intercepts , potentially reflecting historically driven differences in the richness of regional species pools ( Fig 5 and S7 Table ) 14 ., Having accounted for this between-region variation , correlations between coexistence and richness were substantially strengthened compared to the global model ( Afrotropics: ρ = 0 . 54 , Australasia: ρ = 0 . 80 , Indomalaysia: ρ = 0 . 72 , Neotropics: ρ = 0 . 53 , Palearctic: ρ = 0 . 34 , p < 0 . 001 in all cases ) ., The sole exception to this pattern was the Nearctic , where coexistence was negatively correlated with richness ( ρ = -0 . 11 ) ., The consistent positive effect of sister species coexistence on assemblage richness is at odds with purely historical explanations for richness gradients based solely on differences in opportunities for species diversification 11 , 29 and also challenges the idea that high levels of coexistence among sister species simply reflects a lack of community saturation in more depauperate biotas 54 , 73 ., Instead , our analysis confirms the significant role of range expansions in establishing broad-scale gradients in species richness ., A robust demonstration of the fundamental relationships linking energy availability , coexistence , and assemblage richness has hitherto been lacking because of the difficulties in accounting for purely historical processes , including variation in the size of regional species pools or differences in the evolutionary time available for speciation and range expansion 14 , 25 , 74 ., By focusing on interactions between avian sister species of known evolutionary age , we have shown that the probability of coexistence increases with energy availability and that this effect cannot be explained by such historical artefacts ., We further demonstrate that the geographical variation in levels of coexistence among closely related lineages is strongly aligned with observed gradients in assemblage richness , supporting a mechanistic link between the global-scale increase in species richness with energy availability ., The increasing application of molecular phylogenetic data to understanding macroecological patterns has often highlighted the importance of evolutionary history in the origin of broad-scale richness gradients , with many studies supporting a model in which the increase in richness with energy availability arises largely as a byproduct of accelerated rates of species diversification or the greater age and area of tropical biomes 11 , 14 , 25 , 29 , 30 , 32 ., Our phylogenetic analysis of coexistence dynamics is at least partially consistent with this body of work by identifying the critical importance of evolutionary time in enabling the accumulation of sympatry following the generation of species in allopatry ., However , our results also reveal that these historical effects are not sufficient on their own to explain patterns of coexistence and that the formation of species richness gradients thus depends on how energy availability determines the assembly of species into communities ., While resolving the historical dynamics of coexistence based on current species distributions is challenging , our analyses suggest that energy availability has relatively little influence on the rate at which coexistence is established following speciation , and that the predominant effect of energy availability is to maintain coexistence over longer periods of time ., This effect of productivity on the duration of coexistence suggests that the key factor is not an accelerated transition into sympatry as a result of weaker or more diffuse species interactions or by faster rates of character displacement 61 , 75 ., Indeed , it has previously been shown that negative species interactions can constrain the establishment of coexistence following speciation in vertebrates , even in highly productive tropical regions 47 , 48 , 54 ., Instead , our results are consistent with the theory that higher energy availability , acting either directly or indirectly on population dynamics 18 and niche partitioning 24 , reduces the rate of local extinction , ultimately allowing more species to be “packed” into productive tropical ecosystems 23 ., The relative importance of ecological mechanisms linking energy availability to coexistence remains to be resolved ., In particular , while the total biomass and numerical abundance of avian communities appears to generally increase with ecosystem productivity , implying a reduction in local extinction , whether this can account for the magnitude of observed differences in coexistence is unclear 3 , 36 , 76 ., The increased vegetation complexity supported by higher energy environments seems a prime candidate for facilitating the extended coexistence of ecologically similar bird species 24 , 77 , whereas other energy-related processes may exert a similar influence by facilitating local adaptation 78 ., It also seems likely that processes enhancing coexistence will interact synergistically with macroevolutionary diversification , the main alternative explanation for the accumulation of higher species richness in productive regions 14 , 25 , 29 , 32 ., Indeed , according to models of adaptive radiation , a greater capacity for local coexistence is expected to elevate both rates of diversification and species-carrying capacities at the regional scale 1 , 46 ., Ultimately , how greater fluxes of energy and the concomitant increases in resource availability influence species coexistence will likely depend on context , region , scale , and clade ., Nevertheless , our results suggest that energetic constraints on coexistence play a fundamental role in shaping contemporary gradients in species richness , and form a vital component of any mechanistic explanation for global patte

Introduction, Results and Discussion, Materials and Methods

The association between species richness and ecosystem energy availability is one of the major geographic trends in biodiversity ., It is often explained in terms of energetic constraints , such that coexistence among competing species is limited in low productivity environments ., However , it has proven challenging to reject alternative views , including the null hypothesis that species richness has simply had more time to accumulate in productive regions , and thus the role of energetic constraints in limiting coexistence remains largely unknown ., We use the phylogenetic relationships and geographic ranges of sister species ( pairs of lineages who are each other’s closest extant relatives ) to examine the association between energy availability and coexistence across an entire vertebrate class ( Aves ) ., We show that the incidence of coexistence among sister species increases with overall species richness and is elevated in more productive ecosystems , even when accounting for differences in the evolutionary time available for coexistence to occur ., Our results indicate that energy availability promotes species coexistence in closely related lineages , providing a key step toward a more mechanistic understanding of the productivity–richness relationship underlying global gradients in biodiversity .

The increase in the number of species with the availability of energy in the environment is one of the most general but least understood patterns in global biodiversity ., The finite amount of energy flowing through an ecosystem has long been suspected to place a fundamental constraint on the ability of species to subdivide ecological resources , with greater energy availability—and , thus , ecosystem productivity—in the tropics , potentially facilitating increased levels of coexistence ., However , empirical support for this hypothesis has been lacking , raising the possibility that richness is higher in productive ecosystems for largely historical reasons , including the greater geological age and area of tropical biomes , which increases the period of time available for diversity to accumulate ., By combining phylogenetic and geographic data from across the world’s bird species , we show that greater ecosystem productivity is associated with an increased probability of coexistence among closely related lineages and that this pattern contributes to the higher species richness in the tropics ., Our results confirm that contemporary gradients in species richness are fundamentally shaped by energetic constraints on coexistence .

taxonomy, biogeography, ecology and environmental sciences, population genetics, vertebrates, animals, animal phylogenetics, phylogenetics, data management, phylogenetic analysis, speciation, molecular biology techniques, population biology, zoology, research and analysis methods, ecological metrics, computer and information sciences, geography, birds, ecosystems, phylogeography, evolutionary systematics, molecular biology, species diversity, molecular biology assays and analysis techniques, ecology, earth sciences, genetics, biology and life sciences, evolutionary biology, evolutionary processes, organisms

An analysis of phylogenetic and geographic data across the worlds bird species reveals that the probability of species coexistence is constrained by the availability of energy in the environment.

journal.ppat.1001226

The Killing of African Trypanosomes by Ethidium Bromide

Trypanosoma brucei , the African trypanosome , is a protozoan parasite causing human sleeping sickness and the disease nagana in cattle ., For both humans and livestock , there is a compelling need for less toxic and more effective drugs ., One drug , ethidium bromide ( EB , known in the veterinary world as homidium; see structure in Fig . S1 ) was synthesized as a trypanocide over a half-century ago by chemists at Boots Pure Drug Co . , Ltd , in Nottingham , U . K . 1 ., EB is an intercalating agent 2 widely used as a fluorescent stain for DNA in electrophoresis gels although many scientists are concerned about its mutagenicity ., Given its potential dangers , many would be shocked to learn that EB is still used for treating cattle that provide beef and milk for human populations 3 ., EB has long been known to promote loss of the trypanosomes mitochondrial genome , the giant DNA network named kinetoplast DNA ( kDNA ) ., kDNA is an amazing structure , constituting ∼5% of the cells total DNA , that consists of several thousand minicircles ( each 1 kb ) and a few dozen maxicircles ( each 23 kb ) ., These circles are interlocked together in one huge planar network that has a topology like that of medieval chain mail ( reviewed in 4 , 5 ) ., The kDNA network in vivo is condensed into a compact disk-shaped structure residing in the mitochondrial matrix ., Maxicircles , like conventional mitochondrial DNAs , encode rRNAs and a few mitochondrial proteins ( e . g . , subunits of respiratory complexes ) ., However , to form a functional mRNA with an open reading frame , most maxicircle transcripts are edited by insertion or deletion of uridylates at specific sites ., Minicircles encode guide RNAs that are editing templates ( reviewed by 6 ) ., The unusual characteristics of kDNA and its replication pathway ( discussed below ) , coupled with the lack of DNA networks in mammalian cells , would suggest that kDNA and its replication proteins should be attractive targets for selective chemotherapy ., But this possibility was ignored for many years because of the existence of bloodstream form ( BSF ) trypanosomes lacking kDNA ., These dyskinetoplastic ( Dk ) BSFs appear spontaneously or have been induced by treatment with DNA binding agents such as acriflavin or EB 7 , 8 , 9 ., Their existence suggested that kDNA is not essential for viability of BSFs and therefore would not be a drug target ., Acriflavin causes loss of kDNA in other trypanosomatids , such as Leishmania tarentolae , but in this case the cell dies because maxicircles encode essential mitochondrial proteins 10 ., However , a recent discovery changed our thinking on kDNA as a target for chemotherapy ., A report that RNAi knockdown of a mitochondrial RNA ligase involved in editing is lethal to BSFs indicated that editing , and therefore kDNA , are indeed needed for viability 11 ., Subsequent studies revealed that an essential maxicircle gene product in BSFs is the A6 subunit of the membrane-embedded Fo component of the F1 Fo ATP synthase 12 ., In procyclic trypanosomes this enzyme makes ATP , but in BSFs it couples the reverse reaction , ATP hydrolysis , to generation of a mitochondrial membrane potential required for viability 12 , 13 ., If editing of A6 is inactivated in either lifecycle stage , then the ATP synthase cannot function and the parasite dies ., The reason that some Dk cell lines survive is that they acquire a compensating mutation in a nuclear gene ( encoding the gamma subunit of the F1 portion of the ATP synthase ) that rescues Dk cells and permits their survival 12 , 14 , 15 ., Since the compensating mutation occurs at low frequency , kDNA and proteins involved in its replication or gene expression should be valid drug targets in BSFs ., Since EB-mediated kDNA loss could be due to a block in network replication , we will briefly discuss this pathway ( reviewed in 4 , 5 ) , focusing first on minicircles ., In the first step , a topoisomerase ( topo ) II releases monomeric covalently-closed minicircles from the network into the kinetoflagellar zone ( KFZ ) , a region of the mitochondrial matrix between the kDNA disk and membrane near the flagellar basal body ., Proteins in the KFZ initiate and propagate unidirectional theta-type replication ., Free minicircle progeny probably segregate in the KFZ and then migrate to the antipodal sites , two protein assemblies flanking the kDNA disk and positioned ∼180° apart ., Here RNA primers are removed , and most but not all gaps and nicks ( we will refer to these discontinuities as gaps ) are repaired ., The newly replicated minicircles , still containing at least one gap , are then attached to the network periphery by the mitochondrial topoisomerase II ( TbTopoIImt ) positioned in the antipodal sites ., Following completion of replication , when the minicircle copy number has doubled , the network splits in two and minicircle gaps are repaired ., The progeny kinetoplasts then segregate into the daughter cells during cytokinesis ., Much less is known about maxicircle replication ., They also replicate unidirectionally as theta structures , but unlike minicircles , they remain linked to the network during replication ( see 16 , 17 , 18 for information on maxicircle replication ) ., In this paper we report new and unexpected effects of EB on trypanosomes that reveal , for the first time , how this drug kills these parasites ., We tested EB concentrations ranging from 0 . 01 µg/ml to 10 µg/ml on growth of BSF T . brucei 427 ., Although 0 . 02 µg/ml EB stopped growth in 3 days , in most experiments we used 2 µg/ml ( 5 µM ) EB to compare our results with previous data 19 ., This concentration arrested growth and killed the cells ( Fig . 1A ) ., We next used fluorescence microscopy of DAPI-stained cells to examine EBs effect on the kinetoplast ( Figs . 1B , C ) ., It was not surprising that EB initially caused production of small kinetoplasts , and by 72 h , more than 75% of cells had no detectable kDNA ( we refer to these cells as Dk even though we did not confirm that they are completely devoid of kDNA ) ., Examples of cells with normal kinetoplasts , small kinetoplasts , and none at all ( Dk ) are shown in Fig . 1C ., We then isolated and DAPI-stained networks from EB-treated cells and measured their surface areas ( Fig . 1D ) ., Networks from untreated cells averaged ∼5 . 5 µm2 ( in agreement with previous measurements 20 , 21 ) , but after a 3 day EB treatment had shrunk to an average area of ∼2 . 1 µm2 ( Fig . 1D ) ., Because kDNA isolation involves centrifugation , there may have been selective loss of the smallest networks; thus the average area may be smaller than indicated ., To prove that kinetoplast shrinking is due to loss of minicircles and maxicircles , we digested total DNA with Hind III/Xba I . After electrophoresis , we probed a Southern blot for maxicircle and minicircle fragments ( Fig . 1E ) ., Taking into account the loading control , more than half of the minicircles and maxicircles were lost by 72 h ., Covalently-closed circular DNAs isolated from prokaryotic or eukaryotic cells are always negatively supertwisted , with one exception ., Remarkably , covalently-closed minicircles in a kDNA network 22 or free replication intermediates 23 are fully relaxed in vivo ., However , we expected that minicircles isolated from EB-treated cells , either in a network or free , would become negatively supertwisted ., This prediction was based on the equation , Lk = Tw + Wr , that relates linking number ( Lk ) to twist ( Tw ) and writhe ( Wr ) 24 ., Since minicircles in vivo are relaxed ( Wr =\u200a0 ) , then Lk = Tw , and for a 1 kb minicircle Tw is ∼96 ( assuming 10 . 4 bp/helical turn ) ., EB in the mitochondrial matrix should intercalate into minicircles , thereby reducing their helical twist ., Since EB binding does not change Lk , the decrease in twist is compensated by an increase in writhe , or positive supertwisting ., A mitochondrial topoisomerase could then remove the positive supertwists , reducing Lk ., EB , however , would be still bound to the DNA and would be removed only when DNA is isolated ., After EB removal , the twist would revert to its normal level of ∼96 , but since Lk had been reduced in vivo , the writhe would decrease , producing negative supertwisting ., To test the effect of 2 µg/ml EB on free minicircle replication intermediates , we fractionated total DNA from treated cells on an agarose-EB gel that resolves relaxed covalently-closed minicircles ( CC; the replication precursors ) from gapped minicircles ( G , the replication products ) ., A Southern blot ( Fig . 2A ) revealed a profound increase in a new minicircle species , designated fraction E , that migrated near covalently-closed minicircles ., Fraction E forms a smear that broadened between 3 and 72 h , and at later time points its level declined ., There was also an increase in multimeric minicircles , and at least one species , probably an interlocked dimer ( marked * ) , formed a smear like that of fraction E . As observed previously 19 , EB caused a rise in linearized minicircles ( maximum at 1 h , data not shown ) that then leveled off ., Gapped free minicircles rose during the first 3 h followed by a small decline ., We then set out to identify fraction E , first using a 2-D gel to compare DNA from untreated cells with that from cells treated 6 h with 2 µg/ml EB ., The first dimension conditions were the same as those used for the gel in Fig . 2A; the second dimension was run in 30 mM NaOH ., This gel system is effective in resolving free minicircle species 25 , 26 ., We then probed a Southern blot with 32P-oligonucleotides of comparable specific radioactivity and complimentary to either the L-strand ( Fig . 2B , left panel ) or H-strand ( Fig . 2B , right panel ) ., From untreated cells , we observed the well-documented resolution of covalently-closed minicircles ( CC ) and gapped minicircles ( G ) ., These two species hybridized equally to both probes because their strands are equimolar ., Most importantly , this experiment showed that fraction E consists of minicircles in the 1-kb range that also have roughly equimolar amounts of L and H strands ., In our second approach , we treated fraction E ( purified by sucrose gradient sedimentation 25 ) with E . coli topo I , an enzyme that relaxes negative but not positive supertwists 27 ., Electrophoresis on an agarose-EB gel ( Fig . 2C ) demonstrated that the fraction E smear collapsed into a band that migrated with covalently-closed relaxed monomers ., Finally , EM of Fraction E provided the most compelling evidence on its structure ( Fig . 2D ) , proving that Fraction E is a family of supertwisted free minicircles ., The topo I experiment ( Fig . 2C ) showed that the supertwisting is negative ., In addition to supertwisting , DNA can compensate for severe underwinding by forming single-stranded regions and regions of left-handed helix 28 ., In fact , we previously detected Z-DNA in highly supertwisted free minicircles from cells undergoing RNAi of p38 , a protein involved in replication initiation 25 ., To determine whether a region of Z-DNA forms in free minicircles from EB-treated cells , we immunoprecipitated total DNA with anti-Z DNA antibody , centrifuged the immune-complexes , and fractionated the DNA from the supernatant and pellet on an agarose-EB gel ., A Southern blot ( Fig . 2E ) showed that minicircles from untreated cells or following 6 h of EB treatment had no fraction E in the pellet , indicating that these minicircles contain no Z-DNA ., However , after 16 or 24 h of EB exposure , we observed part of fraction E ( with the slowest electrophoretic mobility ) in the pellet , demonstrating that some fraction E minicircles contain regions of Z-DNA ., We conclude that fraction E minicircles have a broad distribution of linking numbers and the most severely underwound have sequences that flip into Z-DNA ., T . brucei has 3 known mitochondrial topoisomerases , TbTopoIImt 29 , TbTopoIAmt 30 , and TbTopoIB ( the latter is functional in both nucleus and mitochondrion 31 ) ., In RNAi experiments in Fig . S2 , we showed that knockdown of TbTopoIImt , but not TbTopoIAmt or TbTopoIB , prevented reduction of the linking number of EB-bound free minicircles and thereby inhibited production of Fraction E . Thus , TbTopoIImt is responsible for supertwisting free minicircles isolated from EB-treated cells ., Experiments already presented provide strong evidence that EB blocks free minicircle replication ., The dramatic rise in covalently-closed monomeric free minicircles in the form of Fraction E ( Fig . 2A ) indicates that minicircle release from the network occurs in the presence of EB ., In addition , their accumulation indicates that the subsequent step , initiation of replication , is inhibited ., We therefore assessed replication directly by measuring incorporation of bromodeoxyuridine ( BrdU ) , a thymidine analog , into free minicircles ., We incubated log phase cells ( ±2 µg/ml EB for 6 h ) with 50 µM BrdU for the last 40 min ., We then isolated total DNA , and fractionated the minicircle species first on a 1-dimensional agarose-EB gel ( Fig . 3A , left panel ) ., A Southern blot confirmed that minicircles were more abundant after EB treatment because of the large increase in Fraction E and multimers ., To assess BrdU incorporation , we ran the same amount of DNA on a 2-dimensional gel ( like that in Fig . 2B ) ., We used this gel to evaluate whether any BrdU-labeled minicircle species accumulated in the presence of EB ., Probing a blot with anti-BrdU antibody revealed BrdU incorporation into gapped minicircles in the absence of EB but little or none in any minicircle species during the EB treatment ( Fig . 3B ) ., This experiment provided even stronger evidence that EB inhibits initiation of minicircle replication ., Based on our findings with free minicircles , we predicted that minicircles in networks isolated from EB-treated cells would also be negatively supertwisted and we used EM to test this possibility ( for this experiment it was essential to extract EB from the DNA prior to EM ) ., We were astonished that most minicircles in networks isolated from cells exposed to 2 µg/ml EB for up to 24 h remained relaxed ., Fig . 4B shows a network from a cell treated for 24 h ( compare with untreated network in Fig . 4A ) ., Although it is impossible to evaluate many minicircles in the network interior , most on the periphery are relaxed ., Only a few ( some marked by arrows ) appear highly twisted ., Fig . S3 shows many more examples of networks from cells treated with EB for 6 h , 15 h , and 24 h ., By 24 h , most networks were smaller and more loosely packed ( Fig . S3 , networks J , K , and L ) ., In these it was easier to evaluate minicircles in the network interior , and most were relaxed ., However , one example ( Fig . S3 , network F ) has a substantial region of the network periphery in which most minicircles are twisted ., From the data presented so far , EB treatment of trypanosomes causes severe underwinding of free minicircles but , despite our inability to assess minicircles in the crowded interiors of many networks , it does not have this effect on many and probably most network minicircles ., This is remarkable because both DNAs reside in the same cellular compartment , the mitochondrial matrix ., One possibility is that the EB caused nicking of network minicircles , thereby preventing supertwisting ., To test for nicking , we studied the same networks used in the previous section , but we spread them for EM in the presence of a high concentration of EB ( 100 µg/ml ) ., If the network minicircles are nicked , they could not supertwist under these conditions ., However , as shown by the example in Fig . 4C ( others are in Fig . S3 , networks H and M ) , we observed extensive supertwisting of minicircles in most networks ., However , some , such as network I in Fig . S3 , contained relaxed minicircles ., These networks must contain mostly gapped minicircles , and they will be further evaluated below ., These data prove that most networks from EB-treated cells contain minicircles that are covalently-closed and that lack of supertwisting is not due to nicking ., A second possibility is that network minicircles bind EB , but there is no topoisomerase available to reduce the minicircle Lk ., We showed in Fig . S2 that there is considerable active TbTopoIImt present ., Furthermore , we recently reported that TbTopoIImt is present throughout the kDNA disk where it mends holes made by minicircle release during replication 20 ., Thus , this possibility is unlikely ., We addressed a third possibility , that network minicircles bind little or no EB , by fluorescence microscopy of EB-treated live cells ., We found that brief EB treatment ( 2 µg/ml added to culture medium ) caused distinct staining of the kinetoplast in nearly all cells , but it is possible that some of this staining could be due to free minicircles ., Unfortunately , the high motility of trypanosomes precluded capture of adequate images of live cells ., Even when the cells were restrained by adherence to poly-lysine coated slides and other methods , they quickly died and lost their staining ., Before this happened their localized movement prevented imaging ., Instead , Fig . S4 shows images of 16 fluorescent kinetoplasts in formaldehyde-fixed cells ., Our conclusion is that kDNA in vivo probably binds some EB , but it could be far less than that observed with free minicircles ., A likely reason for low EB binding could be that the condensed network is associated in vivo with basic proteins 32 , some of which may stabilize the network in its disk-shaped configuration 33 ., Such proteins could prevent unwinding of the minicircle helix and therefore would block EB binding ., One candidate protein for this role is p19 ( GeneDB Accession Number Tb11 . 02 . 2420 ) , recently discovered in our laboratory and shown to localize throughout the kDNA disk ( unpublished studies of J . Wang , Z . Zhao , B . Liu , P . T . Englund , and R . E . Jensen ) ., Recombinant p19 condenses isolated networks in vitro ( not shown ) to a size comparable to that of the kinetoplast in situ ., In fluorometric experiments , we observed that network-bound recombinant p19 inhibited EB binding as measured by the marked reduction in EB fluorescence ( Fig . 5 , compare middle panel with left panel ) ., In a similar assay , p19 could displace EB from a network ( Fig . 5 , right panel ) ., Thus , the most likely explanation for the absence of supertwisting in many if not most network minicircles is that EB binds poorly to networks in vivo ., Perhaps those minicircles that do become supertwisted are devoid of proteins , and it is EB binding to these , along with free minicircles , that contributes to the kinetoplast fluorescence observed in Fig . S4 ., As mentioned above , some networks from EB-treated cells , examined by EM in the presence of 100 µg/ml EB , had many minicircles that did not twist , suggesting that these minicircles were gapped ( an example is network I in Fig . S3 ) ., We further investigated these networks by in vitro labeling the 3′-OH groups at gaps with terminal deoxynucleotidyl transferase ( TdT ) and fluorescein-dUTP 34 ., In networks from cells untreated with EB , we detected the well-characterized polar labeling of partly replicated networks 34 , 35 , 36; labeling is polar because gapped minicircles are attached to the network adjacent to the antipodal sites ., From these cells , never exposed to EB ( Fig . 6A ) , 28% of networks were stained by TdT ( TdT-positive ) ., This value changed little during 24 h of EB treatment ( see time course in Fig . 6B ) ., However , EB dramatically changed the labeling pattern , causing an increase in uniformly labeled networks and a steady decline in polar labeling ., The latter suggested that reattachment of newly-replicated minicircles to the network periphery must be strongly reduced by EB , as expected from the fact that EB blocks minicircle replication ., In the Discussion we will comment further on these data ., If EB kills trypanosomes by blocking initiation of replication of free minicircles , then it would be logical to predict that Dk trypanosomes would be resistant to EB killing ., We therefore studied the effect of EB on Dk 164 , a BSF trypanosome lacking kDNA 8 , 37 ., Our cytotoxicity assay , using a range of EB concentrations , involved colorimetric measurement of acid phosphatase activity released by lysing living cells after 24 h in wells of a 96-well plate 38 ., To our great surprise , we found that EB efficiently killed Dk parasites , and its EC50 , 0 . 3 µg/ml , was slightly lower that of the wild type 427 strain , 0 . 65 µg/ml ( Fig . 7A ) ., Thus , EB must have an efficient killing mechanism unrelated to kDNA ., A likely possibility for the killing of Dk trypanosomes was that EB also targets nuclear DNA replication ., We therefore incubated 427 BSFs with BrdU in the presence and absence of EB for 6 h ., Incorporation of this thymidine analog into nuclear DNA increased with time in untreated cells and was inhibited substantially with 2 µg/ml EB ( Fig . 7B ) ., At first glance , the data in Fig . 7A suggested that a killing mechanism targeting nuclear replication may be as important , or possibly more important , that that targeting kDNA ., However , we then considered the possibility that our 24 h cytotoxicity assay may not reveal the effects of EB-mediated kDNA loss in wild type 427 cells ., The reason is that the killing would not occur until essential maxicircle gene products were depleted , which , depending on their half-lives , may take a few days ., Thus , the 427 cells might contain a time bomb that would not explode until after termination of the 24 h cytotoxicity assay ., To evaluate this possibility , we conducted a new cytotoxicity assay using 427 trypanosomes ( Fig . 7C ) ., In this assay , we collected cells not only for evaluation of the effect of EB on growth , but also for measurement of kinetoplast size by DAPI staining ., In addition , we measured nuclear replication by adding BrdU to the wells for the last 2 h of the 24 h assay ., As shown in Fig . 7C , inhibition of nuclear replication occurred at the same EB concentration as the growth effect; thus , inhibiting nuclear replication kills with little delay ., In contrast , kDNA loss occurs at a >10-fold lower EB concentration and this loss will ultimately lead to cell death but not during the 24 h cytotoxicity assay ., To further evaluate this possibility , we chose an EB concentration , 0 . 02 µg/ml , that , based on the graphs in Fig . 7C , should eventually kill 427 trypanosomes by targeting kDNA and not kill DK-164 cells which would require a higher EB concentration to inhibit nuclear replication ., As predicted , there was little effect of EB on Dk-164 cells ( Fig . 7D ) ., Also , as expected , there was little effect on 427 cells during the first 24 h ( the duration of the cytotoxicity assay ) , but survivors were reduced by 75% at day 2 and few cells were alive at day 3 ., We also noted in our initial studies mentioned in the first paragraph of Results that 0 . 02 µg/ml EB killed trypanosomes within 3 days ., Most studies in this paper on the effect of EB were conducted at 2 µg/ml ., Now it was essential to evaluate further the effects of 0 . 02 µg/ml EB on minicircle and nuclear DNA replication ., For minicircles we conducted an analysis of free minicircles to determine whether covalently-closed free minicircles ( including Fraction E ) accumulate ( because they are unable to initiate replication ) ., In a control experiment , similar to that in Fig . 2A , we found that 6 h treatment with 2 µg/ml EB caused a 36-fold increase in covalently-closed minicircles ( Fig . 7E ) ., Reducing the EB concentration 100-fold to 0 . 02 µg/ml , increased the covalently-closed minicircles 15-fold , still a very potent inhibition of replication ., As with 2 µg/ml , the maximum effect of 0 . 02 µg/ml was at 6 h and at both concentrations there was a large increase in minicircle oligomers ., It was not surprising that 0 . 02 µg/ml EB had a smaller effect in reducing the free minicircle linking number; thus its fraction E smear is more compact ., To measure the effect of 0 . 02 µg/ml EB on nuclear replication , we treated a culture for 0 , 6 , 12 , and 24 h ., At each time point we incubated a sample with 3Hthymidine for 2 h and then measured acid-insoluble radioactivity ( Fig . 7F ) ., Since kDNA constitutes only about 5% of the total DNA , nearly all of the incorporation is in nuclear DNA ., These results show that 0 . 02 µg/ml EB has no effect on nuclear replication at 6 h and a small effect thereafter ., All the experiments in Fig . 7 provide strong evidence that the most sensitive mechanism by which EB kills trypanosomes is to block minicircle replication initiation ., However , inhibiting nuclear replication also contributes to the trypanocidal activity ., Our goal in these studies was to determine whether EB kills trypanosomes by its action on kDNA and , if it does , to determine the killing mechanism ., One remarkable and unexpected effect of EB treatment was that it caused profound supertwisting of free minicircles ( Fig . 2 ) , whereas many if not most network minicircles , in the same cellular compartment , remained relaxed ( Figs . 4 , S3 ) ., The most likely explanation is that the EB binding to the network , although detectable by fluorescence microscopy ( Fig . S4 ) , occurs at a much lower level than it does to free minicircles ., The large reduction in EB binding to network minicircles is most likely explained by the fact that basic proteins like p19 condense the network in vivo and by preventing helix unwinding , inhibit EB binding ( Fig . 5 ) ., The conclusions from this experiment are limited because they are based on only one protein , p19 , whose function is unknown , and because the kDNA disk in vivo is likely bound and stabilized by multiple proteins 32 ., However , the p19 experiments clearly demonstrate that such proteins can reduce EB binding in vivo ( Fig . 5 ) ., Polyamines also bind DNA and prevent EB binding 39 , but proteins seem more likely to be compartmentalized , binding to networks and not free minicircles ., Our EM data provides some clues into the replication status of maxicircles ., Gel electrophoresis of topo II-decatenated networks 40 from cells treated with 2 µg/ml EB for 6 to 24 h revealed that 20–50% of maxicircles were covalently-closed ( data not shown ) ., Furthermore , in 121 EM images of networks ( from cells treated similarly with EB and then observed by EM after EB removal ) , we observed no supertwisted maxicircles ., Thus , the covalently-closed network-bound maxicircles , like most network minicircles , must bind little if any EB in vivo ., We speculate that the lack of EB binding to maxicircles , as in minicircles , is due to bound protein ., If EB does not bind to the maxicircles , it is possible that it has no effect on maxicircle replication ., Further studies are needed to address this issue ., Our most significant finding was that the most effective mechanism by which EB kills trypanosomes is by inhibition of initiation of free minicircle replication ., We initially based this conclusion on the dramatic accumulation of covalently-closed free minicircles , the substrates for replication ( Figs ., 2A ( using 2 µg/ml EB ) and 7E ( using 0 . 02 µg/ml EB ) ) ., Especially in the case of 2 µg/ml EB , the high level of EB that bound these molecules in vivo not only resulted in the development of extensive negative supertwisting after isolation but also , by distorting their helix in vivo , must have prevented assembly of replication proteins at the origin ., The inhibition of BrdU incorporation into gapped minicircles confirmed the replication block ( Fig . 3 ) ., Since newly replicated free minicircles attach to the network poles adjacent to the antipodal sites , the EB-mediated decline in networks with polar TdT labeling led to the same conclusion ( Fig . 6 ) ., But why does the number of networks uniformly-labeled with TdT increase during EB treatment ?, The likely reason is that a partially-replicated network contains gapped minicircles ( labeled by TdT ) in the two polar regions and covalently-closed minicircles ( not yet replicated and unlabeled by TdT ) in the central region 34 , 35 ., In the presence of EB , few if any minicircles attach to the network poles , preventing enlargement of the polar regions ., However , Fig . 2A shows that covalently-closed minicircle release continues , and these must derive from the central region of the network ., Once all are released , the two polar regions , containing gapped minicircles , then fuse ., Thus the network appears , after TdT labeling , to be uniformly-labeled and small in size ., As the network shrinks , minicircle release may become less precise , resulting in release of multimers ( Fig . 2A ) ., We previously found that EB poisons mitochondrial TbTopoIImt ( but not nuclear topo II ) and that cell lysis by SDS-proteinase K results in linearization of ∼2% of the minicircles 19 ., This inhibition of TbTopoIImt is unlikely responsible for cell death because only a small fraction of the TbTopoIImt is trapped as a cleavable complex ( for comparison , a more powerful topo II poison , etoposide , causes linearization of 12% 41 ) ., Furthermore , the fact that TbTopoIImt is responsible for the EB-mediated reduction in free minicircle linking number ( Fig . S2 ) proves that much of this enzyme is catalytically active in the presence of EB ., Since EB blocks initiation of replication of free minicircles , we were initially surprised , and troubled , to find that Dk cells were also killed by EB ., However , further study showed that the EB-susceptibility of Dk cells could be explained by inhibition of nuclear DNA replication ( Fig . 7B ) ., Furthermore , the effect of EB on kinetoplast size occurred at a >10-fold lower concentration , indicating that kDNA replication is the primary target for EB killing of wild type trypanosomes ., Our realization that killing of wild type cells by low EB concentrations does not occur during our 24 h cytotoxicity assays fully clarified this mechanism ., The delay in killing must be due to the time needed for the cell to run out of its essential maxicircle gene product , the A6 subunit of the ATP synthase 12 ., When we analyzed free minicircles from cells treated 6 h with 0 . 02 µg/ml EB , we confirmed that this low concentration also inhibits free minicircle replication ( Fig . 7E ) ., Although our cytotoxicity assays suggest that EB acts primarily on kDNA , this drug has at least some effect on nuclear DNA replication ., The effect is modest at low concentrations ( 0 . 02 µg/ml EB; see Figs . 7C and 7F ) , but inhibition of nuclear replication becomes significant at higher concentrations ( 2 µg/ml EB; see Fig . 7C and compare Fig . 7B with 7F ) ., Therefore , the contribution of inhibition of nuclear DNA replication to trypanosome killing depends on the level of EB in the treated animal ., The blood concentration of ethidium in cattle injected intra-muscularly with the standard dose of 1 mg/kg is 0 . 070–0 . 268 µg/ml at 15 min and ∼0 . 075 µg/ml after 24 h 42 , 43 , 44 ., These concentrations are for the free base form of ethidium ., At the highest concentration detected , 0 . 268 µg/ml ethidium base ( equivalent to 0 . 34 µg/ml EB ) , which is transient , inhibition of nuclear DNA replication is only about 40% of maximum effectiveness ( see Fig . 7C ) ., The 24 h level , ∼0 . 075 µg/ml ( equivalent to 0 . 095 µg/ml EB ) , is about 5 times higher than the 0 . 02 µg/ml EB used in our experiments , suggesting that inhibition of nuclear DNA replication could contribute to killing ., One problem with comparing drug activity in vitro to that in animals is that many drugs bind tightly to serum proteins , thereby reducing markedly in animal

Introduction, Results, Discussion, Materials and Methods

Introduced in the 1950s , ethidium bromide ( EB ) is still used as an anti-trypanosomal drug for African cattle although its mechanism of killing has been unclear and controversial ., EB has long been known to cause loss of the mitochondrial genome , named kinetoplast DNA ( kDNA ) , a giant network of interlocked minicircles and maxicircles ., However , the existence of viable parasites lacking kDNA ( dyskinetoplastic ) led many to think that kDNA loss could not be the mechanism of killing ., When recent studies indicated that kDNA is indeed essential in bloodstream trypanosomes and that dyskinetoplastic cells survive only if they have a compensating mutation in the nuclear genome , we investigated the effect of EB on kDNA and its replication ., We here report some remarkable effects of EB ., Using EM and other techniques , we found that binding of EB to network minicircles is low , probably because of their association with proteins that prevent helix unwinding ., In contrast , covalently-closed minicircles that had been released from the network for replication bind EB extensively , causing them , after isolation , to become highly supertwisted and to develop regions of left-handed Z-DNA ( without EB , these circles are fully relaxed ) ., In vivo , EB causes helix distortion of free minicircles , preventing replication initiation and resulting in kDNA loss and cell death ., Unexpectedly , EB also kills dyskinetoplastic trypanosomes , lacking kDNA , by inhibiting nuclear replication ., Since the effect on kDNA occurs at a >10-fold lower EB concentration than that on nuclear DNA , we conclude that minicircle replication initiation is likely EBs most vulnerable target , but the effect on nuclear replication may also contribute to cell killing .

Trypanosoma brucei is a protozoan parasite that causes cattle disease and human sleeping sickness in Africa ., Trypanosomes are primitive eukaryotes with atypical biological features ., One well-studied example is their mitochondrial genome , known as kinetoplast DNA or kDNA ., kDNA , resembling medieval chain mail , is a giant network of interlocked DNA rings known as minicircles and maxicircles ., Here we study ethidium bromide , a drug used for over 50 years for treating cattle infected with trypanosomes ., EBs killing mechanism has been elusive , and many thought it could not involve kDNA ., We now report that EB kills these parasites by blocking the initiation of minicircle replication , and , at higher drug concentration , it also blocks nuclear replication .

molecular biology/dna replication, infectious diseases/neglected tropical diseases, pharmacology, microbiology/parasitology, infectious diseases/protozoal infections, biochemistry/drug discovery

journal.pntd.0002628

Cofactor-Independent Phosphoglycerate Mutase from Nematodes Has Limited Druggability, as Revealed by Two High-Throughput Screens

For a protein to advance as a potential drug target , it should not only be important in pathogen survival and/or virulence , but also must be “druggable , ” i . e . , susceptible to modulation with drug-like compounds ., Many otherwise promising proteins simply do not have binding pockets that lend themselves to therapeutic intervention 1 , 2 ., Historically , metabolic enzymes have been considered relatively druggable because ( by definition ) they bind small molecules , which can sometimes be mimicked by drugs 3 ., Still , since enzymes reactants may bear little resemblance to drugs in their hydrophilicity or other properties 4 , a proteins amenability to therapeutic modulation cannot be guaranteed even if it is an enzyme ., Another aspect of druggability concerns the chemical tractability of hit scaffolds identified through the HTS process ., While assemblers of small molecule libraries strive to include tractable molecules , depending upon the source ( s ) of those libraries , not all scaffolds may be amenable to chemical modification ., Lymphatic filariasis ( LF ) is an infectious disease caused by the parasite nematodes Wuchereria bancrofti , Brugia malayi , and Brugia timori ., Southeast Asia and sub-Saharan Africa harbor most of the worlds ∼120 million current infections ., It has been estimated that 40 million people suffer significant morbidity and/or disfigurement due to filariasis 5 ., Most disfigurement is caused by adult-stage worms ( macrofilariae ) , which are more impervious than immature worms ( microfilariae ) to current drugs such as diethylcarbamazine , ivermectin , and albendazole 6 ., Thus , there is a strong need for new drugs , especially those that affect previously unexploited target proteins ., In the ongoing search for new LF drug targets , cofactor-independent phosphoglycerate mutase ( iPGAM ) has attracted significant interest ., iPGAM catalyzes the interconversion of 2-phosphoglycerate ( 2-PG ) and 3-phosphoglycerate ( 3-PG ) in glycolysis and gluconeogenesis ., The candidacy of iPGAM as a LF drug target is supported by several lines of evidence ., RNAi knockdown of the C . elegans iPGAM , whose amino acid sequence is 70% identical to that of the B . malayi iPGAM , results in embryonic lethality or developmental defects ( depending on the timing of the dsRNA injection ) , suggesting its functional importance in nematodes 7 ., Selective inhibition of the parasite enzyme without harming the host should be possible , since mammals possess only a cofactor-dependent phosphoglycerate mutase ( dPGAM ) , which differs greatly from iPGAM in structure , mechanism of action , and kinetic profile 8 ., In particular , iPGAM is distinct from dPGAM in being catalytically active even in the absence of the cofactor 2 , 3-bisphosphoglycerate 9 ., Finally , bacterially expressed iPGAMs from B . malayi and C . elegans have been purified and characterized 7 , 8 and thus are readily available for high-throughput screening ( HTS ) ., iPGAMs druggability – another key criterion in drug target prioritization , as noted above – has not yet been subjected to thorough experimental scrutiny , as far as we know ., No potent inhibitors have been publicly reported to date , and the lack of a nematode iPGAM crystal structure further limits assessment of druggability ., At the level of amino acid sequences , the closest iPGAMs with published structures are those from Bacillus stearothermophilus and Bacillus anthracis 10 , 11 , the former being 41% identical and 60% similar to the B . malayi iPGAM ., The Bacillus structures show a monomeric protein with two domains: a phosphatase domain that removes the phosphate group from the glycerate substrate and a transferase domain that returns the phosphate to the substrate 10 ., The two domains may twist to form open and closed conformations , with the open conformation apparently corresponding to an absence of substrate 11 ., Thus , iPGAMs druggability could hinge partly on the fraction of time it spends in the open state , in which access to its active site is increased ., However , this active site may not be especially druggable ., The reactants ( 2-PG and 3-PG ) are highly polar , and the nine amino acids that interact with them in the Bacillus stearothermophilus iPGAM ( S62 , H123 , R153 , D154 , R185 , R191 , R261 , R264 , and K336 ) are all hydrophilic 10 , 12 ., Highly polar molecules ( e . g . , those with >5 hydrogen bond donors or >10 hydrogen bond acceptors 13 ) are generally not “drug-like” in that they are poorly permeable through lipid membranes in the absence of a specific cellular transporter ., The nine polar residues are all conserved in the B . malayi iPGAMiPGAM ., Moreover , the iPGAM active site appears too small to accommodate additional , more hydrophobic moieties ( Christophe Verlinde , personal communication ) ., Thus , the active site of the B . malayi iPGAM is unlikely to be druggable in the sense of being bound by a sufficiently hydrophobic molecule ., The above analysis does not preclude the possibility of allosteric inhibition , however ., In principle , allosteric inhibitors have the advantage of not needing to out-compete enzymes substrates 14 ., In practice , they have shown promise in studies of several infectious disease drug targets , including HIV reverse transcriptase 15 , HIV integrase 16 , hepatitis C virus NS5B polymerase 17 , and Bacillus anthracis edema factor 18 ., In the specific case of iPGAM , one can imagine an allosteric effector that forces the enzyme toward a closed conformation in the absence of substrate binding , thus preventing catalysis ., In the hope of finding B . malayi iPGAM inhibitors suitable for drug development , we performed high-throughput screens ( HTS ) of large compound collections at two sites: Genzyme Corporation ( Waltham , MA , USA ) and the National Center for Drug Screening ( NCDS; Shanghai , China ) ., In doing so , we took advantage of an innovative partnership ( previously discussed in this journal 19 ) between Novo Nordisk , the World Health Organization , and the NCDS ., This partnership has enabled NCDS to screen a compound library formerly owned by Novo Nordisk , as exemplified by the present study and previous work 20 ., iPGAM is dependent on divalent cations 8 and , like all enzymes , is responsive to changes in substrate concentrations ., We screened for iPGAM inhibitors under conditions of abundant Mg2+ and abundant substrate ( 3-PG ) , which approximate normal cellular conditions ., In particular , partial inhibition of glycolysis may lead to a buildup of 3-PG , and we hoped to discover inhibitors ( whether competitive or allosteric ) that would be effective even in the face of elevated substrate levels ., Likewise , we did not pre-incubate the enzyme with compounds before adding substrate because we wanted to identify compounds that could inhibit iPGAM under physiological conditions , i . e . , with substrate present ., As in previous work 7 , 8 , conversion of 3-PG to 2-PG ( i . e . , activity in the “forward”/glycolytic direction ) was monitored via coupling of iPGAM to the downstream glycolytic enzymes enolase , pyruvate kinase ( PyK ) , and lactate dehydrogenase ( LDH ) ., The primary assay readout was absorbance at 340 nm , reflecting NADH consumption by LDH , which in turn reflects upstream activity by PGAM , enolase , and PyK ., The two screening centers workflows were somewhat different ( Fig . 1 ) ., At Genzyme , compounds giving >40% inhibition against the C . elegans iPGAM were cherry-picked and re-tested; reconfirmed hits were then analyzed for chemical tractability 21; compounds deemed tractable were tested against the B . malayi iPGAM and human dPGAM; and compounds showing relatively potent and selective inhibition of B . malayi iPGAM ( IC50 <30 µM and lower than the IC50 vs . human dPGAM ) were advanced to independent confirmation in a luminescence-based assay and efficacy testing with C . elegans larvae ., At NCDS , the HTS was followed by dose-response assays of compounds that gave >20% inhibition in the initial screen ., The best NCDS hit was then tested for activity against the P . falciparum dPGAM ( since the human dPGAM was not readily available at the time ) , confirmed independently in the luminescence-based assay , and tested for efficacy against C . elegans larvae ., Primary efficacy testing was done against C . elegans rather than B . malayi for reasons of convenience ., Any compounds showing good potency versus C . elegans in vitro would have been advanced to a model of B . malayi infection in gerbils 22 ., Full-length , histidine-tagged iPGAM from C . elegans and B . malayi and full-length , histidine-tagged dPGAM from Homo sapiens and Plasmodium falciparum were expressed and purified as described previously 7 , 8 , 23 ., 3-PG , adenosine diphosphate ( ADP ) , nicotinamide adenine dinucleotide ( NADH ) , PyK ( from rabbit muscle , product P7768 ) , LDH ( from rabbit muscle , product L2500 ) and a PyK/LDH mixture ( from rabbit muscle; product P0294 ) were purchased from Sigma-Aldrich ( St . Louis , MO , USA ) ., Enolase ( from yeast; product #15515 ) was procured from Affymetrix ( Santa Clara , CA , USA ) and USB ( Cleveland , OH , USA ) ., Bovine serum albumin ( BSA ) was obtained from Amresco ( Solon , OH , USA ) and Sigma ., 384-well plates came from BD Biosciences ( Franklin Lakes , NJ , USA ) and PerkinElmer ( Waltham , MA , USA ) ., Kinase-Glo was from Promega ( Madison , WI , USA ) ., HTS assays were performed in 384-well plates ( final volume: 50 µL per well ) ., For the HTS at Genzyme , final assay concentrations were 30 mM Tris-HCl ( pH 7 ) ; 5 mM MgSO4; 20 mM KCl; 1 . 5 mM 3-PG ( 3–4 times the Km; see Results below ) ; 500 µM NADH; 3 mM ADP; 38 . 6 ng/mL C . elegans iPGAM; 2 . 5 Units/mL each of enolase , PyK , and LDH; 0 . 2% BSA and 10 µM of each compound tested ( final DMSO concentration: 1% ) ., For the HTS at NCDS , final concentrations were 30 mM Tris-HCl ( pH 7 . 9 ) ; 5 mM MgSO4; 20 mM KCl; 3 . 5 mM 3-PG; 450 µM NADH; 2 . 5 mM ADP; 300 ng/mL B . malayi iPGAM; 2 . 265 U/mL enolase; 3 . 15 U/mL PyK; 4 . 71 U/mL LDH; 0 . 4 mg/mL BSA; and 5 µM of each compound tested ( final DMSO concentration: 2% ) ., At both locations the assay readout was absorbance at 340 nm ., At Genzyme , absorbance data were taken as end-point readings on an Envision microplate reader ( PerkinElmer ) after 25 minutes of incubation at room temperature ( ∼20°C ) ., At NCDS , data were collected kinetically ( every 33 seconds ) on a SpectraMax M2 microplate reader ( Molecular Devices , Sunnyvale , CA , USA ) during 15 minutes of incubation at room temperature following a 2-minute delay ., HTS controls , representing the equivalent of enzyme inhibition , were wells with reduced iPGAM at Genzyme and wells with 50 µM or 200 µM tannic acid ( discovered in preliminary Genzyme studies to reduce iPGAM activity ) at NCDS ., To ensure robust , replicable results , hit compounds from the Genzyme and NCDS sites were shipped to the University of Washington for independent confirmation of inhibition of the B . malayi iPGAM using a separate batch of enzyme and a distinct assay readout ., Catalytic assays were performed as described above except that ATP production by PyK was measured as luminescence at 528 nm following addition of Kinase-Glo , a luciferase-based reagent ., A decrease in the slope of luminescence versus time was interpreted as inhibition of iPGAM ., Proprietary compound libraries housed at Genzyme and NCDS were screened ., All compounds were pre-solubilized in 100% DMSO prior to use ., The ∼220 , 000 compounds screened at Genzyme were taken from a library of ∼250 , 000 compounds from preferred vendors and internally synthesized compounds ., This library emphasizes ( A ) drug-like or lead-like compounds , as judged by the criteria of compliance with the Rule of 5 13 or Rule of 3 24 , compliance with Veber rules 25 , and having similarity to known drugs according to fingerprint analysis; ( B ) heterocycles ( ∼2000 unique ring assemblies ) ; and ( C ) natural product analogs ( >7000 ) ., Over 40 screens have been conducted using this library , and most yielded usable chemical hits in other assays ., The ∼160 , 000 compounds screened at NCDS were from a library of ∼325 , 000 synthetic compounds donated to NCDS by Novo Nordisk ., The structural diversity of this library covers heterocycles , lactams , sulfonates , sulfonamides , amines , secondary amides , and natural product-derived compounds ., Both libraries are intended to be relatively free of nonspecific aggregation-promoting compounds 26 , but aggregators appearing as hits in the primary screen are generally filtered out during the hit confirmation process ., L1 arrested larvae were resuspended in S Basal medium , with E . coli supplied as food and test compounds added from DMSO stocks to final concentrations of 0 to 75 µM ., Worms were incubated in liquid culture in multi-well plates ( 100 µL per well ) at 20°C for three days and then scored for growth defects/arrest ., At least 16 wells ( each containing 20 to 40 worms ) were scored for each concentration of each compound ., Only one generation of worms was followed ., The iPGAM from B . malayi has a GenBank ID of AY330617 and a UniProt ID of Q4VWF8 ., The iPGAM from C . elegans has a GenBank ID of AY594354 and a UniProt ID of G5EFZ1 ., Our attempts to find specific inhibitors of the B . malayi iPGAM met with extremely limited success ., The Genzyme screen tested ∼220 , 000 compounds at a concentration of 10 µM; it identified 110 confirmable hits against the C . elegans iPGAM , but only one of these compounds ( Fig . 2 ) passed all of the follow-up steps shown in Fig . 1 ., The yield of the ∼160 , 000-compound screen at NCDS was equally low ., 233 compounds ( 0 . 15% ) initially appeared to show ≥20% inhibition of the B . malayi iPGAM at a concentration of 5 µM ( Fig . 3 ) , but of these , only one compound consistently gave an IC50 <50 µM ( Fig . 4 ) ., In theory , such an exceedingly low frequency of confirmable hits could reflect problems with ( A ) the performance of the assay , ( B ) the makeup and/or handling of the compound libraries , and/or ( C ) the enzymes being screened ., We now address each of these possibilities ., Regarding assay performance ( A ) , the standard measure of HTS quality is the Z′-factor , which reflects the means and variability of inhibited and uninhibited samples ., Z′-factors range from 1 to below 0 , with values above 0 . 5 indicating a robust assay 27 ., Our mean Z′-factors were 0 . 65 for the Genzyme HTS and 0 . 51 for the NCDS HTS ., Visual inspection of the HTS data from Genzyme and NCDS ( Fig . 5 ) likewise showed large , clean separations between positive and negative controls ., Therefore our assay seemed adequate for detecting iPGAM inhibitors ., Regarding compound libraries ( B ) , there are two potential issues: the chemical “space” covered and compound stability ., While library builders seek to ensure broad coverage of potentially drug-like chemical space , a lack of hits could reflect limits in this coverage ., We also cannot be 100% certain that there were no problems with the storage , handling , or dispensing of the compounds tested ., However , these same Genzyme/NCDS libraries have been used to identify potent hit compounds in screens against other target proteins ( e . g . , 20 , 28 ) ., Therefore it seems unlikely that the low hit rate was caused by systematic degradation or incorrect dispensing of compounds ., Finally , regarding the enzymes screened ( C ) , enzymes were expressed in E . coli from plasmids originally generated by New England Biolabs ., SDS-PAGE analysis gave the expected molecular masses of ∼57 kDa ( B . malayi ) and ∼59 kDa ( C . elegans ) , as previously reported 7 ., Newly determined Kms for 3-PG of 0 . 37 mM ( B . malayi iPGAM ) and 0 . 38 mM ( C . elegans iPGAM ) were consistent with previously reported values 8 of 0 . 35 mM and 0 . 51 mM , respectively ., Likewise , specific activities of the newly purified enzymes were similar to those reported previously ( data not shown ) ., Therefore , there is no indication that the enzyme stocks used in the HTS were misfolded or denatured , which would have hampered the search for inhibitors of the normal well-folded enzymes ., However , it is notable that the Genzyme HTS used the C . elegans iPGAM rather than the B . malayi iPGAM , and that only a subset of the hits against the C . elegans iPGAM were subsequently tested against the B . malayi iPGAM ., Given the strong amino-acid similarity ( 70% identity , 82% similarity ) of the two enzymes 7 , we would expect inhibitor profiles for these enzymes to be similar as well ., Still , inhibitors specific for the B . malayi iPGAM might be missed in screening with the C . elegans iPGAM ., The NCDS HTS directly addressed this possibility; it showed that using the B . malayi iPGAM in the primary screen did not appreciably increase the rate of hits against this enzyme ., Given these considerations , we believe that the low hit rates do not reflect any major experimental limitations , but instead may reflect iPGAMs poor druggability ., There currently is no crystal structure for either the B . malayi or C . elegans enzymes; however , sequence homology 7 suggests that both of these are very similar to that of Bacillus stearothermophilus , for which the crystal structure has been resolved 12 ., That structure suggests a peptide “gate” over the active site ( confirmed in subsequent studies 29 ) which may limit accessibility to potential inhibitors ., Similar problems have been encountered for other enzyme targets ., The crystal structure of cytosolic phospholipase A2 revealed that it contains an α-helical “lid” that can fold to cover the active site 30 ., This feature has made it very difficult to obtain broad structural classes of inhibitors ( 31 and John Leonard , personal communication ) ., The compounds shown in Fig . 2 may be useful as chemical probes in future studies of iPGAM , and thus represent an important outcome of our study ., These compounds potential application in drug development depends on several criteria , such as ( A ) potency against the target enzyme and target parasite , ( B ) chemical properties related to druglikeness and medicinal chemistry potential , ( C ) specificity of inhibition , and ( D ) activity of related compounds ., Regarding potency ( A ) , neither hit compound had an IC50 vs . the B . malayi iPGAM of <10 µM , nor was either efficacious against C . elegans larvae at 25 µM ., It has been proposed that , for anti-helminth hits to merit development into possible lead compounds , they should inhibit helminth motility by 50 to 100% at concentrations below 2 to 10 µg/mL 32 , which would be 9 to 45 µM for a compound whose molecular weight is 223 ( the average for the two compounds shown in Fig . 2 ) ., In this concentration range , our hits showed little or no activity against live worms ., While factors like poor drug solubility , limited uptake into the worm gut , and limited transport across the cuticle could all affect activity in this assay , these compounds are not especially appealing as starting points for drug development ., Regarding chemical properties ( B ) , both hits are reasonably drug-like and amenable to chemical synthesis and modification; e . g . , they do not have limitations such as a high hydrophilicity or numerous chiral centers ., Regarding off-target effects ( C ) , PubChem BioAssay 33 shows that compound Genz-2 ( PubChem CID 3614032 ) does not inhibit the other 9 targets against which it has been tested , while no bioassay data are available for NCDS-1 ( PubChem 606970 ) ., Finally , of the five NCDS-screened compounds based on a scaffold of 1 , 10-phenanthroline , only the hit compound itself ( 5-amino-1 , 10-phenanthroline ) gave any noticeable inhibition of the B . malayi iPGAM ( D ) ., This suggests that most changes to this compounds structure would eliminate its activity against iPGAM , and therefore that improvement of the hit compounds potency through medicinal chemistry would be difficult ., Thus , large-scale efforts at two different screening centers collectively failed to identify any high-priority compounds for drug development studies ., In the absence of published empirical evidence that nematode iPGAMs can be potently and specifically modulated by drug-like molecules , advancing them as drug targets appears highly challenging and risky ., We would also advise caution in the pursuit of non-nematode iPGAMs like the Trypanosoma brucei iPGAM 34 , 35 as drug targets , since their druggability remains uncertain ., Our finding of poor nematode iPGAM druggability contrasts with a recent modeling study of the Wuchereria bancrofti iPGAM 36 , which presents drug-like molecules proposed to be likely inhibitors ., The authors developed a 3-D model of the W . bancrofti iPGAM , using the B . stearothermophilus iPGAM 12 as a template ., They found that 63 residues were conserved among iPGAMs and that 53 residues contribute to binding pockets as defined by Q-SiteFinder 37; the 19 residues belonging to both sets were termed the “common amino acid residues” ( WB-iPGM19cr ) ., When a virtual library of 2 , 344 small molecules were presented to the W . bancrofti iPGAM model in docking simulations , 65 were predicted to interact with at least one of the WB-iPGM19cr residues , and 8 of these 65 ( each linked to exactly one WB-iPGM19cr residue ) are considered to have good ADME/T properties and are “strongly recommended for further clinical trials . ”, We applaud this interest in the W . bancrofti iPGAM but note that none of the predicted inhibitors were tested experimentally ., Having invested considerable resources in screening nematode iPGAMs , only to find that they do not appear druggable , we must ask whether this disappointing outcome could have been predicted ., The crystal structure of the B . stearothermophilus iPGAM does indicate a “gate” region peptide over the active site that could prevent access by potential inhibitors ( see above 12 , 29 ) ., More recent studies on the crystalized structure of Tryanosoma brucei iPGAM also indicate that when crystalized in the presence of substrate , the substrate is buried and is not solvent-accessible , again suggesting a gate-like fold over the active site 35 that may increase the difficulty of finding inhibitors ., We also considered the possibility that allosteric inhibitors might be discovered as part of the screening process ., A priori prediction of allosteric effects remains challenging; as summarized in one review , “In most cases , the novel allosteric binding sites could not have been predicted from the unliganded structure” 38 ., When studying a protein without a solved crystal structure , such as the B . malayi iPGAM , the challenge increases further ., In conclusion , we suggest that target-based drug development suffers from a frustrating paradox: proteins are generally unsuitable for resource-intensive HTS unless they are considered druggable , yet druggability is often difficult to estimate in the absence of HTS data ., Although improved druggability predictions 39 may eventually offer a way out of this paradox , a moderate level of risk currently appears unavoidable in the screening of many novel protein targets .

Introduction, Methods, Results/Discussion

Cofactor-independent phosphoglycerate mutase ( iPGAM ) is essential for the growth of C . elegans but is absent from humans , suggesting its potential as a drug target in parasitic nematodes such as Brugia malayi , a cause of lymphatic filariasis ( LF ) ., iPGAMs active site is small and hydrophilic , implying that it may not be druggable , but another binding site might permit allosteric inhibition ., As a comprehensive assessment of iPGAMs druggability , high-throughput screening ( HTS ) was conducted at two different locations: ∼220 , 000 compounds were tested against the C . elegans iPGAM by Genzyme Corporation , and ∼160 , 000 compounds were screened against the B . malayi iPGAM at the National Center for Drug Screening in Shanghai ., iPGAMs catalytic activity was coupled to downstream glycolytic enzymes , resulting in NADH consumption , as monitored by a decline in visible-light absorbance at 340 nm ., This assay performed well in both screens ( Z′-factor >0 . 50 ) and identified two novel inhibitors that may be useful as chemical probes ., However , these compounds have very modest potency against the B . malayi iPGAM ( IC50 >10 µM ) and represent isolated singleton hits rather than members of a common scaffold ., Thus , despite the other appealing properties of the nematode iPGAMs , their low druggability makes them challenging to pursue as drug targets ., This study illustrates a “druggability paradox” of target-based drug discovery: proteins are generally unsuitable for resource-intensive HTS unless they are considered druggable , yet druggability is often difficult to predict in the absence of HTS data .

Parasitic worms like Brugia malayi cause widespread lymphatic filariasis ( LF ) in southeast Asia and sub-Saharan Africa ., The adult worms causing most of the symptoms of LF are difficult to treat with existing drugs ., As a possible step toward new LF drugs , we searched for inhibitors of the B . malayi cofactor-independent phosphoglycerate mutase ( iPGAM ) , an enzyme thought to be critical to survival and development of this parasite ., Despite testing over 100 , 000 compounds at each of two screening centers , we found only two compounds that consistently inhibited the B . malayi enzyme more strongly than the cofactor-dependent enzyme found in humans ., These compounds have limited potency and are not especially great starting points for drug development ., The 3-dimensional structure of iPGAM suggests that the active site is difficult to access from the surrounding solvent , which may partly explain our very low yield of inhibitors ., We conclude that iPGAM may not be an ideal drug target in B . malayi or related organisms because it is difficult to inhibit with druglike compounds .

medicine, biochemistry, infectious diseases, drug research and development, drugs and devices, enzymes, neglected tropical diseases, biology, lymphatic filariasis, drug discovery

journal.pgen.1002341

Association of NCF2, IKZF1, IRF8, IFIH1, and TYK2 with Systemic Lupus Erythematosus

Systemic lupus erythematosus ( SLE ) is a relapsing-remitting complex trait which most commonly affects women of child-bearing age , with a ratio of 9∶1 in female to males ., The disease prevalence varies with ethnicity , being more prevalent in non-European populations ( approximately 1∶500 in populations with African ancestry and 1∶2500 in Northern Europeans ) 1 ., The condition is characterised by the production of a diverse range of auto-antibodies against serological , intra-cellular , nucleic acid and cell surface antigens 2 ., The wide-ranging clinical phenotypes include skin rash , neuropsychiatric and musculosketal symptoms and lupus nephritis , which may be partially mediated by the extensive deposition of immune complexes ., Today , thanks to improved treatments , the 10-year survival rate after diagnosis has increased to 90% , with lower survival rates being related to disease severity or complications from treatment 3 ., Increased understanding of the underlying genetic basis for lupus is of key importance in improving the prognosis for lupus patients ., Until recently , the genetic basis of lupus remained largely undetermined , with only about ∼8% of the genetic contribution known 4 ., However , within the last three years , tremendous progress has been made in defining novel loci , through three moderate-sized genome-wide association studies in European American cohorts and a replication study in a US-Swedish cohort 5–7 ., The loci previously identified for SLE include genes involved in the innate immune response ( eg . IRF5 ) , T and B cell signalling ( eg . STAT4 , TNFSF4 and BLK ) , autophagy/apoptosis ( eg . ATG5 ) , ubiquitinylation ( UBE2L3 , TNAIP3 , TNIP1 ) and phagocytosis ( ITGAM , FCGR3A and FCGR3B ) ., All of these pathways are of potential importance in lupus pathogenesis 8–10 ., To date , a total of 1729 independent SLE cases have been subjected to genome-wide association genotyping using three genotyping platforms: Illumina 317 K BeadChip 5 , Illumina 550 K BeadChip 6 and Affymetrix 500 K array 7 ., There is currently no published meta-analysis of these datasets ., The aim of the current work was to perform a replication study using our UK SLE cohort on loci that showed some evidence for association in previous studies in order to extend the list of confirmed susceptibility genes for lupus ., To identify additional susceptibility loci for SLE , we first identified the independent genetic variants that showed moderate risk ( 5×10−3<P>5×10−8 ) in a combined US-Swedish dataset comprising 3273 SLE cases and 12188 controls 4 ., We then genotyped 27 independent SNPs in a replication cohort of 905 UK SLE cases and 5551 UK control samples ( Table 1 ) , that included both British 1958 Birth Cohort samples and additional controls from the WTCCC2 project ., For the 27 genotyped SNPs , 10 variants which had not been genotyped by the WTCCC2 project , were imputed using IMPUTE2 11 ., This imputation was performed using CEPH HapMap samples as the phased reference sequence and the boundary of the surrounding haplotype blocks used to demarcate the imputation interval ., The subsequent association analysis excluded two of these ten imputed SNPs because they had less than 95% certainty for the imputation ( Table S2 ) ., In the US/SWE dataset , imputation of selected SNPs not genotyped previously 4 was performed using IMPUTE1 for HapMap ., Phase II CEU sample haplotypes were used as reference with subsequent association analysis performed using SNPTEST and a genomic control factor ( lambda-GC ) values of: 1 . 05 ( US dataset ) and 1 . 10 ( SWE dataset ) after correction for population stratification ., In the UK replication sample by performing allelic association analysis using PLINK for the 23 SNPs passing QC ( Tables S2 and S3 ) , we demonstrated moderate association ( P≤0 . 05 ) for twelve variants - with a lambda-GC of 1 . 01 following ancestry correction ( see Table 2 and Table 3 ) ., Under the null hypothesis , only 1 of the 23 loci would be expected to have P≤0 . 05 ., The observed enrichment of associated SLE genes in the UK dataset suggested that many of these loci were likely to be true-positive associations ., We confirmed the similarity of odds-ratios ( Het P value ) and direction of the effect between the UK and US-SWE datasets ( Table S4 ) and then performed a meta-analysis using Fishers combined P-value ( see Materials and Methods ) ., This meta-analysis revealed five novel associated loci with P<5×10−8 ( Table 2 ) : NCF2 ( neutrophil cytosolic factor 2 ) ( rs10911363 , Pcomb\u200a=\u200a2 . 87×10−11 , ORcomb\u200a=\u200a1 . 19 ) ; IKZF1 ( Ikaros family zinc-finger 1 ) ( rs2366293 , Pcomb\u200a=\u200a2 . 33×10−9 , ORcomb\u200a=\u200a1 . 24 ) ; IRF8 ( interferon regulatory factor 8 ) ( rs2280381 , Pcomb\u200a=\u200a1 . 24×10−8 , ORcomb\u200a=\u200a1 . 16 ) ; IFIH1 ( interferon-induced helicase C domain-containing protein 1 ) ( rs1990760 , Pcomb\u200a=\u200a1 . 63×10−8 , ORcomb\u200a=\u200a1 . 15 ) and TYK2 ( tyrosine kinase 2 ) ( rs280519 , Pcomb\u200a=\u200a3 . 88×10−8 , ORcomb\u200a=\u200a1 . 17 ) ( Table 1 ) ., The strength of these associations was similar to those found from a weighted meta-analysis , using the METAL programme ( Table S4 ) ., A case-only analysis using PLINK in the combined UK/US/SWE dataset revealed no non-additive interactions between the five newly associated variants ( P>0 . 05 ) ., These new SLE loci are discussed in more detail below and with additional information in Text S1 ., Three of the SNPs tested were for loci that had shown genome-wide levels of significance in other SLE GWAS studies ( Table S5 ) ., In the UK cohort we found further support for the association at JAZF1 ( rs849142 PUK\u200a=\u200a0 . 0243 , ORUK\u200a=\u200a1 . 13 ) and identified a third associated variant in the first intron of TNIP1 ( rs6889239 PUK\u200a=\u200a9 . 06×10−6 , ORUK\u200a=\u200a1 . 30 ) , which is in strong LD ( r2\u200a=\u200a0 . 895 ) with both the previous report in Europeans 4 and in perfect LD with a third SNP ( rs10036748 ) , first reported in a Chinese GWAS 12 ., All three variants in TNIP1 are located within a 661 bp region of intron 1 ., We did not replicate the previous association with IL10 ( rs3024505 , PUK\u200a=\u200a0 . 209 ORUK\u200a=\u200a1 . 09 ) ( Table S5 ) ., These analyses increased the evidence of association for a number of additional loci that had shown borderline significance in the original US/SWE GWAS ( Table 3 ) , including CFB , C12ORF30 , SH2B3 , and IL12B ., Genotyping of additional samples will be required to determine if the association signals shown in Table 3 represent confirmed genetic loci for SLE ., The work presented here confirms five new susceptibility loci for SLE at the level of genome-wide significance ( P<5×10−8 ) ., Each of the associated variants lie within , or close to , the coding sequence for genes with known roles in immune regulation: NCF2 , IKZF1 , IRF8 , IFIH1 and TYK2 ., Interestingly , each of these genes has been implicated in interferon signalling ., While the interferons have classically been defined as anti-viral cytokines , recent studies have suggested an important role for interferon in the pathophysiology of SLE 13 ., While most evidence points to the role of type I interferon in SLE 14 there is substantial data suggesting that type II interferon ( IFNγ ) is also involved in SLE pathogenesis 15 ., NCF2 ( neutrophil cytosolic factor 2 ) ( 1q25 ) , is induced by IFNγ and specifically expressed in a number of immune-cell types , including B-cells ., Our data suggest that the NCF2 association is independent from the previously reported signal in the neighbouring locus NMNAT2 , 5 because we found no evidence of strong LD between the genotyped SNP within NMNAT2 ( rs2022013 ) and that in NCF2 ( rs10911363 ) ( r2\u200a=\u200a0 . 136 ) ., Logistic regression in the UK replication cohort confirmed that NMNAT2 did not contribute to the association at NCF2 ( P\u200a=\u200a0 . 777 ) ., NCF2 , as a cytosolic subunit of NADPH-oxidase , may have a role in the increased production of the free radicals characterising B-cell activation 16 ( Figure 1 ) which increases auto-antibody levels and may suggest a mechanism for the involvement of NCF2 as a susceptibility gene for SLE ., There are allele-specific significant expression differences for rs10911363 , following a recessive model of basal expression for the risk T allele of rs10911363 in CEPH individuals but not in YRI and ASN ( CHB+JPT ) HapMap cohorts ( PCEPH\u200a=\u200a0 . 03 ) ( Figure 2A ) ., There is also a significant difference in gene expression for a variant ( rs3845466 ) located 2 kb away from rs10911363 in intron 2 of NCF2 ( Figure S2A ) , using lymphoblastoid cell lines ( LCLs ) from umbilical cords of 75 individuals which were taken from the GENEVAR collection ( P\u200a=\u200a0 . 0228 ) ., The population-specific nature of this correlation could be because of local differences in the pattern of LD within NCF2 between the CEU , YRI and ASN ( CHB+JPT ) HapMap cohorts ., These population specific differences in LD may be between the genotyped SNP and an unknown causal allele ( s ) responsible for an expression difference seen in multiple ethnic backgrounds or between the genotyped marker and an unknown causal allele ( s ) exhibiting population-specific differences in gene expression itself ., However , it will be necessary to confirm these findings in primary cells and tissues , because the EBV-transformed B cells model system may not entirely reflect the physiological conditions in peripheral B cells ., Indeed a recent report showed that there may be systematic changes in gene expression within EBV-transformed B cells 17 ., Nevertheless , with this caveat in mind , and taking each locus on a case-by-case basis , the model-based approach can provide important insights into measurement of transcript levels in ex vivo cells ., For example , the increases in transcript levels that we initially observed in EBV-LCLs for OX40L , were also confirmed in peripheral blood B cells 18 ., IKZF1 ( Ikaros family zinc-finger 1 ) ( 17p14 . 3 ) is a transcription factor essential for dendritic cell and lymphocyte development ., The association with rs2366293 is supported by a report of a second associated variant , rs921916 ( Pcomb\u200a=\u200a2 . 0×10−6 ) 4 , found 860 bp away from rs2362293 , which is in strong LD with rs2366293 ( r2\u200a=\u200a−0 . 746 , D′\u200a=\u200a0 . 925 ) ( Figure S2B ) ., A third SNP , rs4917014 , located ∼200 kb upstream of IKZF1 , showed association with SLE in a Chinese GWAS ( PGWAS\u200a=\u200a2 . 93×10−06 ) , but it was a separate signal from the European SNPs ( r2<0 . 0002 ) 9 , 12 ., IKZF1 has a role in the production of IFNγ , by blocking the production of the Th1 master-regulator T-bet ( Figure 1 ) ., The shifted Th1/Th2 equilibrium ( in favour of Th1 cells ) increases the levels of IFNγ directly 19 rather than indirectly as a result of cross-talk between the type-I and type-II IFN signalling pathways eg ) via type-I interferon mediated activation of STAT1 homodimers , which are the primary means of signalling from IFNγ 20 and have recently been shown to be associated with SLE in a Swedish cohort 21 ., The transcription factor IRF8 ( interferon regulatory factor 8 ) ( 16q24 . 1 ) , shows immune-cell restricted expression ., rs2280381 is found 64 kb downstream of IRF8 , and is in LD with the coding region ( Figure S2C ) , but independent from a susceptibility allele for multiple sclerosis ( rs17445836 ) , 1 kb away 22 ., The lupus variant influences IRF8 gene expression , since LCLs from three HapMap cohorts , showed a significant increase in IRF8 transcript levels in homozygotes for the risk allele ( TT ) compared to homozygotes for the non-risk allele ( CC ) ( P\u200a=\u200a0 . 045 ) ( Figure 2A ) ., IRF8 also has a key role in regulating the differentiation of myeloid and B-cells and in mice , IRF8 restricts myeloid cell differentiation but promotes B-cell differentiation 23 ( Figure 1 ) ., IFIH1 ( interferon-induced helicase C domain-containing protein 1 ) ( 2q24 . 3 ) is an ubiquitiously expressed , cytoplasmic sensor of dsRNA ., The SLE risk allele for rs1990760 ( Table 1 ) is identical to that previously reported in two organ-specific autoimmune diseases: T1D 24 and Graves Disease 25 ., Regression analysis using publically available genotype data from HapMap and expression data from GEO dataset GSE12526 revealed that individuals who were homozygous for the common risk T allele of rs1990760 had significantly higher IFIH1 transcript levels compared to individuals who were homozygous for the non-risk allele ( P\u200a=\u200a0 . 8 . 19×10−5 ) ( Figure S3B ) ., Furthermore , a recent paper showed that the presence of the risk T allele of rs1990760 was correlated with increased levels of IFN-induced gene expression , in lupus patients who were positive for anti-dsDNA antibodies 26 ., Another report demonstrated that IFIHI was rapidly up-regulated by type-I IFNs ( Figure 1 ) , and that IFIH1 signalled downstream through NF-κB , to further increase IFN-α production 27 ., TYK2 ( tyrosine kinase 2 ) ( 19p13 . 2 ) phosphorylates the receptor subunits of cytokine receptors , including type-I IFN receptors which are found on all nucleated cells , leading to increased production of type I interferon responsive genes ( Figure 1 ) ., The significant association in intron 11 TYK2 for rs280519 in our UK cohort ( P\u200a=\u200a5 . 24×10−4 ) crossed the threshold for genome-wide significance when combined with the US/Swedish cohort ., The association for rs280519 increases the genetic evidence for the involvement of TYK2 reported in a smaller UK family-based SLE cohort 28 ., There was an earlier report , using a Swedish/Finnish population , of association in TYK2 ., This Swedish/Finnish study showed association for a missense mutation in exon 8 ( rs2304256 ) ( Pcomb\u200a=\u200a5 . 60×10−5 , PSwe\u200a=\u200a9 . 60×10−5 ) 29 ., The Swedish individuals used in the earlier analysis are a subset of the Swedish individuals analysed for this current manuscript and rs2304526 is in moderate LD with the TYK2 SNP that we typed in this current study - rs280519 ( r2CEPH-HapMap\u200a=\u200a0 . 373 ) ., The association for rs2304256 was replicated in a second moderate sized European study 30 , but not in the GWAS from the SLEGEN consortium 5 ., In preliminary analysis in UK cases and controls , there are data to support the fact that rs280519 is enriched in SLE cases ( n\u200a=\u200a345 ) with renal disease compared to healthy controls ( n\u200a=\u200a5551 ) ( P\u200a=\u200a0 . 033 ) ., There were variants in several loci for which we have found evidence of association ( P<0 . 05 ) in our UK cohort , but which did not reach genome-wide significance in the combined analysis ., One of these variants was rs17696736 , located in intron 15 of C12ORF30 ( MDM20 ) ., This protein is a subunit of N-acetyltransferase complex B ( NatB ) , and may promote apoptosis by reducing cell cycle progression 31 ., In the joint cohort , rs17696736 was in LD ( r2\u200a=\u200a0 . 625 ) with a second variant on chromosome 12q24 , a missense W262R allele ( rs3184504 ) in the lymphocyte adaptor protein SH2B3 ., SH2B3 facilitates T-cell activation by mediating the interaction between the T-cell receptor and T cell signalling molecules 32 ., Both MDM20 and SH2B3 are also associated with T1D 33 , and SH2B3 is additionally associated with celiac disease 34 and both myocardial infarction and asthma 35 ., The associated variant within IL12B , rs3212227 , is located in the 3′ UTR region , and the SLE risk allele is the same as previously reported for psoriasis 36 ., IL12B encodes for the larger subunit ( p40 ) of two cytokines , IL12 and IL23 , and thereby contributes to both Th1 37 and Th17 38 immune responses ., In summary , we have identified five new genes contributing to SLE risk: NCF2 , IKZF1 , IRF8 , IFIH1 and TYK2 ., Dense fine-mapping and/or genomic re-sequencing of each locus will be required to reveal the functional alleles for each gene with respect to immune dysregulation in lupus ., Taken together , these findings further support an important role of interferon pathway dysregulation in lupus pathogenesis ., The ethical approval for the study was obtained from the London Multi-Centre Research Ethics Committee ( London MREC ) ., All of the 905 UK SLE cases conformed to the ACR criteria for SLE 39 with a diagnosis of SLE being established by telephone interview , health questionnaire and details from clinical notes ., Written consent was obtained from all participants ., Genomic DNA from the UK samples was isolated from anti-coagulated whole blood by a standard phenol-chloroform extraction ., Each of the 27 SNPs were genotyped on a custom Illumina chip , using the BeadXpress platform at the Oklahoma Medical Research Foundation ( OMRF ) , Oklahoma ., The panel of ancestry informative markers was typed independently on an Illumina platform at Gen-Probe , Livingstone ., Power calculations were performed in the UK case-control dataset for each of the markers tested , using the algorithm described by Purcell et al 40 ., Taking into account varying minor allele frequencies for the risk alleles and the differences in effect size ( OR ) , and by employing a population prevalence of 0 . 002 and D′ of 1 , with an type I error rates , alpha\u200a=\u200a0 . 05 , each of the SNPs showing novel genome-wide significance in the meta-analysis showed a power of >48% ( 2 ) to detect an association in our cohort ., Markers were excluded from the analysis if they showed a genotyping success rate of less than 95% or had a Hardy-Weinberg P value in the B58BCC control samples of less than P\u200a=\u200a0 . 001 ., A total of 21 cases were removed from the final analysis due to low percentage genotyping ( <95% ) ., All samples were filtered for cryptic relatedness and duplication using an identity by state test in PLINK ( PI_HAT score >0 . 1 ) ., The full list of genotyped variants and the results of the QC analysis are shown in ( Table S3 ) ., A total of 35887 markers , distributed across each autosome , were selected for ancestry correction in the UK case-control cohort , these markers had all been typed as part of the HapMap project and on the WTCCC2 samples ., The 35887 SNPs were chosen from a set of Illumina 317 K markers pruned for LD ( r2<0 . 25 ) after removing regions of known extended LD , including the extended MHC and the region covering the inverted repeat on chromosome 8 ( pers commun . David Morris , Kings College and Kim Taylor , UCSF ) ., This list of AIMs is available directly from the corresponding author , Professor Timothy Vyse ., The EIGENSTRAT PCA analysis was performed on the UK cases and also the control samples , both from the genotyped B58BCC and the WTCCC2 out-of-study controls ., The eleven populations from HapMap3 were used as external references ., Each SNP included in the PCA analysis showed >95% genotyping in the each dataset ., Following EIGENSTRAT analysis , a graph was plotted of PC1 against PC2 for all the cases and controls in the UK study cohort ( Figure S1 ) ., Individuals were only retained for association analysis if the values for their first two principal components fell within 6 SD of the mean for the CEPH HapMap samples ., The genomic inflation factor ( lambda-GC ) for each population was calculated using PLINK ., All sample genotype and phenotype data was managed by , and analysis files generated with BC/SNPmax and BC/CLIN software ( Biocomputing Platforms Ltd , Finland ) ., The imputation intervals for each imputed variant , defined as the bounds of the haplotype blocks , calculated using the Gabriel algorithm in Haploview , ( for details of the intervals see Table S2 ) ., For SNPs which were not genotyped as part of the WTCCC2 project , we performed imputation using a method described by Marchini et al 11 to generate the missing genotypes for case-control association analysis ., Each un-typed variant from our list of tested SNPs , was imputed in the WTCCC2 samples , using HAPMAP as the phased reference sequence ., The LD pattern around each un-typed variant was examined using the CEPH cohort from HapMap ., The boundaries of the haplotype blocks were determined using the default settings for the Gabriel et al algorithm in Haploview ., For each imputed variant , these haplotype boundaries were used to define the boundaries of the imputation interval ( Table S2 ) ., Only SNPs with greater than a 95% certainty in imputation , assessed using the quality score from the IMPUTE2 output file , were used for subsequent analysis ., Allelic association testing , using UK SLE cases with either genotyped control samples or imputed genotypes , was carried out using PLINK ( http://pngu . mgh . harvard . edu/~purcell/plink/ ) ., Prior to performing the meta-analysis , the heterogeneity of odds ratios was tested using METAL and the Cochran-Mantel-Haenszel test ( Table S4 ) ., SNPs with P value<0 . 001 between the two studies were discarded ., Combined analysis of P values generated in the UK samples with those from the US/SWE cohort in published data 4 was conducted using Fishers combined P value and with a meta-analysis using the programme METAL , which weighted the effect size , based on the inverse of the standard error ., To determine whether there was any allele-specific effect on the level of gene expression , we used publically available genotype data on unrelated EBV-transformed B cells ( CEU , YRI and CHB/JPT individuals which were part of the HapMap project ) and expression data from the same individuals ( GSE12526 , GEO database ) 41 ., For each locus , which reached genome-wide significance by meta-analysis , we categorised the expression data based on the SNP genotype for the respective associated variant ( homozygote risk allele , heterozygote and homozygous non-risk allele ) ., The significance of the correlation between genotype and expression level was then calculated using logistic regression analysis in SNPTEST , using gender as a covariate ., Interactions between the five SNPs reaching genome-wide significance following meta-analysis , were assessed using the epistatic option in PLINK ., To maximize the power of this test , we restricted our analysis to the SLE affected individuals from the combined US/SWE/UK cohort .

Introduction, Results, Discussion, Materials and Methods

Systemic lupus erythematosus ( SLE ) is a complex trait characterised by the production of a range of auto-antibodies and a diverse set of clinical phenotypes ., Currently , ∼8% of the genetic contribution to SLE in Europeans is known , following publication of several moderate-sized genome-wide ( GW ) association studies , which identified loci with a strong effect ( OR>1 . 3 ) ., In order to identify additional genes contributing to SLE susceptibility , we conducted a replication study in a UK dataset ( 870 cases , 5 , 551 controls ) of 23 variants that showed moderate-risk for lupus in previous studies ., Association analysis in the UK dataset and subsequent meta-analysis with the published data identified five SLE susceptibility genes reaching genome-wide levels of significance ( Pcomb<5×10−8 ) : NCF2 ( Pcomb\u200a=\u200a2 . 87×10−11 ) , IKZF1 ( Pcomb\u200a=\u200a2 . 33×10−9 ) , IRF8 ( Pcomb\u200a=\u200a1 . 24×10−8 ) , IFIH1 ( Pcomb\u200a=\u200a1 . 63×10−8 ) , and TYK2 ( Pcomb\u200a=\u200a3 . 88×10−8 ) ., Each of the five new loci identified here can be mapped into interferon signalling pathways , which are known to play a key role in the pathogenesis of SLE ., These results increase the number of established susceptibility genes for lupus to ∼30 and validate the importance of using large datasets to confirm associations of loci which moderately increase the risk for disease .

Genome-wide association studies have revolutionised our ability to identify common susceptibility alleles for systemic lupus erythematosus ( SLE ) ., In complex diseases such as SLE , where many different genes make a modest contribution to disease susceptibility , it is necessary to perform large-scale association studies to combine results from several datasets , to have sufficient power to identify highly significant novel loci ( P<5×10−8 ) ., Using a large SLE collection of 870 UK SLE cases and 5 , 551 UK unaffected individuals , we firstly replicated ten moderate-risk alleles ( P<0 . 05 ) from a US–Swedish study of 3 , 273 SLE cases and 12 , 188 healthy controls ., Combining our results with the US-Swedish data identified five new loci , which crossed the level for genome-wide significance: NCF2 ( neutrophil cytosolic factor 2 ) , IKZF1 ( Ikaros family zinc-finger 1 ) , IRF8 ( interferon regulatory factor 8 ) , IFIH1 ( interferon-induced helicase C domain-containing protein 1 ) , and TYK2 ( tyrosine kinase 2 ) ., Each of these five genes regulates a different aspect of the immune response and contributes to the production of type-I and type-II interferons ., Although further studies will be required to identify the causal alleles within these loci , the confirmation of five new susceptibility genes for lupus makes a significant step forward in our understanding of the genetic contribution to SLE .

systemic lupus erythematosus, medicine, rheumatology, genetics of the immune system, genetic association studies, genetics, immunology, biology, human genetics, genetics of disease, genetics and genomics

journal.pcbi.1005542

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements

Stochastic neural dynamics mediate between the underlying biophysical and physiological properties of a neural system and its computational and cognitive properties ( e . g . 1–4 ) ., Hence , from a computational perspective , we are often interested in recovering the neural network dynamics of a given brain region or neural system from experimental measurements ., Yet , experimentally , we commonly have access only to noisy recordings from a relatively small proportion of neurons ( compared to the size of the brain area of interest ) , or to lumped surface signals like local field potentials or the EEG ., Inferring from these the computationally relevant dynamics is therefore not trivial , especially since both the recorded signals ( e . g . , spike sorting errors; 5 ) as well as the neural system dynamics itself ( e . g . , stochastic synaptic release; 6 ) come with a good deal of noise ., The stochastic nature of neural dynamics has , in fact , been deemed crucial for perceptual inference and decision making 7–9 , and potentially helps to avoid local minima in task learning or problem solving 10 ., Speaking in statistical terms , model-free techniques which combine delay embedding methods with nonlinear basis expansions and kernel techniques have been one approach to the problem 11; 12 ., These techniques provide informative lower-dimensional visualizations of population trajectories and ( local ) approximations to the neural flow field , but they may highlight only certain , salient aspects of the dynamics ( but see 13 ) and , in any case , do not directly return distribution generating equations or underlying computations ., Alternatively , state space models , a statistical framework particularly popular in engineering and ecology ( e . g . 14 ) , have been adapted to extract lower-dimensional , probabilistic neural trajectory flows from higher-dimensional recordings 15–25 ., State space models link a process model of the unobserved ( latent ) underlying dynamics to the experimentally observed time series via observation equations , and differentiate between stochasticity in the process and observation noise ( e . g . 26 ) ., So far , with few exceptions ( e . g . 23; 27 ) , these models assumed linear latent dynamics , however ., Although this may often be sufficient to yield lower-dimensional smoothed trajectories , it implies that the recovered dynamical model may be less apt for capturing highly nonlinear dynamical phenomena in the observations , and will by itself not be powerful enough to reproduce a range of important dynamical and computational processes in the nervous system , among them multi-stability which has been proposed to underlie neural activity during working memory 28–32 , limit cycles ( stable oscillations ) , or chaos ( e . g . 33 ) ., Here we derive a new state space algorithm based on piecewise-linear ( PL ) recurrent neural networks ( RNN ) ., It has been shown that RNNs with nonlinear activation functions can , in principle , approximate any dynamical systems trajectory or , in fact , dynamical system itself ( given some general conditions; 34–36 ) ., Thus , in theory , they are powerful enough to recover whatever dynamical system is underlying the experimentally observed time series ., Piecewise linear activation functions , in particular , are by now the most popular choice in deep learning algorithms 37–39 , and considerably simplify some of the derivations within the state space framework ( as shown later ) ., They may also be more apt for producing working memory-type activity with longer delays if for some units the transfer function happens to coincide with the bisectrix ( cf . 40 ) , and ease the analysis of fixed points and stability ., We then apply this newly derived algorithm to multiple single-unit recordings from the rat prefrontal cortex obtained during a classical delayed alternation working memory task 41 ., This article considers simple discrete-time piecewise-linear ( PL ) recurrent neural networks ( RNN ) of the form, zt=Azt−1+Wmax{0 , zt−1−θ}+Cst+εt , εt∼N ( 0 , Σ ) ,, ( 1 ), where zt = ( z1t…zMt ) T is the ( M×1 ) -dimensional ( latent ) neural state vector at time t = 1…T , A = diag ( a11…aMM ) is an M×M diagonal matrix of auto-regression weights , W = ( 0 w12…w1M , w21 0 w23…w2M , w31 w32 0 w34…w3M , … ) is an M×M off-diagonal matrix of connection weights , θ = ( θ1…θM ) T is a set of ( constant ) activation thresholds , st is a sequence of ( known ) external K-dimensional inputs , weighted by ( M×K ) matrix C , and εt denotes a Gaussian white noise process with diagonal covariance matrix Σ=diag ( σ112…σMM2 ) ., The max-operator is assumed to work element-wise ., In physiological terms , latent variables zmt are often interpreted as a membrane potential ( or current ) which gives rise to spiking activity as soon as firing threshold θm is exceeded ( e . g . 42 , 43 ) ., According to this interpretation , the diagonal elements in A may be seen as the neurons’ individual membrane time constants , while the off-diagonal elements in W represent the between-neuron synaptic connections which multiply with the presynaptic firing rates ., In statistical terms , ( 1 ) has the form of an auto-regressive model with a nonlinear basis expansion in variables zmt ( e . g . 44;45 ) , which retains linearity in parameters W for ease of estimation ., Restricting model parameters , e . g . Σ , to be of diagonal form , is common in such models to avoid over-specification and help identifiabiliy ( e . g . 26; 46; see also further below ) ., For instance , including a diagonal in W would be partly redundant to parameters A ( strictly so in a pure linear model ) ., For similar reasons , and for ease of presentation , in the following we will focus on a model for which K = M and C = I ( i . e . , no separate scaling of the inputs ) , although the full model as stated above , Eq 1 , was implemented as well ( and code for it is provided; of course , the case K>M could always be accommodated by pre-multiplying st by some predefined matrix C , obtained e . g . by PCA on the input space ) ., While different model formulations are around in the computational neuroscience and machine learning literature , often they may be related by a simple transformation of variables ( see 47 ) and , as long as the model is powerful enough to express the whole spectrum of basic dynamical phenomena , details of model specification may also not be overly crucial for the present purposes ., A particular advantage of the PLRNN model is that all its fixed points can be obtained easily analytically by solving ( in the absence of external input ) the 2M linear equations, z*= ( A+WΩ−I ) −1WΩθ ,, ( 2 ), where Ω is to denote the set of indices of units for which we assume zm ≤ θm , and WΩ the respective connectivity matrix in which all columns from W corresponding to units in Ω are set to 0 ., Obviously , to make z* a true fixed point of ( 1 ) , the solution to ( 2 ) has to be consistent with the defined set Ω , that is z*m ≤ θm has to hold for all m ∈ Ω and z*m > θm for all m ∉ Ω ., For networks of moderate size ( say M<30 ) it is thus computationally feasible to explicitly check for all fixed points and their stability ., For estimation from experimental data , latent state model ( 1 ) is then connected to some N-dimensional observed vector time series X = {xt} via a simple linear-Gaussian model ,, xt=Bϕ ( zt ) +ηt , ηt∼N ( 0 , Γ ) ,, ( 3 ), where ϕ ( zt ) ≔ max{0 , zt−θ} , {ηt} is the ( white Gaussian ) observation noise series with diagonal covariance matrix Γ=diag ( γ112…γNN2 ) , and B an N×M matrix of regression weights ., Thus , the idea is that only the PL-transformed activation ϕ ( zt ) reaches the ‘observation surface’ as , e . g . , with spiking activity when the underlying membrane dynamics itself is not visible ., We further assume for the initial state ,, z1∼N ( μ0+s1 , Σ ) ,, ( 4 ), which has , for simplicity , the same covariance matrix as the process noise in general ( reducing the number of to be estimated parameters ) ., In the case of multiple , temporally separated trials , we allow each one to have its own individual initial condition μk , k = 1…K ., The general goal here is to determine both the model’s unknown parameters Ξ = {μ0 , A , W , Σ , B , Γ} ( assuming fixed thresholds θ for now ) as well as the unobserved , latent state path Z ≔ {zt} ( and its second-order moments ) from the experimentally observed time series {xt} ., These could be , for instance , properly transformed multivariate spike time series or neuroimaging data ., This is accomplished here by the Expectation-Maximization ( EM ) algorithm which iterates state ( E ) and parameter ( M ) estimation steps and is developed in detail for model ( 1 ) and ( 3 ) in the Methods ., In the following I will first discuss state and parameter estimation separately for the PLRNN , before describing results from the full EM algorithm in subsequent sections ., This will be done along two toy problems , a higher-order nonlinear oscillation ( stable limit cycle ) , and a simple working memory paradigm in which one of two discrete stimuli had to be retained across a temporal interval ., Finally , the application of the validated PLRNN EM algorithm will be demonstrated on multiple single-unit recordings obtained from rats on a standard working memory task ( delayed alternation; data from 41 , kindly provided by Dr . James Hyman , University of Nevada , Las Vegas ) ., The latent state distribution , as explained in Methods , is a high-dimensional ( piecewise ) Gaussian mixture with the number of components growing as 2T×M with sequence length T and number of latent states M . Here a semi-analytical , approximate approach was developed that treats state estimation as a combinatorial problem by first searching for the mode of the full distribution ( cf ., 16; 48; in contrast , e . g . , to a recursive filtering-smoothing scheme that makes local ( linear-Gaussian ) approximations , e . g . 15; 26 ) ., This approach amounts to solving a high ( 2M×T ) -dimensional piecewise linear problem ( due to the piecewise quadratic , in the states Z , log-likelihood Eqs 6 and 7 ) ., Here this was accomplished by alternating between ( 1 ) solving the linear set of equations implied by a given set of linear constraints Ω ≔ { ( m , t ) |zmt ≤ θm} ( cf . Eq 7 in Methods ) and ( 2 ) flipping the sign of the constraints violated by the current solution z* ( Ω ) to the linear equations , thus following a path through the ( M×T ) -dimensional binary space of linear constraints using Newton-type iterations ( similar as in 49 , see Methods; note that here the ‘constraints’ are not fixed as in quadratic programming problems ) ., Given the mode and state covariance matrix ( evaluated at the mode from the negative inverse Hessian ) , all other expectations needed for the EM algorithm were then derived analytically , with one exception that was approximated ( see Methods for full details ) ., The toy problems introduced above were used to assess the quality of these approximations ., For the first toy problem , an order-15 limit cycle was produced with a PLRNN consisting of three recurrently coupled units , inputs to units #1 and #2 , and parameter settings as indicated in Fig 1 and provided Matlab file ‘PLRNNoscParam’ ., The limit cycle was repeated for 50 full cycles ( giving 750 data points ) and corrupted by process noise ( cf . Fig 1 ) ., These noisy states ( arranged in a ( 3 x 750 ) matrix Z ) were then transformed into a ( 3 x 750 ) output matrix X , to which observation noise was added , through a randomly drawn ( 3 x 3 ) regression weight matrix B . State estimation was started from a random initial condition ., True ( but noise-corrupted ) and estimated states for this particular problem are illustrated in Fig 1A , indicating a tight fit ( although some fraction of the linear constraints were still violated , ≈0 . 27% in the present example and <2 . 3% in the working memory example below; see Methods on this issue ) ., To examine more systematically the quality of the approximate-analytical estimates of the first and second order moments of the joint distribution across states z and their piecewise linear transformations ϕ ( z ) , samples from p ( Z|X ) were simulated using bootstrap particle filtering ( see Methods ) ., Although these simulated samples are based only on the filtering ( not the smoothing ) steps ( and ( re- ) sampling schemes may have issues of their own; e . g . 26 , analytical and sampling estimates were in tight agreement , correlating almost to 1 for this example , as shown in Fig 2 ., Fig 3A illustrates the setup of the ‘two-cue working memory task’ , chosen for later comparability with the experimental setup ., A 5-unit PLRNN was first trained by conventional gradient descent ( ‘real-time recurrent learning’ ( RTRL ) , see 50; 51 ) to produce a series of six 1’s on unit #3 and six 0’s on unit #4 five time steps after an input ( of 1 ) occurred on unit #1 , and the reverse pattern ( six 0’s on unit #3 and six 1’s on unit #4 ) five time steps after an input occurred on unit #2 ., A stable PLRNN with a reasonable solution to this problem was then chosen for further testing the present algorithm ( cf . Fig 3C ) ., ( While the RTRL approach was chosen to derive a working memory circuit with reasonably ‘realistic’ characteristics like a wider distribution of weights , it is noted that a multi-stable network is relatively straightforward to construct explicitly given the analytical accessibility of fixed points ( see Methods ) ; for instance , choosing θ = ( 0 . 5 0 . 5 0 . 5 0 . 5 2 ) , A = ( 0 . 9 0 . 9 0 . 9 0 . 9 0 . 5 ) , and W = ( 0 ω − ω − ω − ω , ω 0 − ω − ω – ω , − ω − ω 0 ω – ω , − ω − ω ω 0 − ω , 11110 ) with ω = 0 . 2 , yields a tri-stable system ., ) Like for the limit cycle problem before , the number of observations was taken to be equal to the number of latent states , and process and observation noise were added ( see Fig 4 and Matlab file ‘PLRNNwmParam’ for specification of parameters ) ., The system was simulated for 20 repetitions of each trial type ( i . e . , cue-1 or cue-2 presentations ) with different noise realizations and each ‘trial’ started from its own initial condition μk ( see Methods ) , resulting in a total series length of T = 20×2×20 = 800 ( although , importantly , in this case the time series consisted of distinct , temporally segregated trials , instead of one continuous series , and was treated as such an ensemble of series by the algorithm ) ., As before , state estimation started from random initial conditions and was provided with the correct parameters , as well as with the observation matrix X . While Fig 3B illustrates the correlation between true ( i . e . , simulated ) and estimated states across all trials and units , Fig 3C shows true and estimated states for a representative cue-1 ( left ) and cue-2 ( right ) trial , respectively ., Again , our procedure for obtaining ( or approximating ) the maximum a-posteriori ( MAP ) estimate of the state distribution appears to work quite well ( in general , only locally optimal or approximate solutions may be achieved , however , and the algorithm may have to be repeated with different state initializations; see Methods ) ., Given the true states , how well would the algorithm retrieve the parameters of the PLRNN ?, To assess this , the actual model states ( which generated the observations X ) from simulation runs of the oscillation and the working memory task described above were provided as initialization for the E-step ., Based on these , the algorithm first estimated the state covariances for z and ϕ ( z ) ( see above ) , and then the parameters in a second step ( i . e . , the M-step ) ., Note that the parameters can all be computed analytically given the state distribution ( see Methods ) , and , provided the state covariance matrices ( summed across time ) as required in Eq 17A , 17D and 17F are non-singular , have a unique solution ., Hence , in this case , any misalignment with the true model parameters can only come from one of two sources:, i ) estimation was based on one finite-length noisy realization of the PLRNN process ,, ii ) all second order moments of the state distribution were still estimated based on the true state vectors ., However , as can be appreciated from Fig 1B ( oscillation ) and Fig 4 ( working memory ) , for the two ( relatively low-noise ) example scenarios studied here , all parameter estimates still agreed tightly with those describing the true underlying model ., In the more general case where both the states and the parameters are unknown and only the observations are given , note that the model as stated in Eqs 1 & 3 is over-specified as , for instance , at the level of the observations , additional variance placed into Σ may be compensated for by adjusting Γ accordingly , and by rescaling W and , within limits , A ( cf . 52; 53 ) ., In the following we therefore always arbitrarily fixed Σ ( to some scalar; see Methods ) , as common in many latent variable models ( like factor analysis ) , including state space models ( e . g . 27; 46 ) ., It may be worth noting here that the relative size of Σ vs . Γ determines how much weight is put on temporal consistency among states ( “Σ<Γ” ) vs . fitting of the observations ( “Σ>Γ” ) within the likelihood , Eq 5 ., The observations above confirm that our algorithm finds satisfactory approximations to the underlying state path and state covariances when started with the right parameters , and—vice versa—identifies the correct parameters when provided with the true states ., Indeed , the M-step , since it is exact , can only increase the expected log-likelihood Eq 5 with the present state expectancies fixed ., However , due to the systems piecewise-defined discrete nature , modifying the parameters may lead to a new set of constraint violations , that is may throw the system into a completely different linear subspace which may imply a decrease in the likelihood in the E-step ., It is thus not guaranteed that a straightforward EM algorithm converges ( cf . 54; 55 ) , or that the likelihood would even monotonically increase with each EM iteration ., To examine this issue , full EM estimation of the WM model ( as specified in Fig 4 , using N = 20 outputs in this case ) was run 240 times , starting from different random , uniformly distributed initializations for the parameters ., Fig 5B ( Δt = 0 ) gives , for the five highest likelihood solutions across all 240 runs ( Fig 5A ) , the mean squared error ( MSE ) avg ( xit−x^it ) 2 between actual neural observations xit and model predictions x^it , which is close to 0 ( and , correspondingly , correlations between predicted and actual observations were close to 1 ) ., With respect to the inferred states , note that estimated and true model states may not be in the same order , as any permutation of the latent state indices together with the respective columns of observation matrix B will be equally consistent with the data X ( see also 27 ) ., For the WM model examined here , however , partial order information is implicitly provided to the EM algorithm through the definition of unit-specific inputs sit ., For the present example , true and estimated states for the highest likelihood solution were nicely linearly correlated for all 5 latent variables ( Fig 6 ) , but some of the regression slopes significantly differed from 1 , indicating a degree of freedom in the scaling of the states ., Note that if the system were strictly linear , the states would be identifiable only up to a linear transformation in general , since any multiplication of the latent states by some matrix V could essentially be reversed at the level of the outputs by back-multiplying B with V-1 ( cf . 27 ) ., Likewise , in the present piecewise linear system , one may expect that there is a class of piecewise-linear transformations of the states which is still compatible with the observed outputs , and hence that the model is only identifiable up to this class of transformations ( a general issue with state space models , of course , not particular to the present one; cf . 53 ) ., However , this might not be a too serious issue , if one is primarily interested in the latent dynamics ( rather than in the exact parameters ) ., Fig 7 illustrates the distribution of initial and final parameter estimates around their true values across all 240 runs ( before and after reordering the estimated latent states based on the rotation that would be required for achieving the optimal mapping onto the true states , as determined through Procrustes analysis ) ., Fig 7 reveals that, a ) the EM algorithm does clearly improve the estimates and, b ) these final estimates seemed to be relatively ‘unbiased’ ( i . e . , with deviations centered around 0 ) ., How do the computational costs of the algorithm grow as the number of latent variables in the model is increased ?, As pointed out in Paninski et al . 16 , exploiting the block-tridiagonal nature of the covariance matrices , the numerical operations within one iteration of the state inference algorithm ( i . e . , solving ∂QΩ* ( Z ) /∂Z=0 , Eq 7 ) can be done in linear , O ( M×T ) , time , just like with the Kalman filter ( due to the model’s Markov properties , full inversion of the Hessian is also not necessary to obtain the relevant moments of the posterior state distribution ) ., This leaves open the question of how many more mode search iterations , i . e . linear equation solving ( Eq 7 ) and constraint-flipping ( vector dΩ ) steps , are required as the number of latent variables ( through either M or T ) increases ., The answer is provided in Fig 8A which is based on the experimental data set discussed below ., Although a full computational complexity analysis is beyond the scope of this paper , at least for these example data ( and similar to what has sometimes been reported for the somewhat related Simplex algorithm; 56 ) , the increase with M appears to be at most linear ., Likewise , the total number of iterations within the full EM procedure , i . e . the number of mode-search steps summed across all EM iterations ( thus reflecting the overall scaling of the full algorithm ) , was about linear ( Fig 8B; in this case , single-constraint instead of complete flipping ( see Methods ) was used which , of course , increases the overall number of iterations but may perform more stably; note that in general the absolute number of iterations will also depend on detailed parameter settings of the algorithm , like the EM convergence criterion and error tolerance ) ., Thus , overall , the present state inference algorithm seems to behave quite favorably , with an at most linear increase in the number of iterations required as the number of latent variables is ramped up ., I next was interested in what kind of structure the present PLRNN approach would retrieve from experimental multiple ( N = 19 ) single-unit recordings obtained while rats were performing a simple and well-examined working memory task , namely spatial delayed alternation 41 ( see Methods ) ., ( Note that in the present context this analysis is mainly meant as an exemplification of the current model approach , not as a detailed examination of the working memory issue itself . ), The delay was always initiated by a nose poke of the animal into a port located on the side opposite from the response levers , and had a minimum length of 10 s ., Spike trains were first transformed into kernel density estimates by convolution with a Gaussian kernel ( see Methods ) , as done previously ( e . g . 12; 57; 58 ) , and binned with 500 ms resolution ., This also renders the observed data more suitable to the Gaussian noise assumptions of the present observation model , Eq 3 ., Models with different numbers of latent states were estimated , with M = 5 or M = 10 chosen for the examples below ., Periods of cue presentation were indicated to the model by setting external inputs sit = 1 to units i = 1 ( left lever ) or i = 2 ( right lever ) for three 500 ms time bins surrounding the event ( and sit = 0 otherwise ) , and the response period was indicated by setting s3t = 1 for 3 consecutive time bins irrespective of the correct response side ( i . e . , non-discriminatively ) ., The EM algorithm was started from a range of different initializations of the parameters ( including thresholds θ ) , and the 5 highest likelihood solutions were considered further for the examples below ., Fig 10A gives the log-likelihoods across EM iterations for these 5 highest-likelihood solutions ( starting from 36 different initializations ) for the M = 5 model ., Interestingly , there were single neurons whose responses were predicted quite well by the estimated model despite large trial-to-trial fluctuations ( Fig 9A , top row ) , while there were others with similar trial-to-trial fluctuations for which the model only captured the general trend ( Fig 9A , bottom row; to put this into context , Fig 9B gives the full distribution of correlations between actual and predicted observations across all 19 neurons ) ., This may potentially indicate that trial-to-trial fluctuations in single neurons could be for very different reasons: For instance , in those cases where strongly varying single unit responses are nevertheless tightly reproduced by the estimated model , a larger proportion of their trial-to-trial fluctuations may have been captured by the latent state dynamics , ultimately rooted in different ( trial-unique ) initializations of the states ( recall that the states are not completely free to vary in accounting for the observations , but are constrained by the model’s temporal consistency requirements ) ., In contrast , when only the average trend is captured , the neuron’s trial-to-trial fluctuations may be more likely to represent true intrinsic ( or measurement ) noise sources that the model’s deterministic part cannot account for ., In practice , such conclusions would have to be examined more carefully to rule out that no other factors in the estimation procedure , like different local maxima , initializations , or over-fitting issues ( see below ) , could account for these differences ., Although this was not further investigated here , this observation nevertheless highlights the potential of ( nonlinear ) state space models to possibly provide new insights also into other long-standing issues in neurophysiology ., Cross-validation is an established means to address over-fitting 45 , although due to the presence of both unknown parameters and unknown states , its application to state space models and its interpretation in this context may be a bit less straightforward ., Here the cross-validation error was first assessed by leaving out each of the 14 experimental trials in turn , estimating model parameters Ξ from the remaining 13 trials , inferring states zt given these parameters on the left-out trial , and computing the squared prediction errors ( xit−x^it ) 2 between actual neural observations xit and model predictions x^it on the left-out trial ., As shown in Fig 10B , this measure steadily ( albeit sub-linearly ) decreases as the number M of latent states in the model is increased ., At first sight , this seems to suggest that with M = 5 or even M = 10 the over-fitting regime is not yet reached ., On the other hand , the latent states are , of course , not completely fixed by the transition equations , but have some freedom to vary as well ( the true effective degrees of freedom for such systems are in fact very hard to determine , cf . 59 ) ., Hence , we also examined the Δt-step-ahead prediction errors , that is , when the transition model were iterated Δt steps forward in time , and x^i , t+Δt=bi⋅ϕ ( z^t+Δt ) estimated from the deterministically predicted states z^t+Δt=HΔt ( Ezt ) ( with HΔt the Δt-times iterated map H ( zt ) = Azt + Wϕ ( zt ) + Cst ) , not from the directly inferred states ( that is , predictions were made on data points which were neither used to estimate parameters nor to infer the current state ) ., These curves are shown for Δt = 1 and Δt = 3 in Fig 10C , and confirm that M = 5 might be a reasonable choice at which over-fitting has not yet ensued ., ( Alternatively , the predictive log-likelihood , logp ( Xtest|Ξ^train ) =log∫p ( Xtest|Z^ ) p ( Z^|Ξ^train ) dZ^ , may be used for model selection ( i . e . , choice of M ) , with p ( Z^|Ξ^train ) either approximated through the E-step algorithm ( with all X-dependent terms removed ) , or bootstrapped by generating Z^-trajectories from the model with parameters Ξ^train ( note that this is different from particle filtering since p ( Z^|Ξ^train ) does not depend on test observations Xtest ) ., This is of course , however , computationally more costly to evaluate than the Δt-step-ahead prediction error ., ) Fig 11 shows trial-averaged latent states for both left- and right-lever trials , illustrated in this case for one of the five highest likelihood solutions ( starting from 100 different initializations ) for the M = 10 model ., Recall that the first 3 PLRNN units received external inputs to indicate left cue ( i = 1 ) , right cue ( i = 2 ) , or response ( i = 3 ) periods , and so , not too surprisingly , reflect these features in their activation ., On the other hand , the cue response is not very prominent in unit i = 1 , indicating that activity in the driven units is not completely dominated by the external regressors either , while unit i = 10 ( not externally driven ) shows a clear left-cue response ., Most importantly , many of the remaining state variables clearly distinguish between the left and right lever options throughout the delay period of the task , in this sense carrying a memory of the cue ( previous response ) within the delay ., Some of the activation profiles appear to systematically climb or decay across the delay period , as reported previously ( e . g . 1; 60 ) , but are a bit harder to read ( at least in the absence of more detailed behavioral information ) , such that one may want to stick with the simpler M = 5 model discussed above ., Either way , for this particular data set , the extracted latent states appear to summarize quite well the most salient computational features of this simple working memory task ., Further insight about the dynamical mechanisms of working memory might be gained by examining the system’s fixed points and their eigenvalue spectrum ., For this purpose , the EM algorithm was started from 400 different initial conditions ( that is , initial parameter estimates and threshold settings θ ) with maximum absolute eigenvalues ( of the corresponding fixed points ) drawn from a relatively uniform distribution within the interval 0 3 ., Although the estimation process rarely returned truly multi-stable solutions ( just 2 . 5% of all cases ) , one frequently discussed candidate mechanism for working memory ( e . g . 29; 32 ) , there was a clear trend for the final maximum absolute eigenvalues to aggregate around 1 ( Fig 12 ) ., For the discrete-time dynamical system ( 1 ) this implies it is close to a bifurcation , with fixed points on the brink of becoming unstable , and will tend to produce ( very ) slow dynamics as the degree of convergence shrinks to zero along the maximum eigenvalue direction ( strictly , a single eigenvalue near 1 does not yet guarantee a slow approach , but makes it very likely , especially in a ( piecewise ) linear system ) ., Indeed , effectively slow dynamics is all that is needed to bridge the delays ( see also 1 ) , while true multi-stability may perhaps even be the physiologically less likely scenario ( e . g . 61; 62 ) ., ( Reducing the bin width from 500 ms to 100 ms appeared to produce solutions with eigenvalues even closer to 1 while retaining stimulus selectivity across the delay , but this observation was not followed up more systematically here ) ., Linear dynamical systems ( LDS ) have frequently and successfully been used to infer smooth neural trajectories from spike train recordings 15; 16; 20; 22 or other measurement modalities 63 ., However , as noted before , they

Introduction, Results, Discussion, Models and methods

The computational and cognitive properties of neural systems are often thought to be implemented in terms of their ( stochastic ) network dynamics ., Hence , recovering the system dynamics from experimentally observed neuronal time series , like multiple single-unit recordings or neuroimaging data , is an important step toward understanding its computations ., Ideally , one would not only seek a ( lower-dimensional ) state space representation of the dynamics , but would wish to have access to its statistical properties and their generative equations for in-depth analysis ., Recurrent neural networks ( RNNs ) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective ., Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs ( PLRNNs ) within the statistical framework of state space models , which accounts for noise in both the underlying latent dynamics and the observation process ., The Expectation-Maximization algorithm is used to infer the latent state distribution , through a global Laplace approximation , and the PLRNN parameters iteratively ., After validating the procedure on toy examples , and using inference through particle filters for comparison , the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex ( ACC ) obtained during performance of a classical working memory task , delayed alternation ., Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance , including stimulus-selective delay activity ., The estimated models were rarely multi-stable , however , but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point ., In summary , the present work advances a semi-analytical ( thus reasonably fast ) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects of the nonlinear dynamics underlying observed neuronal time series , and directly link these to computational properties .

Neuronal dynamics mediate between the physiological and anatomical properties of a neural system and the computations it performs , in fact may be seen as the ‘computational language’ of the brain ., It is therefore of great interest to recover from experimentally recorded time series , like multiple single-unit or neuroimaging data , the underlying stochastic network dynamics and , ideally , even equations governing their statistical evolution ., This is not at all a trivial enterprise , however , since neural systems are very high-dimensional , come with considerable levels of intrinsic ( process ) noise , are usually only partially observable , and these observations may be further corrupted by noise from measurement and preprocessing steps ., The present article embeds piecewise-linear recurrent neural networks ( PLRNNs ) within a state space approach , a statistical estimation framework that deals with both process and observation noise ., PLRNNs are computationally and dynamically powerful nonlinear systems ., Their statistically principled estimation from multivariate neuronal time series thus may provide access to some essential features of the neuronal dynamics , like attractor states , generative equations , and their computational implications ., The approach is exemplified on multiple single-unit recordings from the rat prefrontal cortex during working memory .

neural networks, applied mathematics, neuroscience, learning and memory, simulation and modeling, algorithms, cognitive neuroscience, systems science, mathematics, cognition, algebra, memory, research and analysis methods, computer and information sciences, animal cells, dynamical systems, nonlinear dynamics, approximation methods, working memory, cellular neuroscience, cell biology, linear algebra, neurons, biology and life sciences, cellular types, physical sciences, cognitive science, eigenvalues

journal.pntd.0001789

Phenotypic Characterization of Peripheral T Cells and Their Dynamics in Scrub Typhus Patients

Scrub typhus is an acute febrile illness caused by Orientia tsutsugamushi infection , an obligate intracellular bacterium , following the bite of infected larval mites 1 ., While the disease is confined geographically to the Asia-Pacific region , it has been estimated that one billion people are at risk and one million new cases arise each year in the endemic region 2 ., In addition , this infectious disease has recently become an important public health issue due to regional outbreaks 3 , 4 and new emergence 5 , 6 ., Clinical presentations of scrub typhus , typically characterized by eschar , fever , rash , lymphadenopathy , and myalgia , can vary in severity from a mild and self-limiting flu-like syndrome to a life-threatening disease 1 , 7 ., If not properly treated in the early stage or left untreated , patients often develop severe pneumonitis , meningitis , renal failure , myocarditis , and disseminated intravascular coagulation 7 , 8 ., The diverse pathologic changes in multiple organs are mainly due to focal or disseminated multi-organ vasculitis or perivasculitis of small blood vessels since O . tsutsugamushi primarily infects endothelial cells 9 , 10 , 11 ., Although it has been suggested that adaptive immune cells , such as CD8+ T cells , may cause injury to vascular endothelial cells , leading to vasculitis or perivascularitis during infections by diverse intracellular pathogens 12 , 13 , little is known about the underlying mechanisms of the pathologic damage observed in scrub typhus patients ., Despite aggressive attempts to develop a prophylactic vaccine against scrub typhus , all approaches have failed to generate long lasting immunity in humans 6 ., It has been well established that effective cell-mediated immunity is required for protection against Orientia infection in murine models 1 , 6 ., Mice infected with O . tsutsugamushi show enhanced IFN-γ expression 14 , 15 and transfer of IFN-γ-producing Th1 cells protects mice against O . tsutsugamushi 16 ., In a nonhuman primate model , Orientia infection or vaccination induces antigen-specific proliferation of lymphocytes and IFN-γ production by peripheral blood mononuclear cells ( PBMCs ) 17 , 18 ., However , long-lasting memory responses have never been achieved in primate models 18 ., It is also notable that rapid immunosuppression at both humoral and cellular levels has been consistently observed in immunized animals right after bacterial challenge 17 , 19 and recurrent human infection is relatively common in highly endemic regions despite T cell activation during primary infection 20 ., Here , we examined phenotypic characteristics of peripheral blood leukocytes and their dynamics in scrub typhus patients ., Comparative analysis of scrub typhus patients immune cells during acute and convalescent phases of infection with those from healthy controls revealed a dynamic fluctuation of leukocyte populations , especially T cells , during the course of the disease ., Ethical approval for this work was granted by the Institutional Review Board of both Seoul National University Hospital ( IRB No . 0-1001-039-307 ) and Chungnam National University Hospital ( IRB No . 2008-10-08 ) ., All patients and healthy volunteers provided written informed consent prior to sample collection ., Human peripheral blood was drawn from healthy volunteers ( n\u200a=\u200a9 ) and scrub typhus patients ( n\u200a=\u200a34 ) after obtaining informed consent at Chungnam National University Hopspital in Deajon , South Korea ., Scrub typhus was confirmed by the presence of O . tsutsugamushi-specific antibody titer greater than 1∶400 in serum from patients with acute febrile disease and/or at least a four-fold increase in antibody titer ., Scrub typhus patients and healthy controls were matched for gender ( p\u200a=\u200a0 . 455 , Fishers exact test ) and age ( age mean ± SD , patients 57 . 6±13 . 8 versus healthy 63 . 0±3 . 3 years , Students t test , p\u200a=\u200a0 . 2687 ) ., Blood samples were collected from each patient at two time points , once during the acute phase ( samples drawn upon admission and before antibiotic treatment ) and again during the convalescent phase ( samples drawn after antibiotic treatment ) of scrub typhus ., Acute phase is defined as the period between the onset of symptoms to admission ( Median\u200a=\u200a8 days after onset of symptoms , 95% confidence interval ( CI ) =\u200a6 . 4 to 14 . 3 , range 1 to 30 days ) ., Convalescent phase begins about 10 days after the acute phase ( Median\u200a=\u200a19 . 5 days after onset of symptoms , 95% confidence interval ( CI ) =\u200a18 . 0 to 26 . 0 , range 13 to 43days ) ., Among the patients , four have diabetes and three have been diagnosed with cancer ., All of them had been properly treated before the Orientia infection , showed no prior signs of immunodeficiency in their medical records , and successfully recovered from scrub typhus after antibiotics therapy ., The individual clinical information of all the scrub typhus patients enrolled in the study are presented in Table S1 and summarized in Table S2 ., The leukocytes differential count was determined using a Sysmex XE-2100 hematology analyzer ( Sysmex Corporation , Kobe , Japan ) , which differentiates leukocytes by simultaneously measuring volume , structure , and fluorescence 21 ., PBMCs were isolated by standard density centrifugation with Histopaque ( GE Healthcare , Little Chalfont , United Kingdom ) and stored at liquid nitrogen after suspension in freezing media ( 50% fetal bovine serum , 10% DMSO , and 40% RPMI-1640 , Invitrogen , Carlsbad , CA ) until analysis ., Each subset of leukocytes was analyzed simultaneously in order to minimize the variation due to staining procedures ., Cells were stained with various antibodies to analyze the frequency of leukocyte populations and their cellular characteristics as follows; to analyze the frequencies and absolute counts of CD4+ and CD8+ T cell subsets , cells were stained with antibodies against CD3 and CD8 ., The frequency of each population was examined after gating lymphocyte population ( 1 ) ., The frequencies and phenotypic characteristics of CD4+ and CD8+ T cell subsets were further analyzed by co-staining with antibodies to surface antigens , CD4 and CD8 in addition to CD25 , CD45RA , CCR4 , CCR7 , CXCR3 , Fas ( CD95 ) , PD-1 ( all from BD Biosciences , San Jose , CA ) , IL-7Rα ( CD127 ) ( R&D systems , Minneapolis , MN ) or isotype controls ., Some cells were co-stained with annexin V ( BD Biosciences ) in order to measure the cellular apoptosis ., For staining intracellular antigens , cells were stained with anti-Foxp3 ( Biolegend , San Diego , CA ) , CTLA-4 , Ki-67 ( BD Biosciences ) antibodies , or isotype controls after fixation and permeabilization ., For measuring the frequencies of natural killer ( NK ) cells , PBMCs were stained with antibodies to CD3 , CD4 , CD8 , and CD56 ( BD Biosciences ) ., Representative gating strategies for each assay are presented in Figure S1 , S2 , S3 ., Samples ( 2×105∼1×106 events per sample ) were collected and analyzed with an LSRII® ( BD Immunocytometry Systems , San Jose , CA ) ., Data were analyzed using FlowJo® software ( Tree Star , Ashland , OR ) ., The absolute count of each lymphocyte population in the patients were calculated based on leukocyte differential counts and frequency data obtained from FACS analysis are presented in Table S3 and summarized in Table S4 ., Bio-plex cytokine assay ( Bio-Rad Inc . , Hercules , CA ) was used to quantify soluble IL-7 , IL-10 , IL-15 , and IFN-γ in the serum according to the manufacturers instructions ., Samples were measured and analyzed on a Bio-Plex 200 system ( Bio-Rad ) in combination with the Bio-Plex Manager software ( Bio-Rad ) ., All data are expressed as mean ± standard deviation or mean ± standard error of the mean ., The two-tailed Students t test , Wilcoxon sign-rank test , and Mann-Whitney U test were used to compare measurable variables between patients and healthy controls , or patients in acute phase and convalescent phase of infection ., p values<0 . 05 were considered statistically significant ., All statistical analyses were accomplished using GraphPad Prism 5 . 01 ( GraphPad Software Inc . , La Jolla , CA ) ., Whole blood leukocytes were collected from scrub typhus patients during acute and convalescent phases and analyzed for changes in frequencies and subsets ., The absolute numbers ( cells/mm3 , mean ± SD ) of the peripheral blood leukocytes were generally increased in the patients at both acute ( 7 , 175±2 , 296 ) and convalescent ( 7 , 012±1 , 844 ) phases when compared to those of healthy controls ( 5 , 794±742 ) ., Significant changes were detected in neutrophil and lymphocyte populations ( Figure 1A ) ., The frequency ( % , mean ± SD ) of neutrophils was significantly increased during acute phase ( 62 . 96±17 . 66 ) and then returned to baseline levels during convalescent phase ( 41 . 28±15 . 72; healthy controls , 48 . 61±5 . 70 ) ., In contrast , lymphocytes were significantly reduced ( 24 . 49±12 . 73 ) during acute phase and returned to baseline ( 45 . 91±16 . 23 ) in convalescent phase compared to healthy controls ( 40 . 71±5 . 62 ) ., The absolute numbers of each leukocyte population were also changed in similar pattern ( Table S3 and S4 ) ., To examine the population changes of lymphocytes in detail , PBMCs were further analyzed by flow cytometry ( Figure 1B ) ., The decrease of lymphocytes in the patients was mainly due to a significant reduction in CD4+ T cells in both frequency ( % , 27 . 96±15 . 70 ) and number ( cells/mm3 , 498 . 5±387 . 9 ) during acute phase compared with healthy controls ( % , 44 . 33±11 . 24; cells/mm3 , 1 , 072 . 0±382 . 4 ) ., CD8+ T cells were not significantly different in frequency during acute and convalescent phase but were significantly increased in total numbers during the convalescent phase ( 681 . 8±395 . 4 ) when compared to that of acute phase ( 329 . 4±349 . 5 ) ., In order to investigate the mechanism of reduced T cell frequencies during Orientia infection , we next determined the frequencies of apoptotic and proliferating T cells in the patients blood by flow cytometry ., Apoptotic T cells were identified by staining with annexin V and proliferating cells were evaluated by a cellular proliferation marker , Ki-67+ 22 ., As shown in Figure 2A , the frequencies of apoptotic CD4+ ( % , 29 . 60±12 . 58 ) and CD8+ ( 43 . 87±24 . 97 ) T cells within each T cell subset were significantly increased during acute phase of infection when compared with healthy controls ( CD4+ , 15 . 23±4 . 39; CD8+ , 15 . 32±4 . 68 ) ., During convalescent phase , the percentage of apoptotic CD4+ T cells returned to baseline levels but the percentage of apoptotic CD8+ T cells ( 27 . 00±10 . 36 ) was still higher than in healthy controls ., Cellular proliferation was also significantly increased during acute phase of infection in both CD4+ and CD8+ populations ( Figure 2B ) ., During convalescent phase , however , the frequency of proliferating CD8+ T cells ( 32 . 18±13 . 65 ) was remarkably increased when compared with healthy controls ( 1 . 13±0 . 59 ) , whereas proliferation of CD4+ T cells returned to basal level ., These results indicate that both CD4+ and CD8+ T cells undergo rapid turnover during acute phase and CD8+ T cells proliferate more actively than CD4+ T cells during convalescent phase ( Figure 2C ) ., To investigate the potential mechanisms of the rapid turnover and proliferation of T cells in scrub typhus patients , we measured the amount of soluble IL-7 and IL-15 in the serum of patients since these cytokines induce the proliferation of and enhance the survival of T cells in mice and humans 23 ., The levels of IL-7 were significantly higher in the patients sera ( pg/ml , 4 . 12±2 . 34 and 3 . 14±1 . 58 in acute and convalescent phase respectively ) than in healthy controls ( 1 . 40±0 . 70 ) ( Figure 2D ) ., The levels of soluble IL-15 in infected patients were also increased during acute phase ( 2 . 00±1 . 93 ) when compared with healthy controls ( 0 . 61±0 . 30 ) ., Since the balance of effector T cells plays a critical role in controlling an infection , we next examined the overall changes in effector phenotypes of T cells in the patients ., Human effector T cells can be categorized into type 1 CD4+ ( Th1 ) and CD8+ ( Tc1 ) T cells or type 2 CD4+ ( Th2 ) and CD8+ ( Tc2 ) T cells , based on preferential expression of CXCR3 or CCR4 , respectively 24 , 25 ., As shown in Figure 3 , the frequencies of CD4+CXCR3+ Th1 ( % , 10 . 59±3 . 53 in acute phase versus 21 . 28±4 . 60 in healthy controls ) and CD8+CXCR3+ Tc1 cells ( 13 . 44±6 . 82 versus 25 . 77±10 . 66 ) were transiently reduced in infected patients , whereas CD4+CCR4+ Th2 and CD8+CCR4+ Tc2 cells were not changed significantly ., Regulatory T cells ( Treg ) expressing Foxp3 and high levels of CD25 ( CD25++ ) are required for maintaining peripheral tolerance to self-antigen and controlling immune responses during an infection by inhibiting the activation of effector T cells 26 ., The frequency of Treg cells and their phenotypical characteristics in the peripheral blood of scrub typhus patients were examined for the first time ., Foxp3 expression correlated well with the high level of CD25 surface expression in CD4+ T cells ( Figure S4 ) ., Interestingly , both CD4+CD25++ and CD4+Foxp3+ T cells were significantly reduced in scrub typhus patients ( CD4+CD25++ , % , 0 . 191±0 . 194 in acute phase and 0 . 256±0 . 22 in convalescent phase; CD4+Foxp3+ , 0 . 363±0 . 361 in acute phase and 0 . 683±0 . 480 in convalescent phase ) when compared with healthy controls ( CD4+CD25++ , 1 . 096±0 . 250; CD4+Foxp3+ , 1 . 844±0 . 451 ) ( Figure 4A and B ) ., Treg cells were generally increased during convalescent phase compared to that of acute phase , but were still significantly lower than the levels in healthy controls ., To further investigate whether the cellular phenotypes of Treg cells are altered during Orientia infection , we determined the expression levels of CTLA-4 , Fas , and CCR4 , which are involved in suppressor function and migration 26 , 27 ., Despite the significant reduction in Treg frequency in the patients blood , the expression levels of CTLA-4 , Fas , and CCR4 in Treg cells were quite similar to those of healthy controls ( Figure 4C ) ., Next , we investigated the changes in the distribution of effector and memory T cell subsets in the peripheral blood of scrub typhus patients ., The differentiation status of T cells was defined based on differential staining of CCR7 and CD45RA ( Figure 5 ) 28 ., Based on phenotypic markers , T cell subsets can be classified into 4 four subsets: naïve ( CCR7+CD45RA+ ) , central memory ( CM , CCR7+CD45RA− ) , effector memory ( EM , CCR7−CD45RA− ) , and CCR7−CD45RA+ cells ( EMCD45RA+ for CD8+ and CCR7−CD45RA+ for CD4+ T cells ) ., In contrast to the significant reduction of CD4+ T cells during acute phase of infection ( Figure 1 ) , the distribution of CD4+ T cell subsets in healthy controls and scrub typhus patients were generally consistent , with the exception of CM ( % , 21 . 7±12 . 6 in acute phase versus 28 . 0±6 . 8 in healthy controls ) and CCR7−CD45RA+ ( 21 . 3±13 . 1 versus 9 . 1±5 . 8 ) ( Figure 5A and B ) ., For CD8+ T cells , the naive subset was significantly decreased in scrub typhus patients ( 11 . 9±10 . 5 in acute and 8 . 0±7 . 8 in convalescent phase ) compared to that of healthy controls ( 29 . 4±16 . 2 ) ., In contrast , the frequency of EM CD8+T cells in the patients ( 52 . 6±17 . 4 in acute and 61 . 4±15 . 2 in convalescent phase ) was significantly increased ( 35 . 1±9 . 7 in healthy controls ) ., The frequency of EM CD8+ T cells was further increased during the convalescent phase compared to the acute phase ( p\u200a=\u200a0 . 048 ) , whereas EMCD45RA+ CD8+ T cells were significantly decreased during the convalescent phase ( p\u200a=\u200a0 . 008 ) ., The dramatic increase in EM CD8+ T cells in scrub typhus patients prompted us to further investigate the activation status of CD8+ T cells in patients ., We evaluated the distribution of CD8+ T cells expressing low levels of IL-7 receptor alpha ( IL-7Rαlow ) or high levels of programmed death-1 ( PD-1high ) within the CD8+ T cell population ( Figure 6 ) ., These subpopulations mainly include antigen-experienced EM subsets and activated CD8+ T cells 29 , 30 ., As shown in Figure 6 , the frequencies of IL-7Rαlow subsets and PD-1high CD8+ T cells were generally increased in scrub typhus patients ., While IL-7Rαlow CD8+ T cells comprised more than 70% of the total CD8+ T cells throughout the infection , PD-1high cells did not persist and were reduced during the convalescent phase ., Taken together , these data suggest that the major alterations in CD8+ T cell populations in scrub typhus patients might be due to the proliferation of activated CD8+ T cells that differentiated from T cells in early stages ( naïve and CM T cells ) ., Like other infectious diseases , Orientia infection generally induces marked increases in total blood leukocytes ., Since all the patients were treated with antibiotics , this may affect the immune responses seen in the convalescent phase ., Nevertheless , neutrophilia is dominant in the acute phase and is followed by lymphocytosis , largely due to a rise in CD8+ T cells , during the convalescent phase in scrub typhus patients , as previously reported 20 , 31 ., During acute phase , however , we observed a significant reduction in T cell frequencies and total numbers , especially CD4+ cells ( Figure 1B ) ., Acute CD4+ T-lymphopenia has also been observed in other infectious diseases 32 , 33 , 34 and might be explained by the migration of circulating lymphocytes to inflamed tissues or by apoptotic cell death 35 ., Here , we detected a significant increase in apoptosis in peripheral T cells during acute phase ( Figure 2A ) as observed previously in a mouse model infected with O . tsutsugamushi 36 ., One mechanism for T cell apoptosis is activation-induced cell death ( AICD ) , which occurs after the expansion of T cells responding to antigenic stimulation and increased IL-2 37 ., The increase of apoptotic cell death in T cells observed in scrub typhus patients , however , may not solely be due to AICD since it has been shown that the serum IL-2 levels in scrub typhus patients do not change during the symptomatic period 38 ., In addition , the massive apoptosis of peripheral T cells observed in this study ( Figure 2A ) , is quite similar to the sepsis phenotype 35 in which engagement of TCRs by cognate antigen is not required for T cell apoptosis 39 ., Although the underlying mechanisms of T cell apoptosis in scrub typhus patients needs to be defined , the massive apoptotic death of T cells might contribute to the transient immunosuppression observed in animals experimentally challenged with O . tsutsugamushi 17 , 19 ., Previously , it was also reported that antigen-specific CD4+ T cells are rapidly deleted from blood after infection with several pathogens such as Anaplasma marginales 40 , Plasmodium 41 , and Brugia Pahangi 42 ., Rapid decline of antigen-specific CD4+ T cells during the acute infection may be as strategy for the pathogens to modulate the immune responses and eventually leads to the loss of immunological memory 40 ., Although the systemic decline of antigen-specific CD4+ T cells during O . tsutsugamushi infection was not examined in the current study , the systemic apoptosis and lymphopenia of CD4+ T cells observed in scrub typhus patients might explain the absence of memory responses observed in vaccine trials and recurrent human infection ., In addition , the CD4+ T cell lymphopenia observed during the acute phase of scrub typhus may lead to a defect in generating long-lived functional memory CD8+ T cells , i . e . the helpless CD8+ T cell responses , because CD4+ T-cell help is critical for generating functionally-competent memory CD8+ T-cells , 43 , 44 ., Interestingly , we also detected a remarkable increase in Ki-67+ T cells , indicating that T cells actively proliferate during the acute phase of scrub typhus ( Figure 2B ) ., The contradictory finding of increased apoptosis and proliferation of T cells in the peripheral blood of scrub typhus patients prompted us to measure the levels of IL-7 and IL-15 , which potently induce the survival and proliferation of T cells 23 ., Consistent with another report 45 , both of these cytokines were significantly increased in the serum of scrub typhus patients ( Figure 2D ) ., Induction of IL-7 and IL-15 may occur as part of a homeostatic response to T-cell depletion and this would account for the recovery of T cells during the convalescent phase ., IL-15 is also significantly induced in endothelial cells infected with O . tsutsugamushi 46 ., Given that CD8+ T cells proliferate more efficiently than CD4+ T cells in the presence of both cytokines 23 , extensive proliferation of CD8+ T cells during the convalescent phase ( Figure 2B and C ) could be explained by an increase in these cytokines ., Our current results strongly suggest that T cells , especially CD8+ T cells , undergo rapid turnover during Orientia infection ., This phenomenon has also been observed in other infections 47 ., Although Th1-mediated cellular immunity and IFN-γ production by T cells in response to O . tsutsugamushi infection is critical for protection , Th1 and Th2 type responses are not clearly polarized in animal infection models 15 or human scrub typhus patients 38 ., In this study , we also observed elevated IFN-γ ( pg/ml , 24 . 97 l±24 . 00 in the patients versus 1 . 69±4 . 76 in healthy controls ) and IL-10 ( pg/ml , 10 . 75±6 . 93 versus 4 . 23±7 . 37 ) during the acute phase of infection although both cytokines returned to baseline levels during the convalescent phase ., The selective reduction of type 1 T cells in the peripheral blood during the acute phase ( Figure 3 ) could be explained by the specific recruitment of these cells to inflamed tissues ., Indeed , it was previously shown that IFN-γ-inducible protein 10 ( IP-10 ) and Mig , which bind specifically to CXCR3 expressed on type 1 T cells 24 , , are significantly elevated in the plasma of scrub typhus patients 48 ., Initial inflammation at the infection site might be initiated by the infiltration of neutrophils 49 , followed by the recruitment of monocytes and lymphocytes by several chemokines such as MIP-1 α/β , RANTES , and MCP-1 , which are expressed by infected endothelial cells and macrophages upon infection 10 , 46 , 50 ., Systemic elevation of IFN-γ might further enhance the expression of IP-10 and Mig , which lead to local exudation of type 1 T cells to inflamed tissues ., Another interesting finding is the remarkable reduction of CD4+ Treg cells in the peripheral blood of scrub typhus patients ( Figure 4 ) ., A role for Treg cells in O . tsutsugamushi infection has never been previously examined; yet evidence from many chronic infectious diseases suggest that Treg cells represent a double-edged sword , limiting both the magnitude of effector responses and the collateral tissue damage caused by vigorous antimicrobial immune responses 26 ., Even though the marked reduction of Treg cells in the peripheral blood of scrub typhus patients could be due to the migration and accumulation of suppressive cells to the inflamed tissues as observed in other chronic infections 26 , the inverse correlation between a systemic decrease in Treg cells and an increase in proliferating CD8+ T cells ( Figure 2B and 4B ) along with increased CTL activity 48 strongly suggest a functional impairment of Treg cells in scrub typhus patients ., Even though further work is required to clearly define the functional status of Treg cells in specific tissues , it could be assumed that the systemic reduction of Treg cells upon O . tsutsugamushi infection may significantly contribute to tissue damage by deregulating the proliferation and activation of CD8+ T cells ., The proliferation of CD8+ EM subsets ( Figure 5 ) , which may include both activated CTLs and effector memory cells , and a higher frequency of activated CD8+ T cells with PD-1high and IL-7Rαlow phenotypes in the patients ( Figure 6 ) further support this hypothesis ., The significant increase of activated CTLs might play a critical role in anti-Orientia immunity as reported in a mouse infection model 51 ., One may argue that the proliferation of CD8+ EM subsets could be protective and linked to memory response , but this is contradictory to observations drawn from vaccine studies and recurrent human infection ., As mentioned above , it is well established that CD8+ memory cells developed during the primary infection in the absence of CD4+ T cell help are poorly functional although the effector functions of the primary CTL responses are independent of CD4+ T cell help 52 ., In addition to cytotoxic CD8+ T cells , NK cells have also been suggested to play a role in the pathogenesis of scrub typhus 31 , 53 ., In a mouse model for Rickettsia infection , NK cell activity was significantly increased on days 2–6 of infection and depletion of NK cells enhanced the susceptibility of mice to Rickettsia infection 54 , suggesting NK cells have a significant role in early anti-rickettsial immune responses ., In the current study , however , the frequency of total NK cells in scrub typhus patients did not change significantly ( Figure S5 ) ., Considering that the patients samples were collected when they were symptomatic ( i . e . one to two weeks after initial infection ) , this may be beyond the period of NK cell activities ., Nevertheless , we could detect a substantial decrease in peripheral CD56bright NK cells , a regulatory NK cell subset expressing immunoregulatory cytokines such as IFN-γ and IL-10 depending on stimulation 55 , in the patients ( Figure S1 ) , suggesting that these cells may migrate to inflamed tissues during the symptomatic period ., The local distribution and activation status of NK cell subsets in patients needs to be analyzed further to confirm their role in anti-Orientia immunity .

Introduction, Materials and Methods, Results, Discussion

Scrub typhus , caused by Orientia tsutsugamushi infection , is one of the main causes of febrile illness in the Asia-Pacific region ., Although cell-mediated immunity plays an important role in protection , little is known about the phenotypic changes and dynamics of leukocytes in scrub typhus patients ., To reveal the underlying mechanisms of immunological pathogenesis , we extensively analyzed peripheral blood leukocytes , especially T cells , during acute and convalescent phases of infection in human patients and compared with healthy volunteers ., We observed neutrophilia and CD4+ T lymphopenia in the acute phase of infection , followed by proliferation of CD8+ T cells during the convalescent phase ., Massive T cell apoptosis was detected in the acute phase and preferential increase of CD8+ T cells with activated phenotypes was observed in both acute and convalescent phases , which might be associated or correlated with elevated serum IL-7 and IL-15 ., Interestingly , peripheral Treg cells were significantly down-regulated throughout the disease course ., The remarkable decrease of CD4+ T cells , including Treg cells , during the acute phase of infection may contribute to the loss of immunological memory that are often observed in vaccine studies and recurrent human infection .

Scrub typhus is an acute febrile illness caused by Orientia tsutsugamushi infection ., It has been estimated that one billion people are at risk and one million new cases arise each year in Asian-pacific region ., Despite of aggressive attempts to develop a prophylactic vaccine against scrub typhus during the last several decades , all approaches have failed to generate long lasting immunity ., In addition , little is known about the immunological pathogenesis of scrub typhus ., To understand the pathogenic mechanisms of this infectious disease , we extensively analyzed peripheral leukocytes , especially T cells , in Korean scrub typhus patients and compared with healthy volunteers ., We observed neutrophilia and CD4+/T lymphopenia in the acute phase of infection , followed by proliferation of CD8+ T cells during the convalescent phase ., Massive T cell apoptosis was detected in the acute phase and a preferential increase of CD8+ T cells with activated phenotypes was observed in both acute and convalescent phases ., The remarkable decrease of CD4+ T cells , including Treg cells , during the acute phase of infection may contribute to the loss of immunological memory and generate helpless but unregulated cytotoxic T cell responses observed in vaccine studies and recurrent human infection .

medicine, bacterial diseases, infectious diseases, scrub typhus

journal.pbio.2002266

Comparative genomics of the tardigrades Hypsibius dujardini and Ramazzottius varieornatus

The superphylum Ecdysozoa emerged in the Precambrian , and ecdysozoans not only dominated the early Cambrian explosion but also are dominant ( in terms of species , individuals , and biomass ) today ., The relationships of the 8 phyla within Ecdysozoa remain contentious , with morphological assessments , developmental analyses , and molecular phylogenetics yielding conflicting signals 1–3 ., It has generally been accepted that Arthropoda , Onychophora ( velvet worms ) , and Tardigrada ( water bears or moss piglets ) form a monophylum , Panarthropoda 2 , and that Nematoda ( roundworms ) are closely allied to Nematomorpha ( horsehair worms ) and distinct from Panarthropoda ., However , molecular phylogenies have frequently placed representatives of Tardigrada as sisters to Nematoda 1 , 3 , invalidating Panarthropoda and challenging models of the evolution of complex morphological traits such as segmentation , serially repeated lateral appendages , the triradiate pharynx , and a tripartite central nervous system 4 , 5 ., The key taxon in these disagreements is phylum Tardigrada ., Nearly 1 , 200 species of tardigrades have been described 6 ., All are members of the meiofauna—small animals that live in the water film and in interstices between sediment grains 6 ., There are marine , freshwater , and terrestrial species ., Many species of terrestrial tardigrades are cryptobiotic: they have the ability to survive extreme environmental challenges by entering a dormant state 7 ., Common to these resistances is an ability to lose or exclude the bulk of body water , and anhydrobiotic tardigrades have been shown to have tolerance to high and low temperatures ( including freezing ) , organic solvents , X- and gamma-rays , high pressure , and the vacuum of space 8–15 ., The physiology of anhydrobiosis in tardigrades has been explored extensively , but little is currently known about its molecular bases 16 , 17 ., Many other animals have cryptobiotic abilities , including some nematodes and arthropods 18 , and comparison of the mechanisms in different independent acquisitions of this trait will reveal underlying common mechanisms ., Central to the development of tractable experimental models for cryptobiosis is the generation of high-quality genomic resources ., Genome assemblies of 2 tardigrades , H . dujardini 19–21 and R . varieornatus 22 , both in the family Hypsibiidae , have been published ., H . dujardini is a limnoterrestrial tardigrade that is easy to culture 23 , while R . varieornatus is a terrestrial tardigrade and highly tolerant of environmental extremes 24 ., An experimental toolkit for H . dujardini , including RNA interference ( RNAi ) and in situ hybridization , is being developed 25 ., H . dujardini is poorly cryptobiotic compared to R . varieornatus ., H . dujardini requires 48 h of preconditioning at 85% relative humidity ( RH ) and a further 24 h in 30% RH 23 to enter cryptobiosis with high survival , while R . varieornatus can form a tun ( the cryptobiotic form ) within 30 min at 30% RH 26 ., Several anhydrobiosis-related genes have been identified in Tardigrada ., Catalases , superoxide dismutases ( SODs ) , and glutathione reductases may protect against oxidative stress 27 , and chaperones , such as heat shock protein 70 ( HSP70 ) 28–30 , may act to protect proteins from the denaturing effects of water loss 16 , 31 , 32 ., Additionally , several tardigrade-specific gene families have been implicated in anhydrobiosis , based on their expression patterns ., Cytosolic abundant heat soluble ( CAHS ) , secretory abundant heat soluble ( SAHS ) , late embryogenesis abundant protein mitochondrial ( RvLEAM ) , mitochondrial abundant heat soluble protein ( MAHS ) , and damage suppressor ( Dsup ) gene families have been implicated in R . varieornatus extremotolerance 22 , 33 , 34 ., These gene families were named by their subcellular location or function , and expression of MAHS and Dsup in human tissue culture cell lines resulted in elevated levels of tolerance against osmotic stress and X-ray irradiation ( approximately 4 Gy ) ., Surprisingly , analyses of the R . varieornatus genome showed extensive gene loss in the peroxisome pathway and in stress signaling pathways , suggesting that this species is compromised in terms of reactive oxygen resistance and repair of cellular damage 22 ., While loss of these pathways would be lethal for a normal organism , loss of these resistance pathways may be associated with anhydrobiosis ., Desiccation in some taxa induces the production of anhydroprotectants , small molecules that likely replace cellular water to stabilize cellular machinery ., Trehalose , a disaccharide shown to contribute to anhydrobiosis in midges 35 , 36 , nematodes 37 , and artemia 38 , is not present in the tardigrade Milnesium tardigradum 31 ., Coupled with the ability of R . varieornatus to enter anhydrobiosis rapidly ( i . e . , without the need for extensive preparatory biosynthesis ) , this suggests that tardigrade anhydrobiosis does not rely on induced synthesis of protectants ., Entry into anhydrobiosis in H . dujardini does require active transcription during preconditioning , suggesting the activation of a genetic program to regulate physiology ., Inhibition of PP1/2A , an positive regulator of the FOXO transcription factor that induces antioxidative stress pathways , led to high lethality in H . dujardini during anhydrobiosis induction 23 ., As R . varieornatus does not require preconditioning , systems critical to anhydrobiosis in R . varieornatus are likely to be constitutively expressed ., H . dujardini and R . varieornatus are relatively closely related ( both are members of Hypsibiidae ) , and both have available genome sequences ., The R . varieornatus genome has high contiguity and scores highly in all metrics of gene completeness 22 ., For H . dujardini , 3 assemblies have been published ., One has low contiguity ( N50 length of 17 kb ) and contains a high proportion of contaminating nontardigrade sequence , including approximately 40 Mb of bacterial sequence , and spans 212 Mb 19 ., The other 2 assemblies , both at approximately 130 Mb 20 , 21 , eliminated most contamination , but contained uncollapsed haploid segments because of unrecognized heterozygosity ., The initial low-quality H . dujardini genome was published alongside a claim of extensive horizontal gene transfer ( HGT ) from bacteria and other taxa into the tardigrade genome and a suggestion that HGT might have contributed to the evolution of cryptobiosis 19 ., The extensive HGT claim has been robustly challenged 20 , 21 , 39–41 , but the debate as to the contribution of HGT to cryptobiosis remains open ., The genomes of these species could be exploited for understanding the mechanisms of rapid-desiccation versus slow-desiccation strategies in tardigrades , the importance of HGT , and the resolution of the deep structure of the Ecdysozoa ., However , the available genomes are not of equivalent quality ., We have generated a high-quality genome assembly for H . dujardini , from an array of data including single-tardigrade sequencing 42 and long , single-molecule reads , and using a heterozygosity-aware assembly method 43 , 44 ., Gene finding and annotation with extensive RNA sequencing ( RNA-Seq ) data allowed us to predict a robust gene set ., While most ( 60% ) of the genes of H . dujardini had direct orthologues in an improved gene prediction for R . varieornatus , levels of synteny were very low ., We identified an unremarkable proportion of potential HGTs ., H . dujardini showed losses of peroxisome and stress signaling pathways , as described in R . varieornatus , as well as additional unique losses ., Transcriptomic analysis of anhydrobiosis entry detected higher levels of regulation in H . dujardini compared to R . varieornatus , as predicted , including regulation of genes with antistress and apoptosis functions ., Using single-copy orthologues , we reanalyzed the position of Tardigrada within Ecdysozoa and found strong support for a Tardigrade+Nematode clade , even when data from transcriptomes of a nematomorph , onychophorans , and other ecdysozoan phyla were included ., However , rare genomic changes tended to support the traditional Panarthropoda ., We discuss our findings in the context of how best to improve genomics of neglected species , the biology of anhydrobiosis , and conflicting models of ecdysozoan relationships ., The genome size of H . dujardini has been independently estimated by densitometry to be approximately 100 Mb 20 , 45 , but the spans of existing assemblies exceed this , because of contamination with bacterial reads and uncollapsed heterozygosity of approximately 30%–60% of the span estimated from k-mer distributions ., We generated new sequencing data ( S1 Table ) for H . dujardini ., Tardigrades , originally purchased in mixed cultures from Sciento , were cultured with a single algal food source ., Illumina short reads were generated from a single , cleaned tardigrade 42 , and Pacific Biosciences ( PacBio ) long single-molecule reads from DNA from a bulk , cleaned tardigrade population ( approximately 900 , 000 animals ) ., We employed an assembly strategy that eliminated evident bacterial contamination 46 and eliminated residual heterozygosity ., Our initial Platanus 44 genome assembly had a span of 99 . 3 Mb in 1 , 533 contigs , with an N50 length of 250 kb ., Further scaffolding and gap filling 47 with PacBio reads and a Falcon 43 assembly of the PacBio reads produced a 104 Mb assembly in only 1 , 421 scaffolds and an N50 length of 342 kb and N90 count of 343 ( Table 1 ) ., In comparison with previous assemblies , this assembly has improved contiguity and improved coverage of complete core eukaryotic genes 48 , 49 ., Read coverage was relatively uniform throughout the genome ( S1 Fig , S2 Table ) , with only a few short regions , likely repeats , with high coverage ., We identified 29 . 6 Mb ( 28 . 5% ) of the H . dujardini genome as being repetitive ( S3 Table ) ., Simple repeats covered 5 . 2% of the genome , with a longest repeat unit of 8 , 943 bp ., Seven of the 8 longest repeats were of the same repeat unit ( GATGGGTTTT ) n , were found exclusively at 9 scaffold ends , and may correspond to telomeric sequence ( S4 Table ) ., The other long repeat was a simple repeat of ( CAGA ) n and its complementary sequence ( GTCT ) n , and spanned 3 . 2 Mb ( 3% of the genome , longest unit 5 , 208 bp ) ., We identified eighty-one 5 . 8S rRNA , two 18S rRNA , and three 28S rRNA loci with RNAmmer 50 ., Scaffold0021 contains both 18S and 28S loci , and it is likely that multiple copies of the ribosomal RNA repeat locus have been collapsed in this scaffold , as it has very high read coverage ( approximately 5 , 400-fold , compared to approximately 113-fold overall , suggesting approximately 48 copies ) ., tRNAs for each amino acid were found ( S2 Fig ) 51 ., Analysis of microRNA sequencing ( miRNA-Seq ) data with miRDeep 52 predicted 507 mature miRNA loci ( S1 Data ) , of which 185 showed similarity with sequences in miRbase 53 ., We generated RNA-Seq data from active and anhydrobiotic ( “tun” stage ) tardigrades and developmental stages of H . dujardini ( S1 Table ) ., Gene finding using BRAKER 54 predicted 19 , 901 genes , with 914 isoforms ( version nHd3 . 0 ) ., This set of gene models had higher completeness and lower duplication scores compared to those predicted with MAKER 55 , which uses RNA-Seq and protein evidence ( Predicted proteome based BRAKER: 90 . 7% MAKER: 77 . 9% , genome based BRAKER: 86 . 3% , Metazoan lineage used ) ., Minor manual editing of this gene set to break approximately 40 fused genes generated version nHd3 . 1 ., These coding sequence predictions lacked 5′ and 3′ untranslated regions ., Mapping of RNA-Seq data to the predicted coding transcriptome showed an average mapping proportion of approximately 50%–70% , but the mapping proportion was over 95% against the genome ( S5 Table ) ., A similar mapping pattern for RNA-Seq data to predicted transcriptome was also observed for R . varieornatus ., Over 70% of the H . dujardini transcripts assembled with Trinity 56 mapped to the predicted transcriptome , and a larger proportion to the genome ( S6 Table ) ., RNA-Seq reads that are not represented in the predicted coding transcriptome likely derived from UTR regions , unspliced introns , or promiscuous transcription ., We inferred functional and similarity annotations for approximately 50% of the predicted proteome ( Table 2 ) ., The H . dujardini nHd . 3 . 0 genome assembly is available on a dedicated ENSEMBL 57 server , http://ensembl . tardigrades . org , where it can be compared with previous assemblies of H . dujardini and with the R . varieornatus assembly ., The ENSEMBL database interface includes an application-programming interface ( API ) for scripted querying 58 ., All data files ( including supplementary data files and other analyses ) are available from http://download . tardigrades . org , and a dedicated Basic Local Alignment Search Tool ( BLAST ) server is available at http://blast . tardigrades . org ., All raw data files have been deposited in International Nucleotide Sequence Database Collaboration ( INSDC ) databases ( National Center for Biotechnology Information NCBI and Sequence Read Archive SRA , S1 Table ) , and the assembly ( nHd3 . 1 ) has been submitted to NCBI under the accession ID MTYJ00000000 ., We compared this high-quality assembly of H . dujardini to that of R . varieornatus 22 ., In initial comparisons , we noted that R . varieornatus had many single-exon loci that had no H . dujardini ( or other ) homologues ., Reasoning that this might be a technical artifact , we updated gene models for R . varieornatus using BRAKER 54 with additional comprehensive RNA-Seq of developmental stages ( S1 Table ) ., The new prediction included 13 , 917 protein-coding genes ( 612 isoforms ) ., This lower gene count compared to the original ( 19 , 521 genes ) was largely due to a reduction in single-exon genes with no transcript support ( from 5 , 626 in version 1 to 1 , 777 in the current annotation ) ., Most ( 12 , 752 , 90% ) of the BRAKER-predicted genes were also found in the original set ., In both species , some predicted genes may derive from transposons , as 2 , 474 H . dujardini and 626 R . varieornatus proteins matched Dfam domains 59 ., While several of these putatively transposon-derived predictions have a Swiss-Prot 60 homologue ( H . dujardini: 915 , 37%; R . varieornatus: 274 , 44% ) , most had very low expression levels ., One striking difference between the 2 species was in genome size , as represented by assembly span: the R . varieornatus assembly had a span of 55 Mb , half that of H . dujardini ( Table 2 ) ., This difference could have arisen through whole genome duplication , segmental duplication , or more piecemeal processes of genome expansion or contraction ., H . dujardini had 5 , 984 more predicted genes than R . varieornatus ., These spanned approximately 23 Mb and accounted for about half of the additional span ., There was no difference in number of exons per gene between orthologues or in the whole predicted gene set ., However , comparing orthologues , the intron span per gene in H . dujardini was on average twice that in R . varieornatus ( Fig 1B ) , and gene length ( measured as start codon to stop codon in coding exons ) was approximately 1 . 3-fold greater in H . dujardini ( Table 2 , S3 Fig ) ., There was more intergenic noncoding DNA in H . dujardini , largely explained by an increase in the repeat content ( 28 . 6 Mb in H . dujardini versus 11 . 1 Mb in R . varieornatus ) ., Whole genome alignments of R . varieornatus and H . dujardini using Murasaki 61 revealed a low level of synteny but evidence for conserved linkage at the genome scale , with little conservation of gene order beyond a few loci ., For example , comparison of R . varieornatus Scaffold002 of with H . dujardini scaffold0001 showed linkage , with many orthologous ( genome-wide bidirectional best BLAST hit ) loci across approximately 1 . 7 Mb of the H . dujardini genome ( Fig 1A ) ., A high proportion of orthologues of genes located on the same scaffold in H . dujardini were also in one scaffold in R . varieornatus , implying that intrachromosomal rearrangement may be the reason for the low level of synteny ( Fig 1C ) ., We defined protein families in the H . dujardini and new R . varieornatus predicted proteomes , along with a selection of other ecdysozoan and other animal predicted proteomes ( S7 Table ) , using OrthoFinder 62 , including predicted proteomes from fully sequenced genomes or predicted proteomes from the fully sequenced genomes and ( likely partial ) transcriptomes in two independent analyses ., Using these protein families , we identified orthologues for phylogenetic analysis and explored patterns of gene family expansion and contraction , using KinFin 63 ., We identified 144 , 610 protein families in the analysis of 29 fully sequenced genome species ., Of these families , 87 . 9% were species specific ( with singletons accounting for 11 . 6% of amino acid span , and multi-protein clusters accounting for 1 . 2% of span ) ., While only 12 . 1% of clusters contained members from ≥2 predicted proteomes , they accounted for the majority of the amino acid span ( 87 . 2% ) ., H . dujardini had more species-specific genes than R . varieornatus and had more duplicate genes in gene families shared with R . varieornatus ( Table 2 ) ., H . dujardini also had more genes shared with nontardigrade outgroups , suggesting loss in R . varieornatus ., Many families had more members in tardigrades compared to other taxa , and 3 had fewer members ( 115 had uncorrected Mann-Whitney U-test probabilities <0 . 01 , but none had differential presence after Bonferroni correction ) ., In 9 of the families with tardigrade overrepresentation , tardigrades had more than four times as many members as the average of the other species ( S2 Data ) ., There were 1 , 486 clusters composed solely of proteins predicted from the 2 tardigrade genomes ., Of those , 365 ( 24 . 56% ) had a congruent domain architecture in both species , including 53 peptidase clusters , 27 kinase clusters , and 29 clusters associated with signaling function , including 18 G-protein coupled receptors ( see S3 Data ) ., While these annotations are commonly found in clade-specific families , suggesting that innovation in these classes of function is a general feature in metazoan evolution , of particular interest was innovation in the Wnt signaling pathway ., Tardigrade-unique clusters included Wnt , Frizzled , and chibby proteins ., Of relevance to cryptobiosis , 21 clusters with domain annotation relevant to genome repair and maintenance were synapomorphic for Tardigrada , including molecular chaperones ( 2 ) , histone/chromatin maintenance proteins ( 11 ) , genome repair systems ( 4 ) , nucleases ( 2 ) , and chromosome cohesion components ( 2 ) ( see below ) ., HGT is an interesting but contested phenomenon in animals ., Many newly sequenced genomes have been reported to have relatively high levels of HGT , and genomes subject to intense curation efforts tend to have lower HGT estimates ., We performed ab initio gene finding on the genomes of the model species Caenorhabditis elegans and Drosophila melanogaster with Augustus 64 and used the HGT index approach 65 , which simply classifies loci based on the ratio of their best BLAST scores to ingroup and potential donor taxon databases , to identify candidates ., Compared with their mature annotations , we found elevated proportions of putative HGTs in both species ( C . elegans: 2 . 09% of all genes; D . melanogaster: 4 . 67% ) ., We observed similarly elevated rates of putative HGT loci , as assessed by the HGT index , in gene sets generated by ab initio gene finding in additional arthropod and nematode genomes compared to their mature annotation ( Fig 2A , S8 Table ) ., Thus , the numbers of HGT events found in the genomes of H . dujardini and R . varieornatus are likely to have been overestimated in these initial , uncurated gene predictions , even after sequence contamination has been removed , as seen in the H . dujardini assembly of Boothby et al . 41 ., Using the HGT index approach , we identified 463 genes as potential HGT candidates in H . dujardini ( S4 Data ) ., Using Diamond BLASTX 66 instead of standard BLASTX 67 , 68 made only a minor difference in the number of potential HGT events predicted ( 446 genes ) ., We sifted the initial 463 H . dujardini candidates through a series of biological filters ., A true HGT locus will show affinity with its source taxon when analyzed phylogenetically ( a more sensitive test than simple BLAST score ratio ) ., Four-fifths of these loci ( 357 ) were confirmed as HGT events by Randomized Axelerated Maximum Likelihood ( RAxML ) 69 analysis of aligned sequences ( Fig 2B ) ., For 13 candidates , there were not enough homologues found in public databases to estimate phylogenies ., HGT genes are expected to be incorporated into the host genome and to persist through evolutionary time ., Only 164 of the RAxML-confirmed H . dujardini candidates had homologues in R . varieornatus , indicating phyletic perdurance ( S2 Data ) ., HGT loci will acquire gene structure and expression characteristics of their host metazoan genome ., We identified expression at greater than 1 transcript per million ( TPM ) in any library for 338 HGT candidates ., While metazoan genes usually contain spliceosomal introns , and 367 of the candidate HGT gene models included introns , we regard this a lower-quality validation criterion , as gene-finding algorithms are programmed to identify introns ., Therefore , our highest-credibility current estimate for HGT into the genome of H . dujardini is 133 genes ( 0 . 7% of all genes ) , with a less-credible set , showing conservation , expression , and/or phylogenetic validation of 357 ( 1 . 8% ) and an upper bound of 463 ( 2 . 3% ) ., This is congruent with estimates of 1 . 6% HGT candidates ( out of 13 , 917 genes ) for R . varieornatus 22 ., The putative HGT loci tended to be clustered in the tardigrade genomes , with many gene neighbors of HGT loci also predicted to be HGTs ( S4 Fig ) ., We found 58 clusters of HGT loci in H . dujardini and 14 in R . varieornatus ( S6 Data ) ., The largest clusters included up to 6 genes from the same gene family and may have arisen through tandem duplication ., These tandem duplication clusters included intradiol ring-cleavage dioxygenases , uridine diphosphate ( UDP ) glycosyltransferases , and alpha/beta fold hydrolases ., Several clusters of UDP glycosyltransferases with signatures of HGT from plants were identified in the H . dujardini genome , 1 of which included 6 UDP glycosyltransferases within 12 genes ( loci between the genes bHd03905 and bHd03916 ) ., H . dujardini had 40 UDP glycosyltransferase genes , 29 of which were classified as glucuronosyltransferase ( UGT , K00699 ) by Kyoto Encyclopedia of Genes and Genomes ( KEGG ) ORTHOLOG mapping with the KEGG Automatic Annotation Server ( KAAS ) 70 , and of these 27 were HGT candidates ., While UGT can function in a number of pathways , we found that the whole ascorbate synthesis pathway , in which UGT metabolizes UDP-D-glucuronate to D-Glucuronate , has been acquired by HGT in H . dujardini ., R . varieornatus has only acquired L-gulonolactone oxidase ( S5 Fig ) ., Gluconolactonase and L-gluonolactone oxidase were consistently expressed at low levels ( approximately 10–30 TPM ) , but L-ascorbate degradation enzyme L-ascorbate oxidase was not expressed ( TPM < 1 ) ., We explored the H . dujardini proteome and the reannotated R . varieornatus proteome for loci implicated in anhydrobiosis ., In the new R . varieornatus proteome , we found 16 CAHS loci and 13 SAHS loci and 1 copy each of MAHS , RvLEAM , and Dsup ., In H . dujardini , we identified 12 CAHS loci , 10 SAHS loci , and single members of the RvLEAM and MAHS families ( S9 Table ) ., Direct interrogation of the H . dujardini genome with R . varieornatus loci identified an additional possible CAHS-like locus and an additional SAHS-like locus ., We found no evidence for a H . dujardini homologue of Dsup ., Phylogenetic analyses revealed a unique duplication of CAHS3 in R . varieornatus ., No SAHS2 orthologue was found in H . dujardini ( S6 Fig ) , and most of the H . dujardini SAHS loci belonged to a species-specific expansion that was orthologous to a single R . varieornatus SAHS locus , RvSAHS13 ., SAHS1-like genes in H . dujardini and SAHS1- and SAHS2-like genes in R . varieornatus were locally duplicated , forming SAHS clusters on single scaffolds ., R . varieornatus was reported to have undergone extensive gene loss in the stress-responsive transducer of mechanistic target of rapamycin ( mTOR ) pathway and in the peroxisome pathway , which generates H2O2 during the beta-oxidation of fatty lipids ., H . dujardini was similarly compromised ( Fig 3A ) ., We identified additional gene losses in the peroxisome pathway in H . dujardini , as peroxisome proteins PEK5 , PEK10 , and PEK12 , while present in R . varieornatus , were not found in H . dujardini ( TBLASTN search against genome with an E-value threshold of 1E-3 ) ., To identify gene functions associated with anhydrobiosis , we explored differential gene expression in fully hydrated and postdesiccation samples from both species ., We compared single individual RNA-Seq of H . dujardini undergoing anhydrobiosis 42 with new data for R . varieornatus induced to enter anhydrobiosis in 2 ways: slow desiccation ( approximately 24 h ) and fast desiccation ( approximately 30 min ) ., Successful anhydrobiosis was assumed when >90% of the samples prepared in the same chamber recovered after rehydration ., Many more genes were differentially up-regulated by entry into anhydrobiosis in H . dujardini ( 1 , 422 genes , 7 . 1% ) than in R . varieornatus ( fast desiccation: 64 genes , 0 . 5%; slow desiccation: 307 genes , 2 . 2% ) ( S6 Data ) ., The fold change distribution of the whole transcriptome of H . dujardini ( mean 8 . 33 , median 0 . 91 ± 69 . 90 SD ) was significantly broader than those of both fast ( 0 . 67 , 0 . 48 ± 2 . 25 ) and slow ( 0 . 77 , 0 . 65 ± 0 . 79 ) desiccation R . varieornatus ( U-test , p-value < 0 . 001 ) ( Fig 3B ) ., For the loci differentially expressed in anhydrobiosis ( S7 Data ) , we investigated their membership of gene families with elevated numbers in tardigrades and functional annotations associated with anhydrobiosis ., Proteins with functions related to protection from oxidants , such as SOD and peroxiredoxin , were found to have been extensively duplicated in tardigrades ., In addition , the mitochondrial chaperone ( BSC1 ) , osmotic stress-related transcription factor NFAT5 , and apoptosis related-gene poly ( ADP-ribose ) polymerase ( PARP ) families were expanded in tardigrades ., Chaperones were extensively expanded in H . dujardini ( HSP70 , DnaK , and DnaJ subfamily C-5 , C-13 , and B-12 ) , and the DnaJ subfamily B3 , B-8 was expanded in R . varieornatus ., In H . dujardini , we found 5 copies of DNA repair endonuclease XPF , which functions in the nucleotide-excision repair pathway , and , in R . varieornatus , 4 copies of the double-stranded break repair protein MRE11 ( as reported previously 22 ) and additional copies of DNA ligase 4 , from the nonhomologous end-joining pathway ., In both R . varieornatus 22 and H . dujardini , some of the genes with anhydrobiosis-related function appear to have been acquired through HGT ., All copies of catalase were high-confidence HGTs ( S5 Data ) , and 1 copy was differentially expressed during H . dujardini anhydrobiosis ( expression rises from 0 TPM to 27 . 5 TPM during slow dehydration in H . dujardini ) ., R . varieornatus had 11 trehalase loci ( 9 trehalases and 2 acid trehalase-like proteins ) ., While H . dujardini did not have an orthologue of trehalose-6-phosphatase synthase ( TPS ) , a gene required for trehalose synthesis , R . varieornatus had a HGT-derived TPS ( S5 Fig ) ., Previous studies in M . tardigradum have shown that trehalose does not accumulate during anhydrobiosis , and this is supported by the low expression of the R . varieornatus TPS gene ( 10–20 TPM in active and tun states ) ., We note that the R . varieornatus TPS had the highest similarity to TPS from bacterial species in Bacteriodetes , including Chitinophaga , which was one of the contaminating organisms in the Boothby et al . assembly 40 ., The R . varieornatus locus contains spliceosomal introns that do not compromise the TPS protein sequence and is surrounded by metazoan-affinity loci ., The ascorbate synthesis pathway appears to have been acquired through HGT in H . dujardini , and a horizontally acquired L-gulonolactone oxidase was identified in R . varieornatus ( S5 Fig ) ., Several protection-related genes were differentially expressed in anhydrobiotic H . dujardini , including CAHS ( 8 loci of 15 ) , SAHS ( 2 of 10 ) , RvLEAM ( 1 of 1 ) , and MAHS ( 1 of 1 ) ., Loci involved in reactive oxygen protection ( 5 SOD genes , 6 glutathione-S transferase genes , a catalase gene , and a LEA gene ) were up-regulated under desiccation ., Interestingly , 2 trehalase loci were up-regulated , even though we were unable to identify any TPS loci in H . dujardini ., We also identified differentially expressed transcription factors that may regulate anhydrobiotic responses ., Two calcium-signaling factors , calmodulin ( CaM ) and a cyclic nucleotide-gated channel ( CNG-3 ) , were both up-regulated , which may drive cAMP synthesis through adenylate cyclase ., Although R . varieornatus is capable of rapid anhydrobiosis induction , complete desiccation is unlikely to be as rapid in natural environments , and regulation of gene expression under slow desiccation might reflect a more likely scenario ., Fitting this expectation , 5 CAHS loci and a single SAHS locus were up-regulated after slow desiccation , but none were differentially expressed following rapid desiccation ., Most R . varieornatus CAHS and SAHS orthologues had high expression in the active state , several over 1 , 000 TPM ., In contrast , H . dujardini CAHS and SAHS orthologues had low resting expression ( median 0 . 7 TPM ) and were up-regulated ( median 1 , 916 . 8 TPM ) on anhydrobiosis induction ., Aquaporins contribute to transportation of water molecules into cells and could be involved in anhydrobiosis 71 ., Aquaporin-10 was highly expressed in R . varieornatus and differentially expressed in anhydrobiotic H . dujardini ., M . tardigradum has at least 10 aquaporin loci 72 , H . dujardini has 11 , and R . varieornatus has 10 ., The contributions to anhydrobiosis of additional genes identified as up-regulated ( including cytochrome P450 , several solute carrier families , and apolipoproteins ) are unknown ., Some genes differentially expressed in both H . dujardini and R . varieornatus slow-desiccation anhydrobiosis were homologous ( S9 Data ) ., Of the 1 , 422 differentially expressed genes from H . dujardini , 121 genes were members of 70 protein families that also contained 115 R . varieornatus differentially expressed genes ., These included CAHS , SAHS , glutathione-S transferase , and SOD gene families , but in each case H . dujardini had more differentially expressed copies than R . varieornatus ., Other differentially expressed gene families were annotated as metalloproteinases , calcium-binding receptors , and G-protein coupled receptors , suggesting that these functions may participate in cellular signaling during induction of anhydrobiosis ., Many more ( 887 ) gene families included members that were up-regulated by anhydrobiosis in H . dujardini but unaffected by desiccation in R . varieornatus ., These gene families included 1 , 879 R . varieornatus genes; some ( 154 ) were highly expressed in the active state ( TPM > 100 ) ., In addition to gene loss , we predicted

Introduction, Results, Discussion, Methods

Tardigrada , a phylum of meiofaunal organisms , have been at the center of discussions of the evolution of Metazoa , the biology of survival in extreme environments , and the role of horizontal gene transfer in animal evolution ., Tardigrada are placed as sisters to Arthropoda and Onychophora ( velvet worms ) in the superphylum Panarthropoda by morphological analyses , but many molecular phylogenies fail to recover this relationship ., This tension between molecular and morphological understanding may be very revealing of the mode and patterns of evolution of major groups ., Limnoterrestrial tardigrades display extreme cryptobiotic abilities , including anhydrobiosis and cryobiosis , as do bdelloid rotifers , nematodes , and other animals of the water film ., These extremophile behaviors challenge understanding of normal , aqueous physiology: how does a multicellular organism avoid lethal cellular collapse in the absence of liquid water ?, Meiofaunal species have been reported to have elevated levels of horizontal gene transfer ( HGT ) events , but how important this is in evolution , and particularly in the evolution of extremophile physiology , is unclear ., To address these questions , we resequenced and reassembled the genome of H . dujardini , a limnoterrestrial tardigrade that can undergo anhydrobiosis only after extensive pre-exposure to drying conditions , and compared it to the genome of R . varieornatus , a related species with tolerance to rapid desiccation ., The 2 species had contrasting gene expression responses to anhydrobiosis , with major transcriptional change in H . dujardini but limited regulation in R . varieornatus ., We identified few horizontally transferred genes , but some of these were shown to be involved in entry into anhydrobiosis ., Whole-genome molecular phylogenies supported a Tardigrada+Nematoda relationship over Tardigrada+Arthropoda , but rare genomic changes tended to support Tardigrada+Arthropoda .

Tardigrades are justly famous for their abilities to withstand environmental extremes ., Many freshwater and terrestrial species can undergo anhydrobiosis—life without water—and thereby withstand desiccation , freezing , and other insults ., We explored the comparative biology of anhydrobiosis in 2 species of tardigrade that differ in the mechanisms they use to enter anhydrobiosis ., Using newly assembled and improved genomes , we find that Ramazzottius varieornatus , a species that can withstand rapid desiccation , differs from Hypsibius dujardini , a species that requires extended preconditioning , in not showing a major transcriptional response to anhydrobiosis induction ., We identified a number of genetic systems in the tardigrades that likely play conserved , central roles in anhydrobiosis as well as species-unique components ., Compared to previous estimates , our improved genomes show much reduced levels of horizontal gene transfer into tardigrade genomes , but some of the identified horizontal gene transfer ( HGT ) genes appear to be involved in anhydrobiosis ., Using the improved genomes , we explored the evolutionary relationships of tardigrades and other molting animals , particularly nematodes and arthropods ., We identified conflicting signals between sequence-based analyses , which found a relationship between tardigrades and nematodes , and analyses based on rare genomic changes , which tended to support the traditional tardigrade-arthropod link .

taxonomy, horizontal gene transfer, invertebrates, ecology and environmental sciences, computational biology, extremophiles, invertebrate genomics, animals, gene transfer, genomic databases, phylogenetics, data management, phylogenetic analysis, genome analysis, research and analysis methods, computer and information sciences, biological databases, evolutionary systematics, animal genomics, genetic loci, arthropoda, database and informatics methods, genetics, nematoda, biology and life sciences, genomics, evolutionary biology, evolutionary processes, organisms

journal.pcbi.1001054

Using Transcription Modules to Identify Expression Clusters Perturbed in Williams-Beuren Syndrome

Williams-Beuren Syndrome ( WBS; OMIM #194050 ) is a de novo neurodevelopmental disorder occurring in approximately 1/10000 births ., WBS is characterized by mental retardation , with a unique cognitive and personality profile ., Clinical features include supravalvular aortic stenosis ( SVAS ) , connective tissue anomalies , distinctive facial features ( elfin face ) , short stature , hypertension , infantile hypercalcemia , dental , kidney and thyroid abnormalities , premature ageing of the skin , elevated body fat percentage , impaired glucose tolerance and silent diabetes ., The cognitive hallmark of the condition is a striking contrast between a relative strength in auditory memory and language abilities , and a profound impairment in visuospatial construction ., WBS individuals are hypersensitive to sound , with strong emotional responses to music , either positive or negative , and some individuals display unusual musical skills ., In addition to this hyperacusis , which is thought to be due to the absence of acoustic reflexes , WBS individuals may suffer from sensorineural hearing loss as they age ., They are also very sociable , emphatic , loquacious and over-friendly , with a complete lack of fear towards strangers ., Many present an attention deficit disorder with hyperactivity and anxiety 1–9 ., The WBS is associated with a microdeletion within the 7q11 . 23 chromosomal band , which encompasses 28 genes 10–13 ., It is flanked by specific low copy repeats that serve as substrate for non-allelic homologous recombination leading to the deletion 14 ., These rearrangements are facilitated by the paracentric inversion of the region 14 , 15 , as well as the presence of a specific copy number variant 16 ., The most common deletion , occurring in approximately 95% of cases , involves a 1 . 5 megabase ( Mb ) segment , while a larger 1 . 84 Mb deletion is observed in about 1 of 20 cases 14 , 17 ., Larger and smaller atypical deletions have been reported in sporadic cases 18–31 ., While the primary cause of WBS is well-understood , we still know little about the molecular basis of the phenotype ., Only very recently , strains of mice were engineered to carry complementary half-deletions of the region syntenic to the WBS region , which replicate several features of WBS , including abnormal social interaction phenotypes 32 ., Yet , so far the dissection of the phenotype relies mainly on evidence from other mouse models — e . g . single gene knock-out — and atypical deletions in humans ., Findings from these studies suggest some correlations between hemizygosity of certain genes and specific phenotypic features seen in WBS individuals ., For example , the SVAS phenotype was shown to be unequivocally associated with haploinsufficiency of the elastin gene 33–35 ., Furthermore , mouse models hemizygote for some of the orthologs of the WBS deletion most telomerically-mapping genes suggested that these were linked to craniofacial abnormalities ( GTF2I and GTF2IRD1 genes ) 36 , tooth anomalies and visuospatial deficit ( GTF2I , GTF2IRD1 and GTF2IRD2 genes ) 22 , 37 , as well as deficits in motor coordination ( CLIP2 ) 38 ., Likewise , the function of the carbohydrate response element-binding protein ( MLXIPL , a . k . a . ChREBP or WBSCR14 ) in the regulation of the expression of enzymes involved in glucose and lipid metabolism 39-43 suggests that its haploinsufficiency is associated with the higher relative body fat , silent diabetes and/or impaired glucose tolerance found in adult WBS individuals 2 ., We showed in previous work that the vast majority of the genes hemizygous due to the 7q11 . 23 deletion are underexpressed in lymphoblastoid cell lines and fibroblasts derived from patients 44 , consistent with their possible role in some of the WBS phenotypes ., Some of the genes that map to the flank of the microdeletion might also influence the WBS phenotype , as it was recently shown that structural rearrangements affect the relative expression levels of neighboring normal-copy genes ( 44–48 , reviewed in 49 , 50 ) ., To identify which downstream pathways are perturbed in WBS by these two classes of human chromosome 7 ( HSA7 ) genes , we generated genome-wide transcription profiles for primary fibroblasts from eight individuals with WBS and nine sex- and age-matched controls ., We first focus on differentially expressed genes and then on co-expressed gene sets to elucidate the genes and pathways that are dysregulated in WBS and how they may contribute to its clinical phenotypes ., Gene expression in fibroblasts can only provide a partial picture of the gene dysregulation that gives rise to the WBS clinical phenotypes ., Thus , data from other cell types or tissues may provide additional clues as to dysregulated pathways , as well as confirm some of our findings in fibroblasts ., Indeed , comparison with the recently published transcriptome of lymphoblastoid , i . e . EBV-transformed , cell lines from WBS patients 66 revealed a few commonly dysregulated genes ., The expression of 11 common genes was altered with the same sign in both cell types , while for 29 others we observe opposite expression ( Table S11 ) ., Eight of the 11 genes with consistently altered expression were part of 28 dysregulated M1 or M2 modules ( Table S11 ) ., Out of the 72 M1 modules the average gene expression of which is altered in WBS fibroblasts , seven are also changed in the lymphoblastoid cell lines; four modules are altered in the same direction , three modules are opposite in the two studies ., Moreover , 19 of the 23 dysregulated M2 modules are also perturbed in the lymphoblastoid samples , 18 in the same direction ( Table S11 ) , suggesting that the pathways identified in the fibroblasts are disrupted in multiple tissues ., Furthermore , we can surmise that modules consistently regulated in both cell types may represent central pathways influenced by the WBS deletion , while the remaining modules may reflect cell-type specific alterations , which in turn might be important for tissue-specific phenotypes ., We have profiled the transcriptomes of skin fibroblasts from eight WBS patients and nine sex- and age-matched control individuals , and identified a number of transcription modules dysregulated in WBS patient cells ., One caveat of this study lies in the use of isolated cells in vitro that may not reflect all the different tissue-dependent transcriptional changes in vivo that give rise to the complex WBS phenotypes , such as cognitive features or connective tissue anomalies ., Moreover , the samples we consider only allow us to observe the downstream global effects of the primary cause , as opposed to the immediate effect on early development ., However , these cell types are the most readily available samples , and the replication of a subset of the fibroblast dysregulations in lymphoblastoids supports the hypothesis that at least some of these changes appear in multiple cell types as a direct result of the 7q11 . 23 deletion and thus provide clues about pathways that may generally be perturbed in WBS ., Our results reveal a transcriptional network which may contribute to the pathophysiology of WBS ., We propose that many of the WBS phenotypes arise due to the dysregulation of a few key gene products , which influence ( possibly in concert ) “regulatory subnetworks” , leading to specific traits ., Also , disturbances in a process due to one group of genes may trigger compensatory mechanisms in another set , either directly in the cell , or indirectly through intercellular or more systemic effects ., Both our single-gene and modular analyses provide a resource to enable a deeper exploration of the pathophysiology of WBS , which may lead to the discovery of potential novel functional interactions between their products ., Our study further exemplifies how integration of transcription data unrelated to the studied condition can be used to complement annotation-dependent analyses ., Indeed , the modular approach reduces the complexity of the expression data , allowing a more targeted assignment of functional categories to specific sets of co-regulated genes ., Consistently , Turcan et al . recently used a similar methodology to identify groups of genes coherently regulated during cochlear development , which allowed them to pinpoint candidate genes for further study 67 ., It is important to underline that further investigations and more data are needed to distinguish between biologically relevant associations of differentially regulated modules and spurious co-expression signals ., Nevertheless , we think that the information generated by our study ( and made available at http://www . unil . ch/cbg/ISA/Fibroblasts ) provides a testable set of candidate pathways dysregulated in WBS and possibly involved in mediating the wide range of associated phenotypes ., We have obtained the approval of the ethics committees of the University of Lausanne ( reference number Protocol 123/06 ) and of the “Hospices Civils de Lyon” for this project ., All patients provided written informed consent for the collection of samples and subsequent analysis ., Skin fibroblasts of 8 classical WBS and 9 control Caucasian female individuals aged between 3 and 8 years ( see Table S1 for details ) and similar numbers of passages were obtained from the cell culture collections of the Centre de Biotechnologie Cellulaire , CBC Biotec , CRB-Hospices Civils de Lyon , Lyon , France ., The respective presence and absence , as well as the extent of the deletion were ascertained by SybrGreen real-time quantitative PCR as previously described 26 ., Human skin fibroblasts were grown in HAM F-10 , supplemented with 10% fetal bovine serum and 1% antibiotics ( all Invitrogen ) ., Total RNA was prepared using TriZOL Reagent ( Invitrogen ) and RNeasy Mini Columns ( Qiagen ) according to the manufacturers instructions ., The quality of all RNAs was assessed using an Agilent 2100 Bioanalyzer ( Agilent Technologies ) and used as a template for complementary DNA ( cDNA ) synthesis and biotinylated antisense cRNA preparation ., The synthesis of cDNA and cRNA , labeling , hybridization and scanning of the samples were performed as described by Affymetrix ( www . affymetrix . com ) ., The cRNA samples were hybridized to GeneChip Human Genome U133 Plus 2 . 0 arrays ( Affymetrix ) ., The chips were washed , stained and scanned , according to the manufacturers protocol ., The data of the 17 expression arrays produced for this report have been deposited in NCBIs Gene Expression Omnibus ( GEO , http://www . ncbi . nlm . nih . gov/geo/ ) and are accessible through GEO Series accession number GSE16715 ., Expression data analyses were performed using GNU R ( version 2 . 9 . 2 ) 68 and the Bioconductor package ( version 2 . 4 ) 69 ., All R package versions are listed in Table S12 ., Low-level analysis and normalization were done using GCRMA ., For differential expression analysis we filtered the probesets and kept only those present in at least six samples , according to the Affymetrix Present/Absent calls calculated with the affy R package ., To reduce noise , we also removed probesets that do not map to an Entrez gene ., 18 , 429 probesets , mapping to 10 , 570 genes were tested for differential expression , using the moderated t-statistics , as implemented in the limma R package ., In addition to the significant p-value , we required a minimum of 50% change for declaring a gene differentially expressed ., 1 , 114 probesets , corresponding to 868 genes were found differentially expressed at the 5% FDR level , 367 probesets , mapping to 306 genes at the 1% FDR level ., The FDR was controlled using the Benjamini-Hochberg correction 51 ., Gene set enrichment analysis of the WBS hemizygous genes was performed by comparing the mean t-statistics of these genes , for the WBS patients and the control individuals; the reference distribution for this was established by permuting the phenotype labels 10 , 000 times 70 ., Gene Ontology and KEGG Pathway enrichment was calculated via a hypergeometric test , using the eisa and GOstats Bioconductor packages ., The enrichment P-values were corrected using the Benjamini-Hochberg method for the number of categories tested ., A transcription module comprises a subset of genes that are co-expressed in a subset of conditions 56 ., The Iterative Signature Algorithm ( ISA ) 71 is an unsupervised method to identify such modules ., It starts from many random initial sets of genes ( seeds ) that typically converge to a set of potentially overlapping transcription modules ., The ISA assigns a signed score to every gene of the module and every sample of the module ( zero scores imply that the gene or sample is not included in the module ) ., The further the gene/sample score is from zero , the stronger the association between the gene/sample and the rest of the module ., Co-expressed genes of a module have the same sign , whereas opposite signs signal opposite expression ., The scores of the samples are exactly the same as the weighted averages of the expression of the module genes , the weights being the scores of the genes ., Sample scores can be extended to the samples that are not included in the module , by calculating the same weighted average of the module genes for them ., These samples have ( in absolute value ) lower scores than the module samples , by definition ., The extended sample scores can be used to test whether the genes of a module are differentially regulated in some samples ., The aim is to identify dysregulated transcription modules containing genes that are differentially expressed in the cases compared to the control samples ., In the first ISA run , we used skin fibroblast samples from seven experiments from public repositories , as well as collaborators of the AnEUploidy consortium ( the latter can be obtained by contacting the consortium at http://www . aneuploidy . eu/ ) ( Table S4 ) ., For each dataset we downloaded the raw data and normalized them separately with the GCRMA method ., The non-common probesets were omitted and the normalized expression data were merged; the data set included 22 , 277 probesets and 96 samples ., To reduce noise we removed probesets that were called “Present” in less than ten samples , using the standard Affymetrix Present/Absent calls ., We also removed probesets that were not associated with any Entrez gene ., In order to avoid a bias towards genes with multiple probesets we only kept the single probeset with the highest variance for those genes ., The final dataset included 9 , 329 probesets ., We applied the ComBat batch correction algorithm 72 to minimize non-biological variation; we used the “disease status” of the samples as an additional covariate for the correction ( column “disease status” in Table S4 ) ., The additional covariate ensures that we do not remove the signal associated with the different syndromes in the data sets , only the systematic experimental variation ., We ran ISA as implemented in the eisa R package 73 , with gene thresholds 2 , 2 . 2 , … , 4 and sample thresholds 1 , 1 . 2 , … , 2 ., The ISA identified 1 , 094 transcription modules ., For the identification of the dysregulated modules , we used the GCRMA normalized WBS data set ., Probesets that were called “Present” in less than six samples were omitted from the analysis ., We only considered the 7 , 447 probesets that were included both in this filtered WBS data set and the modular study ., 732 modules that contained at least ten genes were tested for dysregulation ., For the dysregulation test we standardized the WBS expression data for every gene separately ., Standardization is an important step , since the test for dysregulation involves the average expression of the module genes ., Specifically , to test a module , we calculated the weighted average expression of its genes , separately for each WBS sample ., The weights were defined by the gene scores of the module ., Then a t-test with unequal variance was performed for the WBS cases against controls ., The t-test P-values were corrected with the Benjamini-Hochberg method ., At the 5% FDR level 72 dysregulated modules were found ., To check the significance of finding 72 dysregulated modules , we permuted the WBS case/control labels 1 , 000 times and tested for dysregulation as before ., These permutations serve as a null-model to estimate how many dysregulated modules could have resulted by chance ., Only 14 permutations yielded at least one dysregulated module ., Within these 14 cases , the mean number of dysregulated modules was 12 . 1 , the median 1 . 5 ., The highest number of dysregulated modules found for a permutation was 58 ., We note that the three permutations that yielded multiple ( false positive ) WBS dysregulated modules had almost correct WBS case/control labels: only one pair was swapped ., Hypergeometric tests were used to calculate the functional enrichment of the 72 dysregulated modules , with Benjamini-Hochberg correction for the number of categories and the number of modules tested ., The significance threshold was chosen as 0 . 05 ., The second modular study ( M2 ) was performed almost identically , but this time the WBS samples were also included in the data set ., The ISA was run on 9 , 460 probesets and 113 samples , using gene thresholds 2 , 2 . 2 , … , 4 and sample thresholds 1 , 1 . 2 , … , 2 ., The ISA found 1 , 035 modules , of which 290 contain at least ten genes and one sample from our study ., These were tested for dysregulation using t-tests for the sample scores of the WBS cases vs . controls , identifying 23 modules that are differentially expressed ., As an additional validation , we permutated the labels of the WBS samples 1 , 000 times; no permutation showed any dysregulated modules ., Enrichment calculation for the dysregulated modules was done the same way as for the M1 modules , using Benjamini-Hochberg multiple testing correction for the number of categories and the number of modules tested , and a significance threshold of 0 . 05 ., We used version 8 . 3 of the STRING database to interrogate the genes that frequently appear in the dysregulated modules ., All network measures were calculated using the igraph R package 74 ., We fitted hierarchical models 60 to the subnetwork of frequent module genes , and also to 1 , 000 randomized networks ., For fitting the hierarchical models , we only considered the largest connected component of the network , consisting of 90 proteins and 203 connections among them ., The randomized networks had the same degree sequence as the original network , and they were produced using Monte-Carlo methods 75 ., The enrichment calculations for the extracellular region genes ( Figure S1 ) were done using hypergeometric tests and the eisa and GOstats R packages ., Only the second level terms in the “Biological process” and “Molecular function” ontologies were tested ., To identify genes commonly dysregulated in cells from WBS patients identified in this study and in 66 , which uses two-color arrays ( GEO accession number GSE18188 ) , we tested the lymphoblastoid samples for differentially expressed genes ., We used the moderated t-statistics and a fold-change threshold of 1 . 5 and applied the Benjamini-Hochberg multiple testing correction method to identify 574 differentially expressed genes ., Forty of these are common with the 868 differentially expressed genes we found in the fibroblast samples ., To test the dysregulation of the fibroblast dysregulated modules in the lymphoblastoid samples , we calculated the weighted mean log fold change of the module genes for each lymphoblastoid array , where the gene scores of the modules were used as weights ., Then we used a t-test to check whether the mean log fold change is significantly above or below zero , followed by the Benjamini-Hochberg multiple testing correction method ., The modules and related details are available at http://www . unil . ch/cbg/ISA/Fibroblasts ., These web pages contain the summary of all M1 and M2 transcription modules and their GO/KEGG enrichment statistics ., An interactive version of Figure 3 is also included; this allows the exploration and annotation of the dysregulated modules , using various criteria ., It is also possible to query the modules that contain a specific gene , or a list of genes ., See the help page of the supplementary material for details ., Additionally , the modules can be visualized interactively with the online version of ExpressionView 76 ., The expression array annotation data were taken from the hgu133a2 . db ( version 2 . 2 . 11 ) and hgu133plus2 . db ( version 2 . 2 . 11 ) Bioconductor packages ., The GO . db package ( version 2 . 2 . 11 ) was used for the Gene Ontology and the KEGG . db package ( version 2 . 2 . 11 ) for the KEGG pathway data ., Software packages are listed in Table S12 .

Introduction, Results, Discussion, Materials and Methods

The genetic dissection of the phenotypes associated with Williams-Beuren Syndrome ( WBS ) is advancing thanks to the study of individuals carrying typical or atypical structural rearrangements , as well as in vitro and animal studies ., However , little is known about the global dysregulations caused by the WBS deletion ., We profiled the transcriptomes of skin fibroblasts from WBS patients and compared them to matched controls ., We identified 868 differentially expressed genes that were significantly enriched in extracellular matrix genes , major histocompatibility complex ( MHC ) genes , as well as genes in which the products localize to the postsynaptic membrane ., We then used public expression datasets from human fibroblasts to establish transcription modules , sets of genes coexpressed in this cell type ., We identified those sets in which the average gene expression was altered in WBS samples ., Dysregulated modules are often interconnected and share multiple common genes , suggesting that intricate regulatory networks connected by a few central genes are disturbed in WBS ., This modular approach increases the power to identify pathways dysregulated in WBS patients , thus providing a testable set of additional candidates for genes and their interactions that modulate the WBS phenotypes .

A fundamental question in current biomedical research is to establish a link between genomic variation and phenotypic differences , which encompasses both the seemingly neutral diversity , as well as the pathological variation that causes or predisposes to disease ., Once the primary genetic cause ( s ) of a disease or phenotype has been identified , we need to understand the biochemical consequences of such variants that eventually lead to increased disease risk ., Such phenotypic effects of genetic differences are supposedly brought about by changes in expression levels , either of the genes affected by the genetic change or indirectly through position effects ., Thus , transcriptome analyses seem appropriate proxies to study the consequences of structural variation , such as the 7q11 . 23 deletion present in individuals with Williams-Beuren syndrome ( WBS ) ., Here , we present an approach that takes experimental data into account instead of relying solely on functional annotation , following the rationale that coherently regulated genes are likely to play a role in the same biological process ., While our algorithm can be applied to expression data from any source , our study provides a resource for the identification of additional candidate genes and pathways to explain the WBS phenotype , as well as a basis for uncovering novel functional interactions between sets of genes .

genetics and genomics/genomics, computational biology/transcriptional regulation, genetics and genomics/gene expression, genetics and genomics/bioinformatics, computational biology/genomics, computational biology, genetics and genomics, computational biology/systems biology, genetics and genomics/medical genetics

journal.pgen.1002511

Ultrafast Evolution and Loss of CRISPRs Following a Host Shift in a Novel Wildlife Pathogen, Mycoplasma gallisepticum

Populations of animals are under constant threat from bacterial pathogens , which can be particularly destructive following a switch to a new host or the evolution of novel virulence mechanisms ., Understanding the rate and process of evolutionary change in pathogens is thus important to assessing the risks of pandemics and developing means to predict and avoid such catastrophic events ., In 1994 , a strain of Mycoplasma gallisepticum ( MG ) was identified as the causative agent of an emerging epizootic in House Finches , a wild songbird inhabiting Eastern North America 1 ., This bacterial pathogen frequently causes disease in commercial chicken and turkey flocks , but it had never been reported in House Finches or any songbird , leading to the suggestion that the epidemic began when MG expanded its host range from poultry to this phylogenetically distant songbird ., MG prevalence reached 60% in some areas , and killed an estimated 225 million finches in the first three years after detection 2 ., The early detection of the epizootic allowed research and citizen-science teams to track its rapid spread throughout eastern North America in exceptional detail , making it one of the best documented wildlife pathogen outbreaks 3–7 ., Although previous genome-wide studies have clarified rates of measurable evolution in viral pathogens 8 , 9 and in bacterial populations evolving under laboratory conditions or as human pathogens 10–18 , less is known about rates of genetic change in bacterial pathogens of non-mammalian vertebrates , particularly on short evolutionary time scales ., Genome-wide and gene-specific estimates of point substitution in bacterial lineages measured over centuries 19 to millions of years 20 suggest maximum substitution rates on the order of 10−7 to 10−9 per site per year ., Although recent work suggests the rate may be even faster for several bacterial species 12 , 14 , 19 , the number of studies documenting whole-genome changes in bacteria during host switches is still small , particularly for wildlife pathogens 21 , 22 ., As part of ongoing surveillance , field isolates of MG obtained from infected finches were sampled at multiple time points from the start of the epidemic in 1994 to 2007 , providing a genetic time series beginning immediately after the host switch , as well as an opportunity to directly measure the tempo and mode of evolution in a natural bacterial population whose genome is as yet uncharacterized ., To characterize patterns of genomic change during its host switch between distantly related avian species , we sequenced whole genomes of 12 House Finch MG isolates from this 13-year time series , with four samples each from the beginning ( 1994–1996 ) , middle ( 2001 ) and recent ( 2007 ) periods ( Table S1 ) ., In addition , to identify putative source strains as well to determine if differences between the House Finch MG strains and the ∼1 Mb published reference Rlow strain from chicken 23 were ancestral or derived , we sequenced four additional strains from chicken and turkey based on phylogenetic analysis of a smaller multistrain data set ( Figure S1 ) ., Our sequence , SNP filtering and between-platform cross-validation protocols yielded a high quality 756 , 552 bp alignment encompassing 612 genes ( Tables S2 , S3 , S4 , Text S1 , Figure S2 ) , and allowed us to monitor point substitutions , genomic indels , IS element insertions , and other changes across the entire genome ( Figure 1 ) , including the entire array of clustered regularly interspaced short palindromic repeats ( CRISPR ) of all 17 strains ( finch and poultry isolates ) ., All House Finch MG samples were collected in the southeastern U . S . ( Table S1 ) , with an emphasis on the well studied population in Alabama 24 , 25 ., The population structure of Eastern House Finches before the epizootic was virtually panmictic 26 , suggesting that there is likely to be little geographic structuring of MG in the east , a hypothesis that could be tested with additional data ., The 12 House Finch strains from the three time periods spanned the known temporal and phylogenetic diversity of this lineage , and included strains that have been used to study host response to pathogen infection in House Finches 27 ., To determine genetic diversity and phylogenetic identity of putative source populations of the House Finch MG strains , and to aid in sampling chicken and turkey strains for sequencing , we first analyzed a previously published data set 28 ., Phylogenetic analysis of 1 , 363 bp obtained from four genomic regions for a large sample ( n\u200a=\u200a82 ) of MG strains suggests that turkeys rather than chickens were the source of House Finch MG and that the MG lineage colonizing House Finches first passed multiple times among chickens and turkeys ( Figure S2 ) ., Although this analysis suggests frequent host switches between chickens and turkeys , which diverged 28–40 MYA 29 , 30 , it also suggests a single switch to the House Finch , a songbird species diverged from chickens by ∼80 MYA 31 ., The whole genome alignment contained strong signals of a founder event as a result of colonization of House Finches ., The total nucleotide diversity ( π ) in the House Finch strains for the four-gene region was only 3 . 1% of the diversity in circulating poultry strains prior to the epizootic , and only 2 . 3% of the poultry diversity when considering the entire House Finch MG genome 28 ( Figure 2 and Table S5 ) ., In agreement with the four-gene analysis , our whole genome sequencing showed that the four sequenced poultry isolates were much more genetically diverse than the 12 House Finch isolates , possessing a total of 13 , 175 SNPs as compared to only 412 SNPs among the House Finch isolates ( Table S2 ) ., The House Finch MG diversity corresponds to π\u200a=\u200a0 . 00014 , or roughly 1 SNP every 1 , 800 bp ., Consistent with purifying selection acting over the longer time period encompassing the divergence of House Finch and poultry MG strains ( as opposed to acting after the host-switch among House Finch strains alone ) , there was a stronger bias against non-synonymous substitutions among the more diverged poultry strains than among the recently diverged House Finch MG strains ( Table S6 ) ., Across the entire genome , only 147 ( 35% ) of the SNPs among the House Finch isolates were phylogenetically informative; the majority ( 265 or 64% ) appeared as singletons ., To further quantify House Finch MG demography , we used a statistical model , the Bayesian skyline plot implemented with BEAST , that utilizes information on dates of sampling to estimate changes in genetic diversity through time 32 , 33 ( Text S2 ) ., The analysis is broadly consistent with field observations suggesting a mid-1990s origin followed by rapid population expansion , though it estimates that the House Finch MG lineages coalesced roughly in 1988 , several years prior to the observation of sick birds in the field ( estimated MRCA of the House Finch MG strains is 19 . 2 years prior to 2007 95% HPD 16 . 9 – 21 . 7; Figure 2d ) ., Discrepancies between coalescence times and observed outbreaks in host populations have been observed for other pathogens , and could possibly be due to selective or demographic effects , or in our case low sample size 12 ., Phylogenetic analysis suggests substantial turnover in the standing SNP variation between sampling intervals , with strong clustering of the 2007 strains , which are distinguished from other House Finch strains by 85 diagnostic SNPs ( Figure 3 ) ., We found that one of the sequenced turkey strains , TK_2001 , was highly similar in sequence to the House Finch strains and shares a number of genomic deletions and transposon insertions as well as duplications and losses of CRISPR spacers ( see below ) with the House Finch MG strains ., This turkey strain may represent a poultry lineage close to the source lineage for House Finch MG ( Figure 3 ) ., In addition to SNPs in House Finch MG we found five large genomic deletions that occurred by 2007 and amounted to ∼42 , 245 bp and encompassing 34 genes relative to the chicken Rlow strain ( Figure 1 and Figure 3 , Table S7 ) ., Three of these deletions are phylogenetically informative among the 17 MG strains ( Table S7 ) , but their conflicting phylogenetic distribution underscores the presence of recombination ( see next section ) ., Two deletions totaling 9 , 275 bp were shared among all strains except the reference ., In addition , we detected six novel IS element insertions in the House Finch MG lineage ( Text S3 , Table S8 ) and three of the genomic deletions were likely mediated by illegitimate recombination between flanking IS elements ( Table S7 ) ., In addition to the 34 genes deleted as part of genomic deletions , we found evidence for pseudogenization of 19 genes relative to the chicken MG reference ( Text S3 , Table S9 ) ., Two genes appear to have been disrupted by transposon insertions and 17 genes were pseudogenized by frameshift or nonsense mutations ( Table S9 ) ., The substantial gene losses we detected , a total of 52 genes ( ∼8 . 6% ) fixed in the House Finch MG lineage , presumably as a result of the bottleneck during host switch ., By contrast , we failed to find a single novel gene in House Finch MG that was not also found in the poultry MG strains ( Text S5 ) ., Comparative analysis with other Mycoplasma genomes showed that 15% of these lost genes also lacked a homologue in the other genomes surveyed whereas 13% had a homologue in every genome ( Table S9 ) ., Despite the small amount of genetic variation segregating among our House Finch Mycoplasma samples ( only 412 SNPs ) , it is not possible to construct a phylogenetic tree for these strains that is free of homoplasies ., Although the four 2007 strains and all 2001 strains except AL_2001_17 clearly formed well defined clades based on 85 and 28 SNPs , respectively , establishing the phylogenetic relationships for the other 5 House Finch MG strains exclusively via SNPs was not possible ( Text S6 , Figure 3 ) ., Although a total of 16 SNPs were phylogenetically informative for the placement of these five strains , the largest cluster of SNPs that were phylogenetically consistent was seven , and overall , 13 different trees were supported by at least 3 SNPs each ., Similarly , substantial homoplasy was found among the four newly sequenced poultry strains and the Rlow reference ., Although 6 , 152 SNPs were parsimony informative for these five strains , the unrooted tree with the best support was in conflict with 4 , 619 ( 75% ) of these SNPs ., These patterns are expected if sites are being shuffled by recombination or horizontal gene transfer ( HGT ) among isolates , and analysis of the entire data set found strong support for this ( Text S4 , Figures S3 , S4 , S5 ) ., Using the pairwise homoplasy index test 34 revealed a statistically significant signal of recombination ( p<10−9 ) ., This signal comes predominantly from the four newly sequenced poultry strains because there is not enough genetic variation to make this test significant when only the House Finch strains are considered ., However if we apply to the House Finch MG strains the homoplasy test by Maynard-Smith and Smith 35 , which is found to perform well in situations of low nucleotide diversity 36 , we again obtain a significant signal for recombination ( p<10−6 ) ., We conclude that , despite a significant signal for recombination in both the poultry and House Finch strains , the House Finch MG cluster as a whole is a distinct and easily identifiable phylogenetic lineage with a long branch separating it from the poultry strains ( Figure 3 ) ., Coalescent analysis 32 of the 12 House Finch isolates sampled at different dates suggested an extraordinary point substitution rate of 1 . 02×10−5 substitutions per site per year ( 95% HPD 7 . 95×10−6 to 1 . 23× 10−5 ( Text S2 ) , consistent with earlier suggestions that Mycoplasma may be among the fastest evolving bacteria 37 ., This rate of point substitution is not restricted to House Finch MG strains but was also found in the poultry strains when analyzed separately ( Text S2 ) , suggesting that rapid evolution was characteristic of MG prior to the House Finch epizootic ., We estimated a similar substitution rate when considering only the four-gene multistrain alignment use to identify poultry strains for sequencing ( Text S2 ) ., We verified that our estimate of substitution rate is robust to different protocols for SNP identification , statistical models and data sets ( Figure 4; Text S7 ) ., Altogether we estimated the substitution rate within a coalescent framework on 34 combinations of SNP calling and model assumptions and found consistent estimates throughout ( Text S1 , Figure 4 , Figure S6 ) ., In addition , we achieved a similar estimate using a Poisson regression approach as well as a root-to-tip regression ( Text S7 and Figure 4 ) ., In addition to a high estimated substitution rate in MG , we found a mutation in the gene-encoding UvrB that could elevate this rate yet further ., UvrB is an essential part of the nucleotide excision repair system , which has been posited to be the most important pathway for maintaining genomic integrity in Mycoplasma 38 ., The mutation truncates the UvrB protein by three amino acids ( Table S10 ) and raises the possibility of the origin of a mutator strain in House Finch MG 39 as the C-terminal of this protein is essential for its function 40 ., Consistent with this idea , we found 14 instances of adjacent SNPs among the 12 House Finch isolates , a notable excess in an alignment with only 412 variable sites ( Table S11 ) ., Moreover , 12 of these 14 are CC→TT double substitutions , which are normally repaired by the UVR system ( Table S10 ) ., For 13 of the 14 doublets , both sites are inferred to have mutated on the same branch of the tree , suggesting single mutational events , and the proportion of doublet mutations involving the same base was drastically higher ( 92 . 8% ) in lineages with the UvrB mutation as compared to those without ( p<0 . 0001; Table S10 ) ., Nonetheless , these doublet mutations are not required to achieve the high rate of substitution that we measured ., They account for less than 7% of the segregating variation and removal of these doublet sites does not affect the high estimated substitution rate ., The UvrB mutation is found in all of our House Finch MG strains as well as the turkey strain TK_2001 , but not in the ancestral chicken strains or the reference chicken strain ., Thus , the mutation appears to have arisen on the lineage leading to the House Finch ., In some bacterial systems , CRISPRs have a well-recognized function in bacterial immunity and defense against phage , although they may possess additional functions , such as gene regulation 41–44 ., We extensively catalogued CRISPR repeats in the House Finch and ancestral poultry strains ( Figure 5 , Text S8 , Table S12 ) ., In so doing we observed drastic changes in the CRISPR system between House Finch and poultry strains ( Figure 5 ) 45–48 ., The House Finch MG strains from 1994–96 contain up to 50 unique spacers , none of which is shared with the four divergent poultry genomes , which each contained a unique set of 36 to 147 spacer regions consistent with a high rate of turnover for a population actively acquiring new spacer sequences ., We found that less than 1% of the 302 unique spacer sequences had similarity to any sequences in the House Finch MG genomes and that none of the remaining spacers had any similarity to sequences in Genbank , indicating an external source for these sequences ( Text S8 ) ., Surprisingly , no novel spacer elements are present in any of the House Finch MG samples or TK_2001 , indicating that the CRISPR array ceased recruiting additional spacers around the time of host switch into the House Finch ., In fact , over the 13-year period of the epizootic , the number of unique spacers present in the CRISPR array of the samples decreased to 28 ( Figure 5 ) ., Further evidence for degradation of the CRISPR locus following the host switch is the complete loss of the four CRISPR-associated ( i . e . “CAS” ) genes in all of the 2007 isolates , a loss that likely renders the CRISPR system in House Finch MG non-functional 45 ., We conducted whole-genome sequencing on a unique 13-year serial sample of Mycoplasma strains circulating in wild House Finches to characterize genomic changes accompanying a host shift from poultry in the mid-1990s as well as to obtain a very high substitution rate for this avian pathogen ., Previous estimates using serial samples and/or the known timing of events presumably tied to the divergence of bacterial strains have generally found much lower rates ., An estimate of 2 . 0×10−6 was obtained for Staphyloccous aureus 12 , 1 . 1×10−7 for Buchnera 19 , 7 . 42×10−7 in Yersinia pestis and 1 . 4×10−6 in Heliobacter pylori 14 ., Disentangling the effects of recombination and point substitution can be challenging and some previously published substitution rates are likely to be upper bounds rather than point estimates 12 ., Our estimate appears to be among the highest reported for a bacterium , and is consistent with other reports of exceptionally high substitution rates in mycoplasmas 37 ., Estimates of substitution rates can be influenced by the interval over which sequences are sampled , with estimates taken from short time intervals often exceeding those taken on biogeographic or geological time scales 49 ., However the small number of SNPs that we detected segregating in House Finch MG populations suggest negligible effects of multiple hits on our estimate , and our use of a coalescent model suggests that effects of ancestral polymorphism on substitution rate estimates should be adequately accounted for 32 , 50 ., Additionally , our estimates of substitution rate were robust to many potential complicating factors , including SNP calling protocol and whether poultry or House Finches were used as the host for sampled sequences ., Given the history and genetic isolation of the House Finch MG strains , the influence of recombination or lateral gene transfer on our estimate of substitution rate is likely also minimized ( Text S7 ) ., The CRISPR dynamics we observed in House Finch MG differ from that seen in other pathogen and bacterial populations ., A recent study of Y . pestis CRISPR arrays from 131 strains 51 indicated a slower pace of CRISPR evolution than observed in MG and pattern of evolution in which acquisition of novel sequences does not play a prominent role ., This study found that in Y . pestis the first part of the CRISPR arrays were conserved and that over 76% of all spacer sequences derived from within the Y . pestis genome ., Similarly , a recent study of E . coli and Salmonella genomes found that strains within 0 . 02% divergence typically have identical CRISPR loci 52 and that spacer sequences were often matched to elements of the E . coli genome ., Additionally , some spacer sequences were shared between strains within a species exhibiting over 1% sequence divergence ., These observations and an estimated substitution rate on the order of 10−10 per site per year suggested that E . coli strains that had diverged for 1 , 000 years sometimes shared identical CRISPR loci , suggesting patterns of evolution different from that expected for a rapidly changing adaptive immune system primed to combat phages , a conclusion that was supported by later work 53 ., By contrast to the pattern seen in these γ-proteobacteria , none of the House Finch MG strains in this study have the same CRISPR locus despite differing at only 0 . 01–0 . 02% of sites and likely having last shared a common ancestor less than 20 years ago ., Our serial sampling suggests that the loss of spacer sequences and the CRISPR system itself can take place on very short time scales in Mycoplasma ., Unlike the patterns seen in E . coli , Y . pestis , and Salmonella , the poultry MG strains in our study did not share any spacer sequences , even though they differed by ∼1% ., These strains had very large CRISPR arrays and 99% of all spacer sequences did not match any known sequence in their genome or in the databases ., Therefore the MG CRISPR loci studied here differ from the those observed in some γ-proteobacteria , a group for which CRISPR dynamics can appear functionally unrelated to ecology or immunity 53–55 ., Instead , our finding of rapid evolution and degradation of the CRISPR loci more closely resembles patterns found in other bacterial groups , particularly those in which CRISPR is involved in phage defense 56 ., CRISPRs are found in only 40% of sequenced bacteria investigated thus far , and often have major roles in bacterial immunity in several lineages investigated in detail 45 ., We were surprised to find a gradual degradation and ultimate apparent functional loss of the CRISPR system in House Finch MG after the host switch and a shift in CRISPR dynamics appears to be a major correlate of host switch in this system ., One possible explanation for this pattern is that MG experienced release from its ancestral phage parasite community ( or other mobile genetic elements such as plasmids ) following introduction into the House Finch ., Loss of traits upon removal of the agent of selection is a common evolutionary response , as are population expansions of animals and plants when introduced into novel habitats unaccompanied by their parasites 57 ., Despite the large amount of ecological research focusing on this host-pathogen system 3–7 , at present nothing is known about phages that infect MG or their role in its evolutionary dynamics ., Therefore the hypothesis of parasite release as a driver of CRISPR loss is purely speculative ., We know of no phage known to infect the Pneumoniae phylogenetic group of mycoplasmas and the few phages known to infect Mycoplasma have proven difficult to characterize 58 ., We might expect Mycoplasma bacteriophages to be host-specific given that they seem to be unusual in their ability to bind to a bacterium with no cell wall and a diverse assortment of surface proteins 58 ., However , we are not aware of even basic data on the degree to which Mycoplasma might be susceptible to the many bacteriophages that they presumably encounter in their environment ., Although phage represent one possible source for these novel ∼30 bp sequences , another possible explanation for the source of the spacer sequences is that they derive from plasmids ., Although unprecedented ( we know of no examples of a naturally occurring plasmid in the Pneumoniae mycoplasmas ) , such a scenario could raise the possibility of easier genetic manipulations in MG where development of such tools has been challenging 59 ., Of the many other possibilities that could explain the observed degradation of the CRISPR loci , we can at least rule out self-interference as an explanation in derived MG strains , given that there is only a single CRISPR cluster in House Finch MG 54 ., Measurement of costs , possible advantages and consequences of CRISPR loss , as well as functional and evolutionary assays and surveys of phage diversity will help determine if the rapid and deadly spread of Mycoplasma following their expansion into the House Finch was facilitated by a lack of phage predation , a short-term advantage of CRISPR degradation or some other , possibly neutral , mechanism ., Although our sequence data is suggestive , explicit functional studies will also be required to demonstrate CRISPR functionality or lack thereof in poultry and House Finch MG and its role , if any , in phage defense ., Genome evolution of MG during its host-switch from poultry to House Finches adds to a growing list of host-switches that are successful in the complete absence of novel genes 21 , 60 , 61 and bacterial lineages exhibiting high rates of point substitution 14 ., Mycoplasmas are some of the fastest evolving organisms on earth 62 having lost many of the repair mechanisms present in other bacteria 38 and this high mutation rate could help introduce deleterious mutations and contribute to the substantial level of pseudogenization that was observed in this study ., The high basal substitution rate in MG may well be elevated yet further by UvrB mutation that we detected , a mutation that could have consequences for the long term genomic integrity of this MG lineage , particularly if it remains genetically distinct from and unable to exchange genes with the poultry MG lineages with a functional UvrB ., Alternatively , given the short ( 3 amino acid ) truncation of this gene in the House Finch strains , another explanation for the greatly increased number of doublet mutations in the lineage carrying the UvrB truncation is that selection has not had enough time to remove them as it has for poultry strains without this mutation ., Although mutator strains are known to have a selective advantage in rapidly evolving laboratory and natural populations 39 , 63 , additional functional and experimental work will be required to determine the selective and functional effect of the mutation we have detected in UvrB , and over what time scales such selective effects might persist ., For this and other endeavors , serial sampling of additional bacterial populations in nature will further clarify the rate at which genomes are remolded during host switches in the wild ., DNA sequence data for 4 gene fragments collected from 74 strains in Ferguson et . al . 28 , was combined with data from 8 strains newly sequenced in this study to yield a Large Sample Multiple Sequence Alignment ( LS-MSA ) 1 , 363 bp in length ( Figure S2 ) ., We estimated nucleotide diversity and the standard deviation of this estimate within and among subgroups of these sequences using DNAsp version 4 . 10 . 9 64 ( Table S5 ) ., In estimating diversity of MG strains sampled from chickens and turkeys , we restricted analysis to those strains sampled during 1994–1996 for comparison with our earliest House Finch strains sampled in a similar time interval ., Twelve strains of MG isolated from House Finches in the Southeastern US were sequenced with the Roche 454 Gene Sequencer ., The average coverage level was 9 . 4X ( Table S1 ) ., Additionally , four MG strains isolated from poultry hosts and selected based on their positions in the multistrain phylogenetic tree were sequenced with the Illumina sequencing platform to an average coverage of ∼410 X ( Tables S2 , S3 , S4 , Text S1 , Figure S2 ) ., Using a coalescent model and a Bayesian framework as implement in BEAST v1 . 52 32 we estimated the mutation rate and times to common ancestry from a 13-taxon alignment composed of the reference MG genome and all of the House Finch MG strains whose genomes were sequenced in this study ( Text S2 ) ., We also ensured that the conclusions from this inference were not sensitive to the SNP calling procedures or the choice of substitution models ( Text S2 , S7 , Figure S6 ) ., In order to compare the mutation rate between the poultry and House Finch MG populations , these quantities were similarly estimated from the 82 taxon LS-MSA after removing nine laboratory strains from the alignment that likely experienced different population dynamics than the wild strains and had unknown sampling dates ., A Poisson regression model was also used to estimate substitution rates by counting mutations along a single lineage assumed to span the dates of sampling for each strain ( Text S7 ) ., We catalogued IS elements using BLAST and the ISFinder database 65 , Text S4 ., We tested for evidence of genetic recombination between MG strains using the genome sequences from our 4 poultry and 2 House Finch strains using the pairwise homoplasy index test 34 as implement in splitstree4 66 , and the homoplasy test by Maynard-Smith and Smith 35 ., Further evidence for the presence of recombination and the number of nonrecombining blocks was provided by other methods ( Text S6 , Figures S3 , S4 , S5 ) .

Introduction, Results, Discussion, Materials and Methods

Measureable rates of genome evolution are well documented in human pathogens but are less well understood in bacterial pathogens in the wild , particularly during and after host switches ., Mycoplasma gallisepticum ( MG ) is a pathogenic bacterium that has evolved predominantly in poultry and recently jumped to wild house finches ( Carpodacus mexicanus ) , a common North American songbird ., For the first time we characterize the genome and measure rates of genome evolution in House Finch isolates of MG , as well as in poultry outgroups ., Using whole-genome sequences of 12 House Finch isolates across a 13-year serial sample and an additional four newly sequenced poultry strains , we estimate a nucleotide diversity in House Finch isolates of only ∼2% of ancestral poultry strains and a nucleotide substitution rate of 0 . 8−1 . 2×10−5 per site per year both in poultry and in House Finches , an exceptionally fast rate rivaling some of the highest estimates reported thus far for bacteria ., We also found high diversity and complete turnover of CRISPR arrays in poultry MG strains prior to the switch to the House Finch host , but after the invasion of House Finches there is progressive loss of CRISPR repeat diversity , and recruitment of novel CRISPR repeats ceases ., Recent ( 2007 ) House Finch MG strains retain only ∼50% of the CRISPR repertoire founding ( 1994–95 ) strains and have lost the CRISPR–associated genes required for CRISPR function ., Our results suggest that genome evolution in bacterial pathogens of wild birds can be extremely rapid and in this case is accompanied by apparent functional loss of CRISPRs .

Documenting the evolutionary changes occurring in pathogens when they switch hosts is important for understanding mechanisms of adaptation and rates of evolution ., We took advantage of a novel host–pathogen system involving a bacterial pathogen ( Mycoplasma gallisepticum , or MG ) and a songbird host , the House Finch , to study genome-wide changes during a host-shift ., Around 1994 , biologists noticed that House Finches were contracting conjunctivitis and MG from poultry was discovered to be the cause ., The resulting epizootic was one of the best documented for a wildlife species , partly as a result of thousands of citizen science observers ., We sequenced the genomes of 12 House Finch MG strains sampled throughout the epizootic , from 1994–2007 , as well as four additional putatively ancestral poultry MG strains ., Using this serial sample , we estimate a remarkably high rate of substitution , consistent with past implications that mycoplasmas are among the fastest evolving bacteria ., We also find that an array of likely phage-derived sequences known as CRISPRs has degraded and ceased to recruit new repeats in the House Finch MG strains , as compared to the poultry strains in which it is diverse and rapidly evolving ., This suggests that phage dynamics might be important in the dynamics of MG infection .

zoology, biology, microbiology, evolutionary biology, population biology

journal.ppat.1000142

XIAP Regulates Cytosol-Specific Innate Immunity to Listeria Infection

The inhibitor of apoptosis ( IAP ) family of proteins plays a key role in cellular signaling , such as apoptosis , by binding to pro-apoptotic proteins , interrupting the intrinsic programmed cell death pathway and activating anti-apoptotic mechanisms 1–3 ., In addition to modulating apoptosis , recent genetic studies have revealed that a Drosophila IAP protein , diap2 , acts as a regulator of anti-microbial immunity 4–7 ., Innate immune signaling pathways are well conserved from Drosophila to humans , suggesting that IAP proteins may also play a role in mammalian innate immunity 8 ., This hypothesis is consistent with a study demonstrating that cIAP2 exacerbates endotoxic shock in mice by controlling macrophage apoptosis 9 ., Furthermore , a cohort of patients with X-linked lymphoproliferative syndrome ( XLP ) were found to have mutations in the gene encoding XIAP , resulting in a primary immunodeficiency 10 ., XIAP , also known as BIRC4 and hILP , contains three baculoviral IAP repeat ( BIR ) domains , the characteristic protein-protein interaction domain of the IAP family 11 ., XIAP also has a carboxy-terminal RING domain with E3 ubiquitin ligase activity that directs proteasomal degradation of target proteins 12 ., Multiple signaling pathways can be modulated by XIAP , including NF-κB , MAP kinase and TGFβ signaling 13–16 ., Moreover , XIAP can integrate cellular responses to diverse stimuli by interacting directly with ligands such as copper to regulate copper homeostasis 17 ., XIAP has been predominantly characterized as an inhibitor of apoptosis , and interacts with many known mediators of programmed cell death , such as JNK , TAK1 , TAB1 , TRAF6 , and caspases-3 , -7 , and -9 3 , 13 , 18 , 19 ., However , XIAP-deficient mice do not appear to have striking defects in apoptosis , thus the role of XIAP in vivo is not yet clearly understood 20 ., The innate immune response protects host organisms against invading pathogens prior to the onset of adaptive immunity ., Pathogens stimulate innate immune signaling through pattern recognition receptors ( PRR ) , which recognize well-conserved pathogen-associated molecular patterns ( PAMPs ) 21 ., PAMPs are detected at the host membrane by TLRs , and in the cytosol by the NLR and the RIG-I-like helicase ( RLH ) sensors 22 , 23 ., Stimulation of either extracellular or intracellular PRR can result in activation of NF-κB and MAP kinase signaling pathways , leading to production of inflammatory mediators such as cytokines and costimulatory molecules 24 ., Activation of TLRs and NLRs together can induce synergy between the signaling pathways , resulting in enhanced activation of innate and adaptive immunity 25 , 26 ., Listeria monocytogenes is a cytosolic bacterial pathogen used extensively to probe aspects of innate and adaptive immunity 27 ., L . monocytogenes is recognized by TLRs expressed on the surface of phagocytes 27 ., After phagocytic uptake , L . monocytogenes escapes from host vacuoles by secreting a pore-forming toxin , listeriolysin O ( LLO ) 28 ., Once in the cytosol , L . monocytogenes can trigger oligomerization and signaling by NOD1 and other NLRs 29 ., Here we show that XIAP plays a protective role during infection by L . monocytogenes ., We present evidence that amplifying JNK activation and subsequent pro-inflammatory cytokine production in response to cytosolic bacteria is one mechanism by which XIAP modulates innate immunity ., We first tested the hypothesis that XIAP contributed to anti-microbial immunity by infecting xiap+/y and xiap−/y mice with 1×105 L . monocytogenes and determining survival over time ( Figure 1A ) ., At 7 dpi ( days post infection ) , 60% of the XIAP-deficient mice had succumbed to infection , whereas all wild-type mice survived ., Similarly , at higher doses of L . monocytogenes more xiap−/y than xiap+/y mice succumbed to infection , although some xiap+/y mice also became moribund ( unpublished data ) ., Depending upon the inoculum , morbidity and mortality of xiap−/y animals occurred between 2 and 5 dpi , prior to peak development of adaptive immunity , suggesting that XIAP had a protective effect during the innate response to bacterial infection ., To better define the role of XIAP during innate immunity to intracellular bacterial infection , we infected wild-type and XIAP-deficient mice intraperitoneally with 5×105 L . monocytogenes , and harvested spleen and liver to enumerate bacterial burden at 24 , 28 and 72 hpi ( Figure 1B ) ., By 48 h , xiap−/y mice had approximately 10-fold more L . monocytogenes in liver and spleen at 48 hpi compared to the xiap+/y mice , consistent with our observation of their decreased survival ., At 72 hpi , the difference between the xiap+/y mice and the xiap−/y was even more pronounced , with the xiap−/y mice supporting 100-fold greater bacterial numbers ., These results indicate that XIAP mediates innate resistance to L . monocytogenes infection ., Mutations in XIAP have been associated with the human immunodeficiency syndrome , XLP 10 ., One feature associated with this disease is an abnormally low number of natural killer T-cells ( NKTCs ) , although it is not yet clear how much this phenotype contributes to immunodeficiency ., To determine if mice lacking XIAP exhibit a similar phenotype to XLP patients , we quantitated the percentage of NKTCs in the spleen of xiap+/y and xiap−/y mice ( Figure 1C ) ., No significant difference in the number of splenic NKTCs was observed between xiap+/y and xiap−/y mice , indicating that survival of NKTCs in uninfected mice is not affected by a deficiency in XIAP , consistent with a previous report 10 ., To determine if NKTC survival or activation was dependent on XIAP during L . monocytogenes infection , we infected animals and determined the number of splenic NK1 . 1+CD3+ NKTCs that expressed CD69 , a marker of activation ( Figure 1D ) ., We observed similar numbers of activated NKTCs in xiap+/y and xiap−/y mice ., These data suggest that XIAP does not play an important role in NKTC survival or activation in a murine model of listeriosis ., We then tested the role of XIAP during infection of primary macrophages , an innate immune effector cell and a well-characterized host for L . monocytogenes replication ., We infected unactivated bone marrow derived macrophages ( BMDMs ) , BMDMs activated with LPS and IFNγ or peritoneal macrophages with L . monocytogenes and measured intracellular bacterial growth over time ( Figure 2 ) ., All types of xiap+/y and xiap−/y macrophages controlled L . monocytogenes infection equally well ., We conclude from these data that XIAP does not contribute directly to restriction of L . monocytogenes growth in macrophages , even though XIAP-deficient mice exhibited an increased bacterial burden compared to wild-type mice ., Taken together , our results demonstrate that XIAP is required for a protective immune response to L . monocytogenes infection in vivo ., XIAP can activate NF-κB–dependent transcription in response to apoptotic stimuli 14 ., In addition to regulating apoptosis , the canonical NF-κB p50/p65 heterodimer has a well-established role in proinflammatory cytokine transcription stimulated by TLR and NLR signaling 21 ., Expression profiling of unactivated macrophages infected with L . monocytogenes did not reveal reproducible differences between wild-type and XIAP-deficient macrophages ( unpublished data ) ., We then reasoned that activated macrophages might be a more relevant environment for studying XIAP function ., We therefore investigated whether XIAP regulated NFκB-dependent processes during L . monocytogenes infection in activated macrophages by measuring translocation of p50 to the nuclear compartment ., Activated BMDM were infected with wild-type L . monocytogenes , and translocation of the p50 subunit of NF-κB was analyzed by immunoblot ( Figure 3A ) ., As early as 0 . 5 hpi , p50 was detected in the nuclear fraction of both xiap+/y and xiap−/y cells; however , in the presence of XIAP there was substantially more p50 in the nuclear fraction over time ., We also measured DNA binding activity of the p65 subunit of the p50/p65 heterodimer in the nuclear fraction of uninfected and L . monocytogenes infected activated macrophages ( Figure 3B ) ., At 1 and 2 hpi , infected xiap+/y macrophage nuclear lysates contained significantly more NF-κB DNA binding activity than infected xiap−/y nuclear lysates , suggesting that XIAP might enhance signaling of NF-κB–dependent pathways stimulated by bacterial infection ., In some contexts , XIAP-dependent NF-κB activation can protect against apoptotic stimuli; therefore we tested if XIAP modulated apoptosis during L . monocytogenes infection ., We first examined apoptosis in activated macrophages during L . monocytogenes infection by flow cytometry of infected cells using Annexin V ( AnnV ) , an indicator of apoptosis ( Figure 3C ) ., A modest but reproducible increase in apoptosis was observed by 3 hpi in XIAP-deficient macrophages compared to wild-type macrophages , which remained consistent throughout infection ( Figure S1A ) ., We also examined apoptosis in infected liver and spleen at sites of L . monocytogenes replication 48 hpi by performing TUNEL staining ( Figure 3D ) ., Although the extent of apoptosis at foci of infection were heterogeneous , there did not appear to be any notable difference in the number or distribution of apoptotic cells per focus in xiap+/y compared to xiap−/y livers or spleens ., We did not observe any XIAP-dependent difference in the numbers of AnnV+ T or B cells present in the spleens of mice at 48 hpi ( Figure S1B ) ., In addition , caspase-3 cleavage in infected activated macrophages was not significantly altered ( unpublished data ) ., While the infected xiap−/y macrophages exhibited a modest increase in cell death , we found no striking evidence for regulation of apoptosis by XIAP in the context of L . monocytogenes infection in vivo ., Thus , XIAP regulates NF-κB activation during L . monocytogenes infection , but may enhance innate immunity by modulating cellular responses other than apoptosis in infected macrophages ., In addition to NF-κB activation , TLR and NLR sensing of microbial infection stimulate MAP kinase phosphorylation , leading to activation 30 ., Previous reports suggested that XIAP can promote JNK phosphorylation via interaction with TAB1 and the MAP3K , TAK1 14 , 16 , 31 ., To determine if XIAP affected JNK phosphorylation during L . monocytogenes infection , we performed immunoblot analysis of infected lysates from xiap+/y and xiap−/y activated macrophages using a phospho-JNK specific antibody ( Figures 4A and S2A ) ., Upon infection with wild-type L . monocytogenes , JNK phosphorylation occurred as early as 0 . 5 hpi in both xiap+/y and xiap−/y cells ., In the xiap−/y macrophages , JNK phosphorylation peaked at 0 . 5 hpi ., However , in the presence of XIAP , enhanced JNK activation was prolonged up to 6 h ., This suggests that XIAP augments JNK signaling during wild-type L . monocytogenes infection ., To determine the contribution of XIAP to cytosol-specific signaling , we compared wild-type L . monocytogenes infection with a strain deficient in LLO or heat-killed L . monocytogenes ( HKLM ) , which both remain trapped in the vacuole ., The LLO− bacteria and HKLM induced JNK phosphorylation at 0 . 5 hpi similarly to infection by wild-type bacteria , suggesting that this early JNK phosphorylation was linked to signaling from the vacuole , most likely through TLRs ., However , JNK phosphorylation in response to vacuolar bacteria quickly diminished after 30 min , in contrast to the extended XIAP-dependent JNK activation observed during wild-type bacterial infection ., To confirm that enhanced JNK phosphorylation in xiap+/y activated macrophages resulted in downstream signaling , we examined phosphorylation of c-jun , a target of JNK , by immunoblot ( Figures 4B and S2B ) 32 ., Upon infection by wild-type L . monocytogenes , c-jun phosphorylation was prolonged in xiap+/y but not xiap−/y cells , similarly to JNK phosphorylation ., Moreover , activation of c-jun upon infection by LLO− bacteria was considerably decreased compared to wild-type bacteria ., To determine if XIAP also stimulated activation of other MAP kinase family members , we analyzed phosphorylation of p38 and ERK by immunoblot of infected macrophage lysates ( Figures 4C , 4D , S2C , and S2D ) ., ERK1 and ERK2 were phosphorylated equivalently in xiap+/y and xiap−/y macrophages in response to infection by all L . monocytogenes strains ., As previously shown , p38 phosphorylation was decreased during infection by vacuole-restricted bacteria compared to wild-type bacteria 33 ., Phosphorylation of p38 upon infection with wild-type L . monocytogenes was not significantly affected by XIAP ., These data demonstrate that XIAP prolongs JNK activation specifically in response to cytosolic L . monocytogenes ., Since XIAP modulated JNK and NF-κB signaling in the context of infection , we hypothesized that induction of proinflammatory cytokines through these pathways would also depend on XIAP ., Activated macrophages were infected with L . monocytogenes for 3 h , and RNA was analyzed by qRT-PCR to determine the expression of a subset of genes involved in innate immunity ( Figures 5A and S3 ) ., Transcription of il6 , tnf , il10 , mip2 , and kc was strongly upregulated upon infection in the presence of XIAP , while induction of ifnb , il1b , ido , and inos was not significantly altered ., To assess if XIAP-dependent gene expression correlated to increased protein production , we compared the secretion of IL-6 and TNF from uninfected and infected activated macrophages ( Figure 5B and 5C ) ., Upon infection by wild-type L . monocytogenes , IL-6 and TNF secretion was induced to a greater extent in xiap+/y macrophages than in xiap−/y macrophages , while infection with the LLO− mutant induced little IL-6 and TNF secretion by either genotype ., To determine if JNK activation was required for induction of IL-6 gene expression and secretion in response to wild-type L . monocytogenes infection , we treated activated macrophages with the JNK inhibitor SP600125 ( Figure 5D ) ., IL-6 secretion by infected macrophages was markedly diminished by JNK inhibition , indicating that JNK activation is required for IL-6 induction by L . monocytogenes ., Moreover , since LLO− mutant bacteria stimulated robust but temporally limited JNK phosphorylation and little IL-6 secretion , we infer that prolonged JNK activation is necessary for maximal IL-6 production during intracellular infection by L . monocytogenes ., When L . monocytogenes infected cells were treated with an ERK-specific inhibitor , IL-6 secretion was similar to the untreated infected control cells ., These results collectively suggest that the presence of XIAP enhances JNK activation in response to cytosolic bacteria , resulting in increased production of proinflammatory cytokines ., To determine if XIAP enhanced proinflammatory gene expression in vivo , we performed qRT-PCR analysis on splenic RNA ., RNA was isolated from splenocytes harvested from uninfected animals or animals infected with L . monocytogenes for 48 h ( Figure 6 ) ., We examined the expression of several proinflammatory cytokines including IL-6 , TNF , and IFN-γ , produced during the innate immune response that are critical for clearing L . monocytogenes infection 34–36 ., The expression of il6 and ifng were significantly enhanced in the presence of XIAP during infection , while expression of tnf and ifnb were not altered ., We also examined the expression of il17 , a cytokine known to enhance expression of il6; we observed no reproducible differences in the expression of il17 37 ., These data confirm the results from our in vitro macrophage model; that XIAP promotes the expression of proinflammatory cytokine genes in response to L . monocytogenes infection ., Innate immune signaling mediated by pattern recognition receptors , located on cellular membranes or in the host cytosol , stimulates transcription and secretion of proinflammatory cytokines ., We used purified TLR and NLR ligands to better define a role for XIAP in innate immune signaling ., Wild-type and XIAP-deficient activated macrophages were treated with TLR ligands , and secretion of IL-6 and TNF was measured after 24 h ( Figure 7A and unpublished data ) ., While some PAMPS , such as the lipoprotein Pam3CSK4 , could induce high levels of IL-6 and TNF , we found no XIAP-dependent differences in proinflammatory cytokine induction ., These results suggest that XIAP does not contribute to cytokine output in response to TLR stimulation alone ., During a physiological infection , intracellular pathogens activate both extracellular and cytosolic innate immune pathways resulting in a coordinated immune response 27 ., One well-characterized consequence of microbial sensing by cytosolic NLR proteins is activation of caspase-1 , which cleaves pro-IL-1β into its mature form 38 ., Since XIAP can regulate the activity of some caspases , we tested whether XIAP contributed to IL-1β production , measured by ELISA , as an indicator of caspase-1 activation ( Figure 7B ) ., Consistent with previous reports , IL-1β production was induced by cytosolic L . monocytogenes , but was not dependent upon XIAP 39 ., We next examined the activation of NLR signaling using MDP , a ligand for NOD2 ( Figure 7C–7E ) ., No differences in cytokine secretion were observed by treatment with MDP alone , however , during a physiological infection bacteria likely present both TLR and NLR ligands to an infected host cell ., PAMPs contained by L . monocytogenes include lipoprotein , muramyldipeptide , bacterial DNA and flagellin 27 ., To determine if XIAP enhanced synergy between TLRs and NLRs , we examined IL-6 , TNF and IL-1β secretion from xiap+/y and xiap−/y activated macrophages in response to the lipopeptide Pam3CSK4 , the NOD2 ligand MDP , or both ( Figure 7C–7E ) ., When Pam3CSK4 and MDP were used in combination , we saw a substantial increase in IL-6 and TNF secretion by xiap+/y but not xiap−/y activated macrophages ., We did not see any XIAP-dependent enhancement of IL-1β secretion in response to Pam3CSK4 and MDP in combination ., To better deconstruct how XIAP might participate in integrating TLR and NLR signaling , we analyzed transcription of the il6 gene from xiap+/y and xiap−/y activated macrophages treated with MDP , Pam3CSK4 , or both ligands ( Figure 7F ) ., Pam3CSK4 induced expression of the il6 gene in an XIAP-independent manner ., Upon treatment with MDP , xiap+/y but not xiap−/y macrophages , responded by upregulating il6 transcript levels approximately 5-fold ., When macrophages were treated with both ligands , xiap+/y macrophages exhibited enhanced expression of il6 compared to treatment of Pam3CSK4 alone , but xiap−/y macrophages did not ., These results demonstrate that XIAP promotes synergy between the TLR and NLR pathways , resulting in increased production of pro-inflammatory cytokines ., Here we show that XIAP can regulate innate immunity to the bacterial pathogen , L . monocytogenes by modulating JNK and NF-κB signaling , resulting in enhanced cytokine production ., We found little evidence to suggest that XIAP regulated apoptosis of bacterially infected cells in vitro or in vivo , but instead found that XIAP promoted synergistic inflammatory cytokine expression induced by extracellular and cytosolic innate immune signaling upon bacterial infection of activated macrophages ., Specifically , XIAP amplified the cytosolic response to MDP or wild-type L . monocytogenes ., These data identify XIAP as a regulator of cytosolic innate immune signaling ., Notably , another IAP family member NAIP5 was found to mediate caspase-1 activation in response to cytosolic bacterial flagellin 40–42 ., NAIP5 function in innate immunity could be attributed to the atypical domain structure of this IAP protein that exhibits similarities to the NLR family of cytosolic sensors 43 ., However , these data taken together with our results lead us to speculate that regulation of innate immune signaling is an important role of mammalian IAPs ., The IAP family appears to play multiple roles in mammalian biology , including protecting cells from apoptotic stimuli , regulating the cell cycle and modulating innate immune signaling ., As a whole , these studies are consistent with genetic evidence in Drosophila demonstrating that dIAP1 primarily protects insect cells from programmed cell death , while dIAP2 is required for anti-microbial function of the Imd pathway 4–7 ., The Imd pathway in Drosophila is activated by peptidoglycan recognition proteins ( PGRPs ) , while functionally analogous innate immune sensing of peptidoglycan in mammalian cells occurs in the cytosol by NOD1 , NOD2 , and NALP3 44 ., The Imd protein in Drosophila shares sequence homology with the mammalian RIP proteins , and a mammalian paralog , RIP2 , is an essential signaling adaptor for the cytosolic peptidoglycan sensors , NOD1 and NOD2 8 , 45–47 ., Thus , the Imd/RIP innate immune signaling module appears to have been co-opted for mammalian cytosolic surveillance for peptidoglycan ., Genetic epistasis experiments in Drosophila place dIAP2 in parallel to TAK1 upstream of JNK and NF-κB signaling pathways 4 ., Similarly , in mammalian cells , XIAP can modulate JNK and NF-κB signaling through TAK1 in endothelial cells and fibroblasts 13 , 48 ., Activation of either NOD1 or NOD2 activates TAK1 , leading us to hypothesize that during bacterial infection , XIAP may facilitate this key association , linking cytosolic sensors to downstream signaling mediators 49 , 50 ., During infection , microbial pathogens present multiple PAMPs recognized by the innate immune system , eliciting a coordinated protective response ., This concept is illustrated by the paradigm of IL-1β processing , where TLRs mediate transcription of pro-IL-1β; however , cleavage and secretion are dependent upon activation of the caspase-1 inflammasome by cytosolic PAMPs 51 ., However , IL-1β deficient mice are as resistant to L . monocytogenes infection as wild-type mice , suggesting that other inflammatory cytokines mediate innate immune control of this infection 52 ., In contrast , IL-6- , TNF- and IFNγ-deficient mice are more susceptible to L . monocytogenes infection at 48 hpi than wild-type mice , demonstrating a requirement for IL-6 , TNF , and IFNγ in protection from this particular pathogen 34–36 , 53 , 54 ., IFNγ is largely produced by innate immune effector cells other than macrophages , thus our observation that ifng transcription is decreased in the spleens of L . monocytogenes-infected XIAP mutant mice must be due to either a XIAP-dependent cell autonomous defect in a different cell type or a non-autonomous defect in an IFNγ producing cell resulting from a defect in macrophages 55 ., Since XIAP is expressed in many different tissues , it is reasonable to suppose that XIAP may have pleiotropic effects in the innate immune response to L . monocytogenes 56 ., However , macrophages are primary producers of IL-6 and TNF , and notably , NOD2 signaling is known to stimulate production of IL-6 and TNF 45 , 57 ., The deficit in IL-6 and TNF production we observed in infected xiap−/y activated macrophages , and the defect in gene expression in vivo likely contributes to the enhanced susceptibility of XIAP-deficient animals to L . monocytogenes infection ., Recent reports indicate that macrophages treated with LPS become tolerized to re-stimulation with TLR ligands 58 , 59 ., Additionally , when macrophages are tolerized by LPS , the role of NOD1 and NOD2 in cytosolic surveillance becomes more critical during infection 60 ., In our model , macrophages are activated with LPS and IFNγ prior to infection ., When activated macrophages are infected with L . monocytogenes , the induction of proinflammatory cytokines is XIAP-dependent , indicating that XIAP plays a more critical role in regulating the innate immune response to cytosolic pathogens in macrophages where the TLR pathway may be tolerized and an inflammatory gene expression program initiated ., We use these data to integrate XIAP into a cytosolic surveillance model whereby upon recognition of microbial ligands in the cytosol by innate immune sensors such as NOD2 , XIAP enhances association and function of signal transducers such as TAK1 and JNK 13 , 18 ., Recruitment of signaling molecules by XIAP upon NLR stimulation would potentiate signaling pathways activated by TLRs , leading to maximal proinflammatory cytokine production ., Apoptotic and microbial stimuli activate similar signaling pathways , but may lead to different outcomes ., Macrophages as innate immune effector cells can control microbial infection by secreting cytokines and other pro-inflammatory molecules or by carrying out programmed cell death 61 ., It has been hypothesized that when macrophages receive a strong inflammatory stimulus , they undergo apoptosis rather than secreting cytokines as a means of protecting the host 40 , 62 , 63 ., Although previous data implicated XIAP in modulating apoptosis , our data demonstrate that XIAP also has an important role in proinflammatory cytokine production ., However , we suggest that these two functions for XIAP may not be completely distinct , as the outcome of XIAP-dependent modulation of JNK and NF- κB pathways may depend on the quality and intensity of the stimulus 31 ., Additionally , the ability of XIAP to regulate innate immunity is likely cell type and context dependent , as we did not see reproducible XIAP-dependent transcriptional regulation in unactivated macrophages ., Future studies will determine which aspects of XIAP function contribute to immune signaling and elucidate the complex role of XIAP in the mammalian immune response ., Mice deficient in XIAP ( accession #U88990 ) were generated on a 129/Sv×129/SvJ background as previously described 20 ., The XIAP-deficient mice were backcrossed onto the C57Bl/6 background for more than 10 generations ., Six- to 12-week-old male XIAP-deficient mice or wild-type littermate controls were used for infection experiments ., All animals received humane care as outlined by the Guide for the Care and Use of Laboratory Animals ( University of Michigan Committee on Use and Care of Animals ) ., For cell culture infections , Listeria monocytogenes strains 10403S ( wild-type ) and hly− ( LLO− ) were inoculated into liquid brain-heart infusion ( BHI ) broth and incubated at 30°C overnight without shaking64 ., Prior to infection , L . monocytogenes cultures were washed and resuspended in PBS ., HKLM was prepared by incubating bacteria at 70°C for 1 h ., For animal infections , L . monocytogenes was grown to log-phase in BHI and aliquots were stored at −70°C ., For each experiment , a vial was back-diluted and allowed to grow to OD600 0 . 5 ., The bacteria were washed in PBS and diluted before injection ., Mice were injected intraperitoneally with 5×105 L . monocytogenes equivalent to 0 . 5 LD50 for infection by the intraperitoneal route in C57Bl/6 mice 65 ., The number of viable bacteria in the inoculum and organ homogenates was determined by plating 10-fold serial dilutions on Luria broth ( LB ) agar plates ., For evaluation of survival , animals were infected with 1×105 or 5×105 L . monocytogenes , and observed every 24 h post-infection ., For histology , the spleen and liver from infected mice were harvested at 48 hpi and fixed in 10% neutral buffered formalin ., Paraffin sections were prepared and stained with ApopTag by the Cancer Center Research Histology and Immunoperoxidase Lab at the University of Michigan ., Bone marrow macrophages were differentiated in DMEM supplemented with 20% heat inactivated FBS , 2 mM L-glutamine , 1 mM sodium pyruvate , 0 . 1% β-mercaptoethanol , and 30% L929 conditioned medium ., Bone marrow cells were cultured at an initial density of 107 cells per 150 mm non-tissue culture treated dish for 6 d , with fresh medium added at day 3 ., Cells were harvested with cold PBS without calcium and magnesium ., BMDM were activated overnight in 10 ng/ml LPS ( Sigma #L6143 ) and 10 ng/ml ( 100 U/ml ) interferon-γ ( Peprotech #315-05 ) ., Activated macrophages were infected with L . monocytogenes at an MOI of 10 , such that bacteria were observed in the cytosol in approximately 99% of the macrophages ., Peritoneal macrophages were harvested by peritoneal lavage ., Cells were pooled from two mice prior to plating ., For L . monocytogenes growth curves , cells were plated on coverslips at a density of 1 . 7×105 cells/ml in 24-well plates ., Macrophages were infected with L . monocytogenes for 0 . 5 h , washed 3 times with PBS , followed by addition of fresh medium with 50 µg/ml gentamicin ., At each time point , 3 coverslips were lysed in water and plated on LB agar plates for to determine CFU ., IL-6 ( R&D Systems ) , IL-1β ( R&D Systems ) , and TNF ( University of Michigan Cellular Immunology Core ) in the culture medium were measured by ELISA ., Where indicated , cells were treated for 30 min with TLR ligands as follows: MDP 10 µg/ml ( Bachem #4009623 ) , Pam3CSK4 2 µg/ml ( Invivogen #tlrl-pms ) , poly ( I:C ) 10 µg/ml , LPS 10 ng/ml ( Sigma #L6143 ) , flagellin 10 ng/ml ( Invivogen #tlrl-flic ) , imiquimod 5 µg/ml ( Invivogen #tlrl-imq ) , CpG DNA 1 µg/ml ( IDT CpG F 5′-TCCATGACGTTCCTGACGTT , CpG R 5′-AACGTCAGGAACGTCATGGA ) ., At 8 and 24 h post-treatment , supernatants were harvested for measurement of cytokines by ELISA ., Inhibition experiments were conducted as described above , except cells were treated with 20 µM JNK inhibitor , SP600125 ( Sigma #S5567 ) , or 10 µM ERK inhibitor U0126 ( Cell Signaling #9903 ) for 1 h prior to infection ., For nuclear and cytoplasmic fractionation , cells were lysed in NP-40 lysis buffer ( 50 mM Tris pH 8 . 5 mM EDTA pH 8 , 150 mM NaCl , 0 . 05% NP-40 Igepal , EDTA-free protease inhibitor cocktail Roche ) ., Nuclei were pelleted by centrifugation at 1 , 000 rpm for 5 min; the cytosolic fraction was further clarified by centrifugation at 14 , 000 rpm for 10 min ., Nuclei were washed and either resuspended in 2× SDS-PAGE lysis buffer for immunoblot or lysed for NF-κB ELISA by resuspension in nuclear lysis buffer ( 20 mM HEPES pH 7 . 9 , 400 mM NaCl , 1 mM EDTA , 10% glycerol , 0 . 1 mM DTT , EDTA-free protease inhibitor cocktail Roche ) and incubated at 4°C for 30 min ., Nuclei were flash frozen and used for NF-κB p65 ELISA analysis ( Stressgen EKS-446 ) ., BMDM were plated and activated overnight in 10 ng/ml LPS and 10 ng/ml interferon-γ ., Cells were infected for 30 min at an MOI of 10 , bacteria were removed by 3 washes with PBS , and fresh medium containing 50 µg/ml gentamicin added ., At 3 hpi , the medium was removed and spun to collect any non-adherent cells; the remaining cells were removed from the dish by incubating with ice-cold PBS without calcium and magnesium for 20 min at 4°C ., Cells were stained with Annexin V and propidium iodide according to the manufacturers protocol ( BD Biosciences #556420 ) ., Splenocytes were harvested from uninfected or L . monocytogenes infected mice ., BMDM were harvested from plates with ice cold PBS without Ca+ or Mg+ ., Cells were blocked with Fc block ( BD Pharmingen 553142 ) for 15 min on ice ., Cells were incubated in staining buffer ( PBS , 10% FBS ) with the indicated antibodies for 20 min on ice , followed by 3 washes in staining buffer ., When necessary cells were incubated with secondary antibodies in staining buffer on ice for 20 min , and washed 3 times in staining buffer ., Flow cytometric acquisition was performed on a FACSCanto ., The data was analyzed using FlowJo software ., The following antibodies were used: from BD Pharmingen; B220-PE ( 553089 ) , NK1 . 1-biotin ( 553163 ) , CD69-PE ( 553237 ) ; from Southern Biotech CD3 ( 1530-02 ) , Streptavidin-APC ( 7100-11L ) ., Whole cell lysates were generated by adding 2× SDS-PAGE sample buffer directly t

Introduction, Results, Discussion, Materials and Methods

The inhibitor of apoptosis protein ( IAP ) family has been implicated in immune regulation , but the mechanisms by which IAP proteins contribute to immunity are incompletely understood ., We show here that X-linked IAP ( XIAP ) is required for innate immune control of Listeria monocytogenes infection ., Mice deficient in XIAP had a higher bacterial burden 48 h after infection than wild-type littermates , and exhibited substantially decreased survival ., XIAP enhanced NF-κB activation upon L . monocytogenes infection of activated macrophages , and prolonged phosphorylation of Jun N-terminal kinase ( JNK ) specifically in response to cytosolic bacteria ., Additionally , XIAP promoted maximal production of pro-inflammatory cytokines upon bacterial infection in vitro or in vivo , or in response to combined treatment with NOD2 and TLR2 ligands ., Together , our data suggest that XIAP regulates innate immune responses to L . monocytogenes infection by potentiating synergy between Toll-like receptors ( TLRs ) and Nod-like receptors ( NLRs ) through activation of JNK- and NF-κB–dependent signaling .

During a bacterial infection , the innate immune response plays two critical roles: controlling early bacterial replication and stimulating the adaptive immune response to clear infection ., Host recognition of bacterial components occurs through pathogen sensors at the cell surface or within the host cell cytosol ., Inhibitor of apoptosis proteins ( IAPs ) have been recently implicated in immune regulation , but how IAPs contribute to immunity is incompletely understood ., Here , we show that X-linked IAP ( XIAP ) protects against infection by the cytosolic bacterial pathogen , Listeria monocytogenes , which causes severe disease in neonates and immunocompromised individuals ., We found that XIAP enhanced MAP kinase signaling in L . monocytogenes infected macrophages , a key innate immune effector cell ., Additionally , XIAP enabled synergy between cell surface and cytosolic bacterial sensors , promoting increased gene expression of proinflammatory cytokines ., Our findings suggest that IAPs are integral regulators of innate immune signaling , coordinating extracellular and intracellular responses against microbial components to control bacterial infection .

microbiology/immunity to infections, microbiology/innate immunity, immunology/innate immunity, infectious diseases/bacterial infections, immunology/immunity to infections

journal.pgen.1007756

Distinguishing genetically between the germlines of male monozygotic twins

Estimates of the incidence of human twinning range from <8 per 1000 live births in Asia to >18 per 1000 live births in Central Africa 1 ., This considerable geographic variation is mainly attributable to dizygotic ( DZ ) twinning and likely reflects the influence of social , environmental and genetic factors ., The incidence of monozygotic ( MZ ) twins , by contrast , is rather constant at approximately 4 per 1000 live births world-wide 2 ., MZ twins arise from a single zygote and therefore initially have the same genome , hence the layman’s term ‘identical’ twins ., With every 1 in 250 males being a MZ twin , instances in which the presence of a genetic ‘clone’ can hamper forensic case work are more than a theoretical possibility ., In fact , real life examples 3 include the 1999 case of a female student who was raped in Grand Rapids , MI , US ., Five years later , DNA analysis led to the identification of a potential perpetrator , who happened to have a MZ twin brother , and both the likely candidate and his brother denied their involvement ., In 2009 , Malaysia police in Kuala Lumpur arrested MZ twin brothers , one of whom was a drug driver caught in the act ., When the case came to court , however , there was reasonable doubt as to which twin was involved , and both men walked free ., The ostensible indistinguishability of MZ twins has also challenged the probative value of genetic testing in the context of paternity disputes ., For example , in 2007 , a woman in the US gave birth to a child after she had had sex with MZ twin brothers ., A DNA test identified both likely fathers with 99 . 9% probability but , owing to the nature of the genetic markers included , could not discriminate between the two men ., In the end , one brother was ruled the biological father on the grounds of other circumstantial evidence ., The coalescence of all cellular lineages in a single fertilization event is the basis of the generally held view that MZ twins are indistinguishable genetically ., However , after the twinning event ( i . e . , after the splitting of the original embryo ) , cell divisions along the lineages of one twin can be assumed to occur independently of the cell divisions in the other twin , at least regarding the acquisition of de novo mutations ., Therefore , given the number of cell divisions during embryonic development and the size of the human genome , there is a reasonable chance that any two tissue samples taken from MZ twins after birth may differ regarding the presence or absence of one or more post-twinning genetic alterations ., The potential utility of this phenomenon for discriminating between the germlines of male MZ twins was highlighted in a previous thought experiment 4 , suggesting an 83% probability that an offspring of a MZ twin carries at least one germline mutation ( henceforth termed ‘variant’ ) that can be detected in his sperm sample , but not in that of his twin brother ., This theoretical conjecture was corroborated empirically by Weber-Lehmann et al . 5 who carried out ultra-deep next generation DNA sequencing ( NGS ) and confirmatory Sanger sequencing in sperm samples of a MZ twin pair and a blood sample of a child of one of the twins ., Five de novo single nucleotide substitutions were found ( first by NGS and then confirmed by targeted Sanger sequencing ) in the father and child , but not the uncle ., Given the technical feasibility afforded by NGS , it is anticipated that genetic MZ twin discrimination will become a common forensic practice ., At the point when such testing is used in civil or criminal cases , however , the genetic expert will be required to quantify the evidential value of the laboratory results ., The likelihood ratio ( LR ) , which weighs the probability of the data under two alternative ( mutually exclusive ) hypotheses , is generally regarded as the most reasonable way to fulfil this requirement 6 ., In the aftermath of the proof-of-principle report 5 , Eurofins Genomics and Forensics Campus have been requested , by court order , to undertake similar analyses to distinguish between the germlines of MZ twin brothers ., Such cases involve either the assignment of one twin to a sperm sample collected in connection with a criminal offense , or a paternity dispute ., Under each scenario , DNA from saliva of the twins is used for genetic testing , i . e . the reference material is from a different tissue source than the forensic evidence ( sperm or peripheral blood , respectively ) ., Although this indirect approach may be less certain than a same-tissue comparison , most cases can be solved eventually because a sufficient number of discriminatory mutations are detected ., As was noted above , however , reporting the experimental evidence must also include quantification of its probative value by way of calculating LRs ., In the following , we describe and exemplify a newly developed mathematical approach to meet this demand ., In our mathematical considerations , we presume that the intended germline discrimination is based upon NGS data from saliva or blood of the MZ twins , labelled A and B , that were generated as described in the Lehmann-Weber et al . report 5 ., The same data reasonably will have served to identify potentially discriminating variants prior to the genetic analysis of the cells that derived from the germline of one of the twins ., These latter cells will comprise either a sperm sample or the paternal genomic complement of an offspring of that twin , genotyped by targeted Sanger sequencing rather than NGS for reasons of the relatively large amount of input DNA required and current costs ., Following common practice , the evidential value of the genetic data is quantified by means of the LR of the two mutually exclusive hypotheses “the cells came from the germline of twin A” ( hypothesis A ) and “the cells came from the germline of twin B” ( hypothesis B ) ., Thus , the possibility that the cells derived from the germline of a third man essentially was ruled out beforehand on the basis of external evidence such as , for example , sufficiently discriminating short tandem repeat ( STR ) profiles ., Typically , DNA sequence analysis will reveal a number , n , of de novo mutations ( most commonly single base pair substitutions ) that are prevalent in both the germline-derived cells and the somatic cells of one twin but not , or in only very small amounts , in the somatic cells of the other twin ., Other variants , particularly those found in only one sample , are not informative for germline discrimination and are therefore not considered any further ., Moreover , since the rate of recurrent somatic mutation is of the order of 2 . 7×10−9 per base pair per mitosis 7 , our mathematical considerations will rest on the presumption that every discriminating mutation traces back to one , and only one , molecular event during the development of the twins ( and their germlines ) ., Finally , we will assume that all discriminating variants arose before the embryo split to give rise to the twins ., Although most , if not all , discriminating variants will have arisen post-twinning in reality , this assumption is nevertheless reasonable because it is conservative in the sense that it systematically favours the alleged carrier of a discriminating variant , say twin A . Except for the remote possibility of a recurrent event in the germline of ( non-carrier ) twin B , postulating that the mutation occurred in twin A after twining would automatically rule out the possibility that the ( variant-carrying ) germline-derived cells came from twin B . For theoretical reasons , it appears reasonable to assume that the ( unknown ) frequency , pX , k , of the kth variant among the germ cells of twin X follows a beta distribution with parameters αX , k and βX , k ., In fact , the course of pX , k during embryonic development can be viewed as a realization of Polya’s urn model , where single balls are repeatedly drawn from an urn containing black and white balls , each time followed by the return of the same ball and another ball of the same colour to the urn ., The analogy works by equating the division of a variant-carrying cell with drawing a black ball ., Probability theory then tells us that , with time , the distribution of the relative frequency of black balls in the urn , and hence pX , k , converges to a beta distribution 8 ., The beta distributions employed here to characterise pX , k result from Bayesian updates of a single prior , with parameters α and β , of the variant frequency at the end of the pre-twinning period ., Updating is based upon the NGS read counts , vX , k and wX , k , of the variant and wild-type alleles , respectively , in the body tissue sample of twin X , i . e . the germline frequency of the kth mutation follows a beta distribution with parameters αX , k = α+vX , k and βX , k = β+wX , k ., Reasonable initial settings of parameters α and β can be derived from a consideration of the branching process underlying early embryonic development ., If the pre-twinning embryo has undergone a small number , m , of cell divisions , the frequency , p , at that stage of any post-fertilization mutation has expectation, E ( p ) =m2m+2−4, ( 1 ), and variance, Var ( p ) = ( 2m−12m−1−m22m−1 ) / ( 2m+4−16 ) ., ( 2 ), For a detailed derivation of formulas 1 and 2 , see Materials and Methods ., In >98% of cases , MZ twinning occurs before the end of the first week of pregnancy , and 25% of twinning events pre-date blastocyst formation at day 5 2 ., The rate of cell division during early human embryonic development is fairly constant , amounting to approximately one cycle per day 9 ., We may therefore reasonably assume that , on average , a pre-twinning embryo has undergone 0 . 25· ( 1+2+3+4+5 ) /5+0 . 75· ( 6+7 ) /2≈5 . 5 cell divisions ., Setting m = 5 . 5 results in E ( p ) = 3 . 11×10−2 and Var ( p ) = 1 . 80×10−3 ., When these two figures are equated to the expectation , α/ ( α+β ) , and variance , ( αβ ) / ( 1+α+β ) · ( α+β ) 2 , of a beta distribution , we obtain α = 0 . 4895≈0 . 5 and β = 15 . 2510≈15 ., Below , two types of scenarios requiring discrimination between the germlines of MZ twins will be considered , namely sperm sample donor identification and paternity testing ., In line with our proof-of-principle report 5 , the data underlying the likelihood calculations in both instances will be assumed to comprise, ( i ) the NGS counts of the discriminating variants as obtained in the somatic tissue samples from the twins and, ( ii ) the corresponding genotypes as determined in the germline-derived cells by targeted Sanger sequencing ., Identifying the male donor of a sample of germline-derived cells is a common issue in forensic casework , arising in paternity testing and in many criminal investigations , particularly in sexual offenses ., Our own experience shows that both instances may also include an alleged donor who has a monozygotic ( MZ ) twin brother , so that unambiguous donor identification by genetic analysis alone appears challenging , if not impossible ., With the advent of high throughput , time- and cost-efficient NGS technology , DNA sequencing of individual human genomes has become feasible and , in fact , relatively easy ., Thus , distinguishing between the genomes of MZ twins on the basis of post-twinning somatic mutations is practical ., This possibility has been demonstrated amply in studies targeting nuclear DNA 11 , 12 or mitochondrial DNA from peripheral blood 13 ., To our knowledge , however , successful discrimination between the germlines of male MZ twins has only been reported once before , namely by our group 5 ., Donor identification among MZ twin brothers is complicated by the fact that many legal systems do not provide for the enforcement of semen donation ., This limitation implies that the reference tissue used for the identification of potentially discriminating genetic variants ( usually blood or saliva ) differs from the target tissue ( sperm ) ., The opportunity for discriminating mutations to occur under this constraint is thus limited to the narrow developmental window separating the twinning event from the migration of the primordial germ cells into the yolk sac ., This time period comprises ≤15 cell divisions and an intermediate population bottleneck , so that the number of discriminating mutations detectable in somatic tissue ( i . e . blood ) is likely to be very small ., In fact , in the majority of cases that have been worked on so far , only two such mutations were observed , a number that is in very good agreement with its theoretical expectation of 1 . 78 derived in the thought experiment 4 that primed the proof-of-principle study 5 and the present work ., At first glance , one might assert that two discriminating variants seem inadequate because , normally , genetic trace donor identification or paternity testing requires consistent matching of genotypes of , for example , 10 or more short tandem repeat ( STR ) markers ., In the setting considered here , those same STRs are applied first to reduce the potential candidates for comparison to essentially the MZ twins and , thus , exclusion is required of only one alternative candidate donor , not many candidates ., Putting the possibility of a sample switch aside , in principle , a single discriminating variant would suffice to identify the source of the germline-derived cells in question ., Owing to issues of sample and processing quality , however , the evidential power of the approach undoubtedly would be bolstered by the presence of a least two discriminating variants ., Moreover , if these variants are located on different chromosomes , they can be assumed to be stochastically independent in the sense that the presence of one of the two underlying mutations in a given cell did not affect the probability of the presence of the other mutation ( see below ) ., Under this assumption , the joint likelihood of hypothesis X , given the genetic data , equals the product of the variant-specific likelihoods and the LR will reach a size sufficient for robust decision-making by the court or jury ., When reporting inclusionary results ( i . e . , matches or similar terminology ) , the genetic expert is usually required to quantify the evidential value of the data , and the LR is a generally accepted manner to do so ., The underlying mathematical theory is well established for classical forensic applications but has not been developed yet for cases involving MZ twins ., Therefore , we devised a formal framework for LR calculations by relating the unknown germline concentration of a genetic variant to its NGS read counts in somatic tissue , using Bayesian updating ., Our approach is based upon the assumption that all discriminating variants found in the alleged twin arose before twinning , which is highly conservative because it allows for the low-level presence of each discriminating variant in the other twin without invoking ( highly improbable ) recurrent mutations ., Moreover , we employed one and the same prior for updating the frequency distributions in both twins ., This strategy can easily be misunderstood as being anti-conservative because the high saliva frequency of a discriminating variant in the alleged twin may seem to require that the prior for the non-alleged twin is adjusted upwards ., This argument is invalid because the cells constituting the two post-twinning embryos result from sampling without replacement , not from sampling with replacement ., A high variant frequency in one twin therefore suggests a low variant frequency in the other ., Moreover , the variant-bearing cells likely cluster spatially in the pre-twinning embryo because they emerge from the continued duplication of neighbouring cells ., Therefore , the prior distribution rather should be adjusted downwards for the non-alleged twin , if anything , and adopting identical priors is conservative ., It should be noted that calculating likelihoods from updated beta priors alone implies that the LR is bound to converge to infinity with increasing sequence coverage ., Formally , this represents a logical inconsistency because recurrent mutation in the germline of the non-alleged twin remains a possible explanation of the sequence data if one or more discriminating variants are rare or even lacking from his somatic tissue ., For the level of sequence coverage pertinent in current real-world cases , this is not an issue because the beta-derived likelihoods are orders of magnitudes larger than the human germline mutation rate of 1 . 2×10−8 per base pair per generation 7 ., Therefore , the model-based numbers clearly would dominate any more complex likelihood definition accounting for the possibility of recurrent germline mutation as well ., However , this issue may be worth revisiting if and when advances in DNA sequencing technology indeed allow substantial increases of the sequence coverage ., As was noted above , discriminating variants on different chromosomes usually may be assumed to be stochastically independent ., It must be emphasized in this context that the validity of this assumption is not a matter of high or low population frequency of the variants , or of high site-specific mutation rates ( i . e . location of the variants in mutational hotspots ) ; even highly probably events can be independent ., Instead , stochastic independence between variants could be violated if the mutation rate during embryonic development varied between cell cycles in genome-wide fashion ., One conceivable mechanism by which such temporal clustering of de novo mutations may arise is exposition to an exogenous mutagen ., In this case , however , a higher overall prevalence of novel mutations would be expected to be detected in the twins ., In the cases that we have worked on so far , however , the sequencing results did not show any indications of such an increase ., In conclusion , NGS has rendered genetic discrimination between the germlines of MZ twins a realistic option , fit for practical forensic casework ., The few but important somatic mutations that arise early on during the development of twin embryos can now be identified with justifiable effort ., Although the experimental work required in connection with such cases may have been relatively expensive to date , the costs of NGS are likely to decrease in the future ., More so , our novel read count-based method of LR calculation provides a simple means to quantify the residual uncertainty about donorship in a highly conservative and , therefore , mutually acceptable way ., The current prevalence of MZ twin births 2 implies that , in ~1% of crime cases or paternity disputes , standard forensic DNA typing may turn out inadequate to resolve the potential donors ., From now on , however , most cases implicating one or the other MZ twin can be successfully addressed genetically ., Moreover , by highlighting the discriminatory power afforded by NGS in the special case of MZ twins , this and previous work 4 , 5 should also invigorate use of this technology in other forensic contexts such as , for example , the hitherto cumbersome kinship analysis of distant relatives ., Whilst the validity of the statistical model underlying our work may occasionally require reconsideration , depending upon individual circumstances , it should represent a scientifically sound , simple and viable basis for the mathematical workup of practical cases ., To put the approach in perspective , we refer to the famous quote from British statistician George E . P . Box: “Since all models are wrong , the scientist cannot obtain a ‘correct’ one by excessive elaboration . On the contrary , following William of Occam he should seek an economical description of natural phenomena . ” 14, The present work was motivated by the requirement to analyse genetic data generated on official order by investigating prosecutions or family courts ., All individuals affected in such cases are invariably informed about the reason and scope of the analyses ., Neither the genetic data nor the analysis results are disclosed to third parties ., In order to avoid ethical conflict , the authors and the editors of PLoS Genetics therefore agreed that no genetic data or other details from real forensic casework are publicized in connection with the present work ., Our mathematical derivations start out from a zygote carrying two identical alleles at the ( autosomal ) genetic locus of interest ., During each round of cell division , the number of alleles present in the developing embryo is doubled , so that a variant arising from a de novo mutation during the kth cell cycle has frequency 1/2k+1 among the 2k+1 homologous chromosomes present in the daughter cells ., Moreover , the kth cycle comprises the synthesis of 2k+1 nascent chromosomes , each representing a target of possible mutation ., Let p be the frequency of a variant that originated from one of the first m cell cycles ., The above considerations imply that , after the mth cycle ,, P ( p=12k+1 ) =2k+1∑i=1m2i+1=2k∑i=1m2i=2k−12m−1, for 1≤k≤m ., This leads to, E ( p ) =∑k=1m12k+1⋅2k−12m−1=12m−1⋅∑i=1m122=m2m+2−4, for the expected value of p , and, Var ( p ) =∑k=1m122k+2⋅2k−12m−1−m216 ( 2m−1 ) 2=∑k=0m−112k+4⋅12m−1−m216 ( 2m−1 ) 2, =116 ( 2m−1 ) ⋅∑k=0m−1 ( 12 ) k−m22m−1=116 ( 2m−1 ) ⋅2m−12m−1−m22m−1, for the variance of p .

Introduction, Results, Discussion, Materials and methods

Identification of the potential donor ( s ) of human germline-derived cells is an issue in many criminal investigations and in paternity testing ., The experimental and statistical methodology necessary to work up such cases is well established but may be more challenging if monozygotic ( MZ ) twins are involved ., Then , elaborate genome-wide searches are required for the detection of early somatic mutations that distinguish the cell sample and its donor from the other twin , usually relying upon reference material other than semen ( e . g . saliva ) ., The first such cases , involving either criminal sexual offenses or paternity disputes , have been processed successfully by Eurofins Genomics and Forensics Campus ., However , when presenting the experimental results in court , common forensic genetic practice requires that the residual uncertainty about donorship is quantified in the form of a likelihood ratio ( LR ) ., Hence , we developed a general mathematical framework for LR calculation , presented herein , which allows quantification of the evidence in favour of the true donor in the respective cases , based upon observed DNA sequencing read counts .

In many instances of practical forensic casework , particularly when connected to sexual assault , genetic analysis is carried out to identify the likely donor of a sperm sample left at the crime scene ., The experimental and statistical methodology for such investigations is well established ., In cases involving monozygotic ( MZ ) twin suspects , however , the procedure is hampered by the fact that the two individuals usually coincide for the genetic markers tested ., One way to overcome this problem is to use the latest DNA sequencing technology to undertake a genome-wide search for those few mutations that occur during early embryonic development and hence allow distinguishing between MZ twins in later life ., Following this approach , the first cases of criminal sexual offense have been worked on successfully by Eurofins Genomics and Forensics Campus , leading to the identification of sperm sample donors from saliva reference samples taken from MZ twin suspects ., As a matter of principle , however , the residual uncertainty of the experimental results needs to be evaluated and reported as well ., Therefore , we developed a novel mathematical framework to quantify the evidential power of the genetic data in cases attempting to identify MZ twin donors , based upon comprehensive DNA sequencing ., Moreover , we demonstrate that the same mathematical method can be used to resolve paternity disputes involving alleged fathers who have MZ twin brothers .

sequencing techniques, medicine and health sciences, clinical laboratory sciences, body fluids, cell cycle and cell division, cell processes, social sciences, saliva, twins, germ cells, next-generation sequencing, developmental biology, mutation, genome analysis, molecular biology techniques, embryos, sperm, law and legal sciences, research and analysis methods, embryology, genomics, animal cells, molecular biology, somatic mutation, diagnostic medicine, cell biology, anatomy, physiology, genetics, transcriptome analysis, biology and life sciences, cellular types, computational biology, dna sequencing, forensics

journal.pbio.1000015

Cell Lineages and the Logic of Proliferative Control

In biology , “control” is often used interchangeably with “regulation , ” but in engineering , control has a precise meaning: It refers to the strategies that enable a system to achieve desired ends , usually in a robust manner ., To begin talking about the control needs of growing tissues and organs , we must first ask what are the “desired” ends , and to what kinds of uncertainties and perturbations must growth and differentiation be robust ?, Perhaps the most obvious objective of a growth control system is to reach and maintain a specified size ., Sizes of organs such as the brain , for example , are genetically specified within narrow tolerances ( e . g . , 16 ) ., Moreover , self-renewing organs , such as the liver , seem to “remember” their appropriate sizes , as they accurately regenerate to their original sizes following even massive lesions 17 ., The fact that many genetic alterations can affect final organ size ( e . g . , 18 , 19 ) suggests that there are diverse molecular pathways by which size may be regulated ., A less obvious performance objective is control of growth rate ., Consider , for example , a self-renewing tissue that maintains constant size by balancing continual cell death with cell production ., Following an injury in which differentiated cells are destroyed , if there is no adjustment in cell production , those cells will be replaced only at the same ( often very slow ) rate at which they previously turned over ., In regenerating tissues , however , it is common to observe a dramatic increase in proliferation following injuries , with rapid restoration of tissue morphology and size 17 , 20 , 21 ., Even in tissues that do not regenerate , control of growth rate is likely to be important during development , so that the changing sizes of different organs are properly coordinated with each other ., Other possible targets of control are the proportions of cell types in a tissue ., For example , in a branched lineage ( one with more than one terminal-stage cell type ) a fixed ratio of end products may be important for tissue or organ function 22 ., In lineages that operate continuously , it may also be desirable to ensure that stem and progenitor cells ( which do not usually contribute directly to tissue function ) are not too great a fraction of the tissue mass ., How difficult should it be for tissues to achieve such objectives ?, With control , the difficulty of the task depends upon the magnitude of the perturbations that are normally encountered ( e . g . , genetic and/or random effects on cell behavior , environmental fluctuations , injury , and disease ) ; the sensitivity of the systems behavior to those perturbations; and the level of imprecision in output that is acceptable ., In recent years , increasing attention has been focused on the control challenges of biological networks , including those associated with metabolism , intracellular signaling , and gene regulation ( e . g . , 23–26 ) ., Superficially , cell lineages look a great deal like these other kinds of pathways ( Figure 1 ) ., Yet the components of lineages—cell stages—do not just transmit signals or material from one to another; they typically undergo autonomous , exponential expansion at the same time ., This imparts a characteristic volatility to lineage dynamics that no doubt poses challenges for control ., Given such challenges , it would not be surprising if the control of tissue and organ growth necessitates control strategies unlike those encountered elsewhere in biology ., Here , we take steps toward identifying such strategies ., One way to identify the control needs of a system , and the strategies that may be used to address those needs , is to build models and explore their behavior ., Figure 2A is a general representation of an unbranched cell lineage that begins with a pool of stem cells , ends with a postmitotic cell type , and possesses any number of transit-amplifying progenitor stages ., If cells at each stage are numerous , and divisions asynchronous , then the behavior of such a system over time can be represented by a system of ordinary differential equations ( Figure 2B ) with two main classes of parameters ., The v-parameters quantify how rapidly cells divide at each lineage stage ( in particular , v = ln2/λ , where λ = the duration of a cell cycle ) ., The p-parameters quantify the fraction of the progeny of any lineage stage that remains at the same stage ( i . e . , 1-p is the fraction that differentiates into cells of the next stage ) ., Thus p may be thought of as an amplification , or replication , probability ., As each lineage stage has its own v and p , we use subscripts to distinguish them ., Let us refer to the number of terminal-stage cells at any point in time as the output of a lineage system ., From Figure 2B , we can see that a system is not stable—over time the output increases without bound—if pi > 0 . 5 for any, i . In contrast , if pi < 0 . 5 for all i , stem and progenitor cells eventually run out , and the production of new terminal-stage cells stops ., Provided terminal-stage cells do not die at an appreciable rate , such a system will reach a final state with a fixed number of terminal-stage cells ., Finally , if p0 = 0 . 5 , and pi < 0 . 5 for i > 0 , then the system will eventually produce terminal-stage cells at a constant rate ., If such cells die or are shed with a constant probability per unit time ( represented in Figure 2B by the rate constant d ) , then the output will approach a steady state , the solution for which is given in Figure 2C ( solutions for certain cases of final-state behavior are also given in Protocols S1–S3 , sections 5 and 6 ) ., The result in Figure 2C describes a steady state that is quite sensitive to the systems parameters ., For example , output is proportional to the number of stem cells ( χ0 , which remains constant at its initial value ) and the rate of stem cell division ( v0 ) , and inversely proportional to the rate of terminal-stage cell death ( d ) ., Output varies even more sensitively with the pi ., For example , increasing the value of a pi from 0 . 45 to 0 . 4725—a 5% change—necessarily produces a 74% increase in the output of terminally differentiated cells ., In engineering , parameter sensitivity is usually quantified as the fold change in output for a given fold change in the parameter ( equivalent to the slope of a log-log plot of output vs . parameter ) ., Thus , a linear relationship corresponds to a sensitivity of 1 ( directly proportional ) or −1 ( inversely proportional ) ., From Figure 2C , we may calculate that the sensitivity of the output to any pi is pi/ ( 1 − 3pi + 2pi2 ) , which for pi < 0 . 5 is always greater than 1 , and grows without bound as pi approaches 0 . 5 ., In well-regulated biological systems , parameter sensitivities ≥ 1 tend to be undesirable , since genetic or environmental variability can easily cause several-fold changes in the biological processes ( levels of proteins , cell growth rates , etc . ) that underlie parameters 27–29 ., A system that cannot compensate for such variation is justifiably considered fragile ( the opposite of robust ) ., Arguably , the most severe fragility of the system in Figure 2 is the constraint placed on the stem cell replication probability: p0 must be exactly 0 . 5 for a non-zero steady state to exist ( effectively , the systems sensitivity to p0 is infinite ) ., This is simply another way of stating that , unless exactly half of all stem cell progeny are stem cells , lineages eventually either go extinct or explode ., Meeting this constraint can be achieved by having every stem cell undergo perfect asymmetric divisions , but that does not seem to be what normally happens ., Rather , individual stem cells behave stochastically , sometimes giving rise to two , one , or zero stem cells ( e . g . , 6 , 8 , 30 ) ., For the exact condition p0 = 0 . 5 to arise as a population average , when such behavior is not a cell autonomous imperative , is an extraordinary—and yet poorly understood—feature of stem cell systems ., The idea that negative feedback is used to regulate tissue size and enhance regeneration is an old one ., Over 40 y ago , Bullough 31 introduced the term chalone to refer to secreted factors that inhibit growth of the tissues and organs that secrete them ., When a tissue is injured or partially removed , reduction in chalone levels would thus result in an up-regulation of tissue production ., The view that chalones are secreted factors was supported by in vitro experiments , and by experiments with parabiotically joined pairs of animals in which partial hepatectomy in one animal led to liver cell proliferation in the other 32 ., Although many of the original , in vitro–defined chalones have yet to be fully characterized , genetic studies in the 1990s demonstrated that growth and differentiation factor 8 ( GDF8 ) /myostatin ( Mstn1 , MGI:95691 ) , a member of the transforming growth factor β ( TGFβ ) superfamily of secreted signaling molecules , is specifically expressed by striated muscle cells ( the terminal-stage cells of muscle lineages ) , inhibits the production of muscle , and when genetically eliminated from animals , results in the production of supernumerary muscle cells and an increase in muscle mass 33 ., Subsequently , GDF11 ( MGI:1338027 ) —a close relative of GDF8—was shown to be produced specifically by cells of the neuronal lineage of the mouse OE , and to provide feedback to inhibit the production of neurons ( olfactory receptor neurons; ORNs ) in that system 34 ., Animals deficient in GDF11 also develop supernumerary ORNs ., In recent years , factors that exert negative feedback on growth have been described for many other tissues , including skin , liver , bone , brain , blood cells , retina , and hair ( Table S1 ) ., Many of these factors turn out to be members of the TGFβ superfamily , especially the TGFβ/activin branch of that superfamily 35 ., The OE of the mouse is a particularly useful system for studying lineage progression and feedback: It is continually self-renewing; its lineage stages are well defined; its cells can be studied in tissue culture; and it can be manipulated in vivo through genetic , chemical , or surgical means 36–38 ., The OE neuronal lineage consists of a stem cell ( which expresses Sox2 MGI: 98364 , a gene encoding an SRY-box transcription factor ) , that gives rise to cells that express the proneural gene Mash1 ( Ascl1 , MGI: 96919 ) , which in turn give rise to cells that express another proneural gene , Neurogenin1 ( Ngn1; Neurog1; MGI: 107754 ) , which in turn give rise to cells that exit the cell cycle and differentiate into ORNs ., Recent data have raised the possibility that the Sox2+ and Mash1+ stages are not truly distinct , but rather are interchangeable states of the stem cell ( K . K . Gokoffski et al . , unpublished data ) ., However , the Ngn1+ cell—which is usually referred to as the Immediate Neuronal Precursor , or INP—is clearly a distinct transit-amplifying cell stage ( Figure 3A; 34 , 39 , 40 ) ., The INP appears to give rise solely to ORNs ,, i . e . , it does not represent a lineage branch point 39 ., It is therefore interesting that the feedback actions of GDF11 seem to be directed specifically at INPs 34: In vitro , GDF11 completely , but reversibly , arrests INP divisions , yet it has no effect on proliferation of Mash1+ or Sox2+ cells ., In vivo , the increase in neuronal number observed in Gdf11−/− mice is accompanied by an increase in INPs , but not in Mash1+ or Sox2+ cells ., These data imply that GDF11 regulates tissue size by inhibiting the proliferation of a committed transit-amplifying cell ., Because GDF11 can slow and even arrest INP divisions , it is natural to model GDF11-mediated negative feedback as an increase in the cell-cycle length of the INP ( Figure 3B ) ., Indeed , there is abundant literature showing that GDF11 , GDF8 , and other TGFβ superfamily members slow rates of progression through the cell cycle , at least in part by inducing cyclin-dependent kinase inhibitors 34 , 41–44 ., Increasing the INP cell-cycle length is equivalent to decreasing its v-parameter , v1 ( Figure 3B ) ., Unfortunately , the result in Figure 2C states that the steady state outputs of lineage systems are independent of all v except for that of the stem cell ( v0 ) ., This makes intuitive sense: if one decreases the division rate of an intermediate-stage cell in a lineage , the unchanged influx of cells from the previous lineage stage will cause its numbers to rise proportionately ., From the standpoint of the lineage output , the two effects will cancel ., Apparently then , having GDF11 ( or any other factor ) feed back onto the INP cell division rate can be of no use in controlling the steady state level of ORNs ., Could such feedback serve a function related to some other performance objective , such as rate control ?, As mentioned earlier , without control , lineage systems would be expected to return to steady state after a perturbation ( i . e . , regenerate ) with a time scale similar to that over which terminal-stage cells normally turn over ., In principle , feedback onto the cell division rate of a lineage intermediate could improve this ., However , as explained below , the utility of this strategy turns out to be very limited: Figure 3C shows a simulated regeneration experiment in which output , via GDF11 , feeds back onto v1 ., At the start of the experiment , all ORNs are synchronously destroyed , and the time course of the return to steady state is followed ( this type of perturbation can be produced experimentally by transecting the olfactory nerve or removing one or both olfactory bulbs of the brain 45 ) ., For comparison , the figure also shows what the time course of the return to steady state would be in the absence of feedback ( dashed line ) ., From Figure 3C , we can see that feedback enables the system to regenerate faster , but we also observe a very high proportion of INPs ( they are virtually as numerous , at steady state , as ORNs ) ., It turns out that speeding up regeneration requires a large feedback gain ( the parameter h in Figure 3B ) , which in turn drives down steady state ORN numbers ( relative to other cells ) ., If we define progenitor load as the percentage of the entire tissue that is composed of progenitors ( stem cells plus INPs ) , we find that requiring the steady state progenitor load to be less than 50% limits any improvement in regeneration speed to about 3 . 2-fold; restricting progenitor load to 10% drops this value to about 2 . 6-fold ( Figures S16 and S17 in Protocols S1–S3 ) ., In fact , experimental data indicate that the progenitor load in the OE is below 10% 46–48 ., There is another cost of achieving fast regeneration through feedback on v1: the lower the progenitor load , the more necessary it becomes to use values of p1 that are perilously close to 0 . 5 ( i . e . , nearly half the output of INPs needs to be more INPs; Figures S16 and S17 in Protocols S1–S3 ) ., As discussed earlier , when p-parameters are close to 0 . 5 , system output becomes extremely sensitive to small variations in those parameters ( and thus very fragile ) ., All told , feeding back onto the rate at which INPs divide does not seem to be a particularly good control strategy ., We wondered whether GDF11 might do a better job if it fed back onto a different parameter of INP growth: p1 , the replication , or amplification , probability ., Analysis of a model of this sort of feedback ( Figure 3D ) reveals several remarkable things: First , with feedback on p1 , the constraint p1 ≤ 0 . 5 goes away: Any INP replication probability allows for establishment of a steady state ., Second , the fragility of the steady state output can be substantially reduced ., In particular , sensitivity to the number of stem cells , the rate of stem cell division , and the death rate of terminally differentiated cells can be made arbitrarily small for appropriate parameter choices ., Sensitivity to p1 can also be greatly reduced ( to values <1 ) , even if p1 is large ( Figures S1 and S2 in Protocols S1–S3 ) ., Finally , such a system can mount explosive regeneration after a perturbation ., In some cases , the return to steady state can be as much as 100 times faster than in the absence of feedback ., Furthermore , this can be accomplished without the need for a high progenitor load ., Figure 3E shows this behavior for a particularly effective set of parameters ., Notice how , in response to an acute loss of terminal-stage cells ( ORNs ) , transit-amplifying cells ( INPs ) undergo a rapid , but transient , increase in number , following which , terminal-stage cells are restored rapidly to values close to steady state ., This sort of behavior closely parallels what is seen in the OE following olfactory bulbectomy ( in which ORN degeneration is induced by olfactory bulb removal ) : a transient upsurge in progenitor cell numbers , followed by a wave of neuronal production 20 , 40 , 46 , 49–51 ., The fact that feedback aimed at p1 can , in theory , produce more useful and realistic behaviors than feedback aimed at v1 , raised the possibility that the actual target of GDF11 might be p1 , and not v1 , as initially thought ., To resolve this issue , we carried out tissue culture experiments in which mouse OE progenitor cells were pulse-labeled with 5-bromo-2-deoxyuridine ( BrdU; to label cells undergoing division ) , and evaluated at successive times thereafter to determine when the progeny of dividing cells acquire immunoreactivity for NCAM , a marker for terminally differentiated ORNs ., As shown previously , most dividing cells in these cultures are INPs , and their cell cycle length is about 17 h 39 ., If all INP divisions result in production of ORNs , the acquisition of NCAM immunoreactivity by all BrdU-labeled cells should occur after sufficient time to progress through the rest of S-phase , G2-phase , M-phase , and however long it takes for NCAM levels to rise above the threshold of detection ., If some INPs replicate , however , then a fraction of labeled cells will not express NCAM until one cell cycle ( ∼17 h ) later ( if the replicating fraction is high enough , some progeny will go through several cell cycles before acquiring NCAM immunoreactivity; cf . 39 ) ., Accordingly , delay in the onset of NCAM expression can be used as a measure of the INP replication probability ., Figure 4 shows the effect of GDF11 ( added to the culture medium 12 h prior to BrdU labeling ) on acquisition of NCAM expression by BrdU pulse-labeled cells ., In Figure 4J , data for two different “chase” periods are graphed ., In the absence of GDF11 , about 60% of BrdU-labeled cells become NCAM-positive within 18, h . In the presence of low levels of GDF11 , this percentage rises as high as 75% , then falls again at high concentrations of GDF11 to less than 10% ., The increase in neuronal differentiation in response to low levels of GDF11 documents that GDF11 indeed suppresses INP replication ( i . e . , it lowers p1 ) ., The fact that this increase gives way to a large decrease in neuronal differentiation at high GDF11 levels is most likely due to the additional effect of GDF11 on the rate of cell cycle progression: As the INP cell cycle is progressively lengthened , one would expect that an 18-h chase period would cease being long enough to allow BrdU-labeled cells to go on to differentiate ., This would lead to a sharp drop-off in the percentage of BrdU-labeled cells that acquire NCAM expression , but with longer chase times ( e . g . , 36 h ) , this effect would be overcome ., That is indeed what is observed ( Figure 4J ) ., A numerical simulation of the experiment , in which GDF11 negatively regulates both p1 and v1 , replicates both qualitative and quantitative features of the experimental data ( Figure 4K; Protocols S1–S3 , section 10 ) ., Having the output of the OE lineage feed back onto p1 seems to be an effective strategy for meeting two control objectives: steady state robustness ( low sensitivity to stem cell number χ0 , cell division rates v0 , and v1 , and the death rate constant of the terminal-stage cell d ) and rapid regeneration ., But the ability to meet each objective separately does not guarantee that both can be met together ( i . e . , for the same sets of parameters ) ., As it turns out , the two strategies are largely incompatible ., Numerical exploration of the parameter space shows a strong negative correlation between robustness and enhancement of regeneration ( Figure 5A ) ., Cases for which the sensitivity to χ0 , v0 , or d is less than 0 . 4 ( i . e . , a 2-fold change in parameter will cause ≤32% change in output ) , generally do not exhibit acceleration in regeneration speed exceeding approximately 8-fold ., In fact , this result is skewed by cases in which regeneration speed goes from extremely slow ( in the absence of feedback ) to merely very slow ., If one restricts the analysis to cases in which regeneration from complete loss of terminal-stage cells is 80% complete in fewer than 29 transit-amplifying cell cycles ( ∼20 d for INPs ) , then to achieve parameter sensitivities less than 0 . 4 , the best possible improvement in regeneration speed is less than 2-fold ( Figure 5A and 5B ) ., Upon closer inspection , other unfortunate tradeoffs can be seen: For the cases in Figure 5A , improvement in regeneration speed was calculated by simulating a complete loss of terminal-stage cells and then measuring the return to steady state ., If we use a milder perturbation ( a 75% loss of terminal-stage cells ) , but otherwise the same parameters , the return to steady state is , unexpectedly , quite slow ( Figure 5C ) ., The need to sustain injury that is massive before regeneration can be rapid hardly seems like a good strategy for an organism in the real world ., To define the conditions under which this phenomenon occurs , we calculated , for all the cases in Figure 5A , the ratio of two regeneration times: the time for regeneration from a 100% perturbation , and the time for regeneration from a 75% perturbation ., In Figure 5D , this value ( “speed ratio” ) is plotted against fold improvement in regeneration speed ( for the 100% perturbation , compared with no feedback ) ., The data show that the speed of regeneration following massive injury cannot be improved by more than about 3-fold , without sacrificing the speed of regeneration following less-than-massive injury ., Altogether , tradeoffs among regeneration speed , sensitivity to parameters , and sensitivity to initial conditions make the control strategy of having GDF11 feed back onto p1 less attractive than it originally seemed ., Analysis of cases in which GDF11 inhibits both p1 and v1 ( which corresponds most closely to what GDF11 does in vitro; Figure 4J and 4K ) shows some improvement in the tradeoff between regeneration speed and parameter sensitivity , but the effect is not dramatic ( Figure S18 in Protocols S1–S3 ) ., Accordingly , we wondered whether additional control elements might still be missing ., As mentioned in Table S1 , many feedback inhibitors of tissue and organ growth belong to the TGFβ superfamily of growth factors , with those of the TGFβ/activin branch ( which signals through the intracellular proteins Smad2 and Smad3 ) being the most highly represented ., Recently , we found that activinβB ( Inhbb; MGI: 96571; hereafter referred to simply as “activin” ) is highly expressed in the OE and , like GDF11 , has growth-inhibitory effects on the neuronal lineage ., Unlike GDF11 , however , activins effects are aimed specifically at the Sox2+ and Mash1+ populations , and not at INPs ( K . K . Gokoffski et al . , unpublished data ) ., This implies that two feedback loops exist in the OE , one aimed at stem cells , and one aimed at transit-amplifying cells ( Figure 5E ) ., Like GDF11 , activin could potentially feed back onto a v-parameter ( namely v0 , the rate of stem cell division ) or a p-parameter ( namely p0 , the stem cell replication probability ) , or both ., For technical reasons , a pulse-chase experiment similar to that in Figure 4 cannot be performed to sort this out ., However , we infer that feedback onto p0 must occur , because Sox2+ and Mash1+ populations are markedly expanded in the OE of ActβB−/− mice ( K . K . Gokoffski et al . , unpublished data ) ., If activin only regulated v0 , loss of activin would result in stem cells that cycle faster , but it could not increase their numbers ., Interestingly , when we add the feedback effects of both activin and GDF11 into the equations for the behavior of the ORN lineage , the expression for the steady state value of ORNs becomes very simple: ( 2p0 − 1 ) /j , where j is the feedback gain for activin ( Protocols S1–S3 , section 4 ) ., This constitutes a dramatic improvement in robustness—the system will , at steady state , always produce the same number of terminal-stage cells regardless of how many stem cells it starts with , how fast stem cells divide , or how quickly terminal-stage cells are lost ., Perhaps even more strikingly , the problematic constraint that the stem cell population must intrinsically “know” to replicate exactly half the time ( p0 = 0 . 5 ) vanishes ., As long as p0 > 0 . 5 , feedback automatically ensures that the stem cell population behaves in the necessary way ., All of these improvements in steady state control come solely from the single feedback loop of system output onto p0 ., When such a loop is in place , however , feedback onto other p- and v-parameters can have additional useful effects: Consider , for example , the matter of regeneration speed , which we previously found could be increased through feedback onto p1 or v1 , but only by sacrificing robustness , low progenitor loads , or the ability to regenerate quickly from a variety of initial conditions ( Figures 3C and 5A–5D ) ., When feedback is directed solely at stem cells , we also fail to achieve good performance: Feedback onto p0 hardly improves regeneration speed at all ( Figure S19 in Protocols S1–S3 ) , and although feedback onto p0 and v0 together can produce fast rates of regeneration ( Figure S21 in Protocols S1–S3 ) , those rates still show a very sensitive dependence on initial conditions ( Figure S22 in Protocols S1–S3 ) ., In contrast , when feedback is directed at both stem and transit-amplifying cell stages—i . e . , the arrangement that actually occurs in the OE—it becomes possible to achieve very rapid regeneration , with low progenitor loads , from almost any starting conditions ., This includes conditions in which variable numbers of stem , transit-amplifying , or terminal-stage cells are depleted ., Figure 5F shows an example of such a case ., Not only is such performance possible , it occurs over a substantial fraction of the parameter space ( that is , a substantial fraction of randomly chosen sets of parameters meet all of these performance objectives ) ., Figure 6A shows graphically how , as feedback loops are added one at a time , good control ( robustness , stability , low progenitor load , and fast regeneration from a variety of conditions ) is found over an increasing fraction of the parameter space ( exploring wide ranges on all parameters ) ., In evaluating the magnitude of this effect , it should be noted that fractions of parameter space in the range of 0 . 1%–1 . 5% are remarkably high , given the numbers of parameters in each model ( cf . 52 ) ., For example , when there are eight independent parameters ( as there are when feedback is directed at p0 , v0 , p1 , and v1 ) , good performance over 0 . 1% of the parameter space means that the average parameter value “works” over 42% ( ∼0 . 0011/8 ) of its range ., In Figure 6 , most parameters were explored over three orders of magnitude ( i . e . , they were randomly selected from a log-uniform distribution with a 1 , 000-fold range ) , so for such cases , 42% means that the average parameter can be varied over an 18-fold range ( 1 , 0000 . 42 ) without loss of good control ., What is the significance of a control system that works over a large portion of its parameter space ?, It means that the output of the system can be adjusted ( through changes to the parameters ) without the control strategy itself being jeopardized ., From a biological perspective , this means that the system is evolvable , a feature we should expect to observe in most biological control systems 53 ., So far , we have said much about the cell stages and processes that are targets for feedback in cell lineages , and little about the quantitative details of feedback signals ., In Figures 3 and 5 , feedback was modeled using Hill functions; these are natural choices for the actions of secreted growth factors , since saturable binding of ligands to receptors is usually well described by them 54 ., Hill functions typically employ a parameter n , the Hill coefficient , to fit dose-response relationships that are positively ( n > 1 ) or negatively ( n < 1 ) cooperative ., In Figures 3 , 5 , and 6A , a Hill coefficient of 1 was used , but more detailed exploration of the two-loop feedback system ( with feedback on p0 , v0 , p1 , and v1 ) shows that system performance increases steadily as n goes from 0 . 5 to 2 ( Figure 6B and 6C ) ., This makes intuitive sense if we consider that high values of n make Hill functions more switch-like ., In the limit of a perfect switch ( infinite n ) , the drive for increased growth would be zero when output is at the desired value , yet maximal when output is even slightly below the desired value ., Such a strategy clearly achieves the fastest possible regeneration following a perturbation ., In biology , dose-response relationships that are fit by Hill coefficients other than 1 arise for a variety of reasons besides biochemical cooperativity; these include buffering , competition , feedback , and distributed multistep reactions 55–57 ., Generally speaking , Hill coefficients quantify the sensitivity of output to input ( in the limit of high input , the Hill coefficient and the engineering definition of sensitivity are equivalent ) ., Thus , in our models of feedback in the OE , Hill coefficients near 1 mean that the amount of activin and GDF11 signaling in stem cells and INPs ( respectively ) is roughly proportional ( over some range ) to the number of cells producing activin and GDF11 ( i . e . , the size of the tissue ) ., It occurred to us that this situation—feedback proportional to tissue size—might not be so easy for tissues to achieve ., As a tissue grows in size , one can certainly envision the total amount of material it produces increasing proportionally , but it is the concentrations—not the amounts—of factors like GDF11 and activin to which cells respond ., How the concentrations of secreted ligands change as tissues grow turns out to depend both on issues of geometry ( tissue shape and boundary properties ) , and issues of cell biology ( rates of ligand capture and turnover ) ., For example , consider a hypothetical tissue surrounded by a boundary across which macromolecules cannot diffuse ., In this case , a secreted protein produced everywhere in the tissue should reach a steady state concentration determined by the balance between production and local degradation ., If the tissue doubles in size , it will make twice as much of the protein , but distribute it over twice the volume ., The result will be no change in concentration ., In a truly “closed” tissue , secreted molecules cannot be used as part of a strategy for growth control ., Fortunately , epithelia , such as the OE , are not closed systems ., Although tight junctions between epithelial cells prevent escape of molecules from the apical surface , there appears to be little or no impediment to diffusion across a basal lamina into the underlying connective tissue stroma 58 ., Within such a geometry , we may use approaches developed for the analysis of morphogen and signaling gradients 59–62 to calculate expected intraepithelial distributions of secreted molecules ( Protocols S1–S3 , section 11 ) ., The res

Introduction, Results, Discussion, Materials and Methods

It is widely accepted that the growth and regeneration of tissues and organs is tightly controlled ., Although experimental studies are beginning to reveal molecular mechanisms underlying such control , there is still very little known about the control strategies themselves ., Here , we consider how secreted negative feedback factors ( “chalones” ) may be used to control the output of multistage cell lineages , as exemplified by the actions of GDF11 and activin in a self-renewing neural tissue , the mammalian olfactory epithelium ( OE ) ., We begin by specifying performance objectives—what , precisely , is being controlled , and to what degree—and go on to calculate how well different types of feedback configurations , feedback sensitivities , and tissue architectures achieve control ., Ultimately , we show that many features of the OE—the number of feedback loops , the cellular processes targeted by feedback , even the location of progenitor cells within the tissue—fit with expectations for the best possible control ., In so doing , we also show that certain distinctions that are commonly drawn among cells and molecules—such as whether a cell is a stem cell or transit-amplifying cell , or whether a molecule is a growth inhibitor or stimulator—may be the consequences of control , and not a reflection of intrinsic differences in cellular or molecular character .

Many tissues and organs grow to precise sizes and , when injured , regenerate accurately and rapidly ., Here , we ask whether the organization of cells into lineages , and the feedback interactions that occur within lineages , are necessary elements of control strategies that make such behavior possible ., Drawing on mathematical modeling and the results of experimental manipulation of the mouse olfactory epithelium , we show that performance objectives , such as robust size specification , fast regeneration from a variety of initial conditions , and maintenance of high ratios of differentiated to undifferentiated cells , can be simultaneously achieved through a combination of lineage structures , signaling mechanisms , and spatial distributions of cell types that correspond well with what is observed in many growing and regenerating tissues ., Key to successful control is an integral-feedback mechanism that is implemented when terminally differentiated cells secrete molecules that lower the probability that progenitor cells replicate versus differentiate ., Interestingly , this mechanism also explains how the distinctive proliferative behaviors of stem cell and “transit-amplifying” cell populations can emerge as a consequence of feedback effects , rather than intrinsic programming of cell types .

developmental biology, cell biology, neuroscience

Are common, generic strategies used in the quantitative control of tissue growth and regeneration? An investigation of feedback effects in multistage lineages suggests they are.

journal.pgen.1002142

A Two-Stage Meta-Analysis Identifies Several New Loci for Parkinsons Disease

Until the recent developments of high throughput genotyping and genome-wide association ( GWA ) studies , little was known of the genetics of typical Parkinsons disease ( PD ) ., Studies of the genetic basis of familial forms of PD first identified rare highly penetrant mutations in LRKK2 1 , 2 , PINK1 3 , SNCA 4 , PARK2 5 and PARK7 6 ., Following these findings , GWA scans for idiopathic PD identified SNCA and MAPT as unequivocal risk loci 7 , 8 , 9 , 10 , 11 as well as implicated BST1 8 , GAK 12 , and HLA-DR 13 ., Using sequence based imputation methods 14 , the meta-analysis of several GWA scans 7 , 9 , 10 , 11 conducted by the International Parkinsons Disease Genomics Consortium ( IPDGC ) identified and replicated five new loci: ACMSD , STK39 , MCCC1/LAMP3 , SYT11 , and CCDC62/HIP1R 15 and confirmed association at SNCA , LRRK2 , MAPT , BST1 , GAK and HLA-DR 15 ., We conducted a two-stage association study ., Combining stage 1 and stage 2 , the data consist of 12 , 386 PD cases and 21 , 026 controls genotyped using a variety of platforms ( Table 1 ) ., Stage 1 used genome-wide genotyping arrays and our initial analysis 15 focused on the subset of SNPs that passed genome-wide significance in stage 1 ., For stage 2 genotyping , we used a custom content Illumina iSelect array , the ImmunoChip and additional GWAS typing as previously described 15 ., The primary content of the ImmunoChip data focuses on autoimmune disorders but , as part of a collaborative agreement with the Wellcome Trust Case Control Consortium 2 , we included 1 , 920 ImmunoChip SNPs on the basis of the stage 1 GWA PD results ., Here , we report the combined analysis for this full set of 1 , 920 SNPs ., This step1+2 analysis identified seven new loci that passed genome-wide significance in the meta-analysis ., During the process of analyzing these data and preparing for publication , we became aware that another group was also preparing a large independent GWA scan in PD for publication ( Do et al , submitted ) ., Following discussion with this group we agreed to cross validate the top hits from each study by exchanging summary statistics for this small number of loci ., To provide further insights into the molecular function of these associated variants , we tested risk alleles at these loci for correlation with the expression of physically close gene ( expression quantitative trait locus , eQTL ) and the methylation status ( methQTL ) of proximal DNA CpG sites in a dataset of 399 control frontal cortex and cerebellar tissue samples extracted post-mortem from individuals without a history of neurological disorders ., In addition to eleven loci that passed genome-wide significance in stage 1 15 , we identified over 100 regions of interest defined as 10 kb windows containing at least one SNP associated at p<10−3 ., We submitted the most associated SNP in each region for probe design and follow-up genotyping using the ImmunoChip platform ., For each region of interest , we also added four SNPs in high level of linkage disequilibrium ( LD ) to provide redundancy where the most associated SNP would not pass the Illumina probe design step or the assay for that SNP would fail ., To complete the array design we also added all non-synonymous dbSNPs located in known PD associated regions 1 , 2 , 3 , 4 , 5 , 6 ., Out of these 2 , 400 submitted SNPs , 1 , 920 passed QC and were included in the final array design ., For these 1 , 920 SNPs we combined stage 1 and stage 2 associated data in a meta-analysis of 12 , 386 cases and 21 , 026 controls ( Table 1 ) from the IPDGC ., We exchanged summary statistics for these most significant hits with an additional large , case-control replication dataset ( 3 , 426 PD cases and 29 , 624 controls ) in an attempt to demonstrate independent replication ., On the basis of stage 1+2 results , seven new SNPs passed our defined genome-wide significance threshold ( p<5×10−8 , Table 2 and Figure 1 ) ., These loci are either novel or the previous evidence of association was not entirely convincing in individuals of European descent ., We combined these results with the independent replication ., Five of these seven loci replicated and showed strong combined evidence of PD association ( p<10−10 overall ) ., Taking either the nearest gene ( or the strongest candidate when available ) to designate these regions , these five loci are 1q32/PARK16 7 , 4q21/STBD1 , 7p15/GPNMB , 8p22/FGF20 16 and 16p11/STX1B ., rs708723/1q32 has been previously reported as PD associated ( PARK16 , 7 , 8 ) but this SNP lacked the unequivocal evidence of association in European samples ( p\u200a=\u200a9 . 47×10−10 in stage 2 only ) ., To understand the potential biological consequences of risk variation at this locus we tested whether rs708723 was correlated with either gene expression or DNA methylation status of proximal transcripts or CpG sites respectively ( Table 3 ) ., We found correlations with the expression of NUCKS1 ( p\u200a=\u200a1 . 8×10−7 ) and RAB7L1 ( p\u200a=\u200a7 . 2×10−4 ) ., We also found correlations with the methylation state of CpG sites located in the FLJ3269 gene ( p\u200a=\u200a3 . 9×10−22 ) ., In the case of 16p11/STX1B , the proximal gene to the most associated SNP rs4889603 is SETD1A ., However , STX1B is located 18 kb upstream of rs4889603 and is a more plausible PD candidate gene 17 owing to its synaptic receptor function ., We therefore used this gene to designate this region ., Our methQTL/eQTL dataset identified a correlation between the rs4889603 risk allele and increased methylation of a CpG dinucleotide in STX1B ( Table 3 ) ., The SNP rs591323 in the 8p22 region is located ∼150 kb downstream of the FGF20 gene ( NCBI build 36 . 3 ) , for which association with PD has been suggested previously in familial PD samples 16 , 18 but which remained controversial 19 ., Our findings provide further support for a PD association at this locus , but again , whether the functionally affected transcript is FGF20 or not remains unclear ., The regions 4q21/STBD1 and 7p15/GPNMD have not been previously implicated in PD etiology ., We found that the risk allele of rs156429 , the most associated SNP in the 7p15 region , is associated in our eQTL dataset with decreased expression of the proximal transcript encoded by NUPL2 ( Table 3 ) ., The same risk allele is also associated with increased methylation of multiple CpG sites proximal to GPNMB itself ( Table 3 ) ., Neither of these regions contains an obvious candidate gene ., Two additional loci ( 3q26/NMD3 and 8q21/MMP16 ) showed strong evidence of association in stage 1 and 2 but were not disease associated in the Do et al dataset ., Further replication is required to clarify the role of variation at these loci in risk for PD ., The strongly associated G2019S variant in the LRRK2 gene 20 was included in the Immunochip design and we replicated the published association: control frequency: 0 . 045% case frequency 0 . 61% , estimated odds ratio: 13 . 5 with 95% confidence interval: 5 . 5–43 ., However , the case collections have been partially screened for this variant therefore its frequency in cases and the odds ratio is likely to be underestimated ., The ImmunoChip array design provides some power to detect whether multiple distinct association signals exist at individual loci ., Indeed , if a SNP showed an independent and sufficiently strong association in stage 1 , it would have been included in stage 2 provided that it was not located in the same 10 kb window as the primary SNP in the region ., There is precedent for this in PD , with the previous identification of independent risk signals at the SNCA locus 11 ., We therefore used the Immunochip data to test whether any of the seven loci in Table 2 showed some evidence of more than one independent signal ., None of these seven loci showed any association ( p>0 . 01 ) after conditioning on the main SNP in the region ., In contrast , after conditioning on the most associated SNPs rs356182 in the SNCA region , several SNPs remained convincingly associated ( p\u200a=\u200a9 . 7×10−8 for rs2245801 being the most significant ) ., Lastly , we performed a risk profile analysis to investigate the power to discriminate cases and controls on the basis of the 16 confirmed common associated variants ( Table 4 ) ., For each locus , we estimated the odds ratio on the basis of stage 1 data and we applied these estimates to compute for each individual in the ImmunoChip cohort a combined risk score ., Solely based on these 16 common variants , and therefore not considering rare highly penetrant variants such as G2019S in LRKK2 20 , we found that individuals in the top quintile of the risk score have an estimated three-fold increase in PD risk compared to individuals in the bottom quintile ( Table 4 ) ., We note however that the effect size of several of these associated variants could be over-estimated ( an effect known as winners curse , see 21 ) but given the consistent estimates of odds ratio across studies ( Table 4 ) we expect this bias to be minimal ., The combination of GWA scans and imputation methods in large cohorts of PD cases and controls has enabled us to identify five PD associated loci in addition to the 11 previously reported by us ., Two of these loci ( 1q32/PARK16 , 8p22/FGF20 ) implicate regions that had been previously associated with PD risk 8 , 16 ., The 1q32/PARK16 showed convincing evidence of association in the Japanese population 8 but until now the association P-value had not passed a stringent genome-wide significance threshold in samples of European descent 7 ., The 8p22/FGF20 locus had been previously reported in a study of familial PD 16 and we provide the first evidence of association in a case-control study ., The remaining three loci ( STX1B/16p11 , STBD1/4q21 and GPNMB/7p15 ) are new ., Adding the eleven previously reported common variants 15 to the five convincingly associated loci identified in this study , common variants at 16 loci have now been associated with PD ., Controlling for the risk score based on the 11 SNPs previously identified 15 in the risk profile analysis ( Table 4 ) , the addition of these five new loci provides a modest but significant ( p\u200a=\u200a2 . 2×10−3 ) improvement of our ability to discriminate PD cases from controls ., Combining eQTL/methylation and case-control data implicates potential mechanisms which could explain the increased PD risk associated some of these variants ., In particular , the strong eQTL in the 1q32/PARK16 region with the RAB7L1 and NUCKS1 genes ( Table 3 ) suggests that either one of these genes could be the biological effector of this risk locus ., However , existing data show that eQTLs are widespread and this co-localization could be the result of chance alone 22 ., Additional fine-mapping work will be required to assess whether the expression and case-control data are indeed fully consistent ., While we are unable to unequivocally pinpoint the causative genes underlying these associations , their known biological function can suggest likely candidates ., At the 1q32/PARK16 loci our association and eQTL data indicate that RAB7L1 and NUCKS1 are the best candidates ., The former is a GTP-binding protein that plays an important role in the regulation of exocytotic and endocytotic pathways 23 ., Exocytosis is relevant for PD for two main reasons: firstly , since dopaminergic neurotransmission is mediated by the vesicular release of dopamine , i . e . dopamine exocytosis 24 , and secondly because it has been shown that alpha-synuclein knock-out mice develop vesicle abnormalities 25 , thus providing a potential direct link between genetic variability in the gene and a biological pathway involved in the disease ., Less is known regarding NUCKS1; it has been described to be a nuclear protein , containing casein kinase II and cyclin-dependant kinases phosphorylation sites and to be highly expressed in the cardiac muscle 26; but an involvement in PD pathogenesis has yet to be suggested ., At the 16p11/STX1B locus , notwithstanding the fact that other genes are in the associated region , STX1B is the most plausible candidate ., It has been previously shown to be directly implicated in the process of calcium-dependent synaptic transmission in rat brain 17 , having been suggested to play a role in the excitatory pathway of synaptic transmission ., Since parkin , encoded by PARK2 , negatively regulates the number and strength of excitatory synapses 27 , it makes STX1B a very interesting candidate from a biologic perspective ., FGF20 at 8p22 has been suggested to be involved in PD 16 , albeit negative results in smaller cohorts have followed the original finding 28 ., FGF20 is a neurotrophic factor that exerts strong neurotrophic properties within brain tissue , and regulates central nervous development and function 29 ., It is preferentially expressed in the substantia nigra 30 , and it has been reported to be involved in dopaminergic neurons survival 30 ., The ImmunoChip data provide limited resolution for the detection of multiple independent association signals in these regions ., A previous study 31 reported some evidence of allelic heterogeneity at the 1q32/PARK16 locus but the ImmunoChip data do not support this result ., A previous study 11 also reported two independent associations at the 4q22/SNCA locus and our data are consistent with this scenario ., However , the newly reported secondary association ( rs2245801 ) is in low LD ( r2\u200a=\u200a0 . 21 ) with rs2301134 , the SNP reported in 11 as an independent association ., Taken together , these findings suggest that at least three independent associations exist at SNCA/4q22 ., A more exhaustive fine-mapping analysis using either sequencing of large cohorts or targeted genotyping arrays will also be required to fully explore this locus ., As yet , we do not know which of the variants and which genes within each region are exerting the pathogenic effect ., We cannot exclude that some of the currently reported variants are in fact tagging high penetrance , but rare , mutations 32 ., Nevertheless , the successful identification of these 16 risk loci further demonstrates the power of the GWA study design , even in the context of disorders like PD that have a complex genetic component ., We therefore expect that further and larger association analyses , perhaps using dedicated high-throughput genotyping arrays like the ImmunoChip , will continue to yield new insights into PD etiology ., Participating studies were either genotyped using the ImmunoChip as part of a collaborative agreement with the ImmunoChip Consortium , or as part of previous GWA studies provided by members of the IPDGC or freely available from dbGaP 7 , 9 , 10 , 11 ., Genotyping of the UK cases using the Immunochip was undertaken by the WTCCC2 at the Wellcome Trust Sanger Institute which also genotyped the UK control samples ., The constituent studies comprising the IPDGC have been described in detail elsewhere 15 , although a summary of individual study quality control is available as part of Table S1 ., In brief all studies followed relatively uniform quality control procedures such as: minimum call rate per sample of 95% , mandatory concordance between self-reported and X-chromosome-heterogeneity estimated sex , exclusion of SNPs with greater than 5% missingness , Hardy Weinberg equilibrium p-values at a minimum of 10−7 , minor allele frequencies at a minimum of 1% , exclusion of first degree relatives , and the exclusion of ancestry outliers based on either principal components or multidimensional scaling analyses using either PLINK 33 or EIGENSTRAT 34 to remove non-European ancestry samples ., All GWAS studies utilized in this analysis ( and in the QTL analyses ) were imputed using MACHv1 . 0 . 16 14 to conduct a two-stage imputation based on the August 2009 haplotypes from initial low coverage sequencing of 112 European ancestry samples in the 1000 Genomes Project 35 , filtering the data for a minimum imputation quality of ( RSQR>0 . 3 ) 14 ., Logistic regression models were utilized to quantify associations with PD incorporating allele dosages as the primary predictor of disease ., Imputed data was analyzed using MACH2DAT , and genotyped SNPs were analyzed using PLINK ., All models were adjusted for covariates of components 1 and 2 from either principal components or multidimensional scaling analyses to account for population substructure and stochastic genotypic variation ( except in the UK-GWAS data which were not adjusted for population substructure ) ., Single SNP test statistics were combined across datasets using a score test methodology , essentially assuming equal odds ratio across cohorts ., In addition , fixed and random effects meta-analyses were implemented in R ( version 2 . 11 ) to confirm that the score test approximation does not affect the interpretation of the results ., We also tested the relevant SNPs heterogeneity across cohorts and no significant heterogeneity was detected ( Table S2 ) ., We communicated to our colleagues in charge of the independent study ( Do et al ) the seven SNPs listed in Table 2 ., For this subset of SNPs they selected the marker with the highest r2 value on their genotyping platform and provided us with the following summary statistics: odds ratio , direction of effect , standard error for the estimated odds ratio and one degree-of-freedom trend test P-value ., Quantitative trait analyses were conducted to infer effects of risk SNPs on proximal CpG methylation and gene expression ., For the five replicated SNP associations ( Table 2 ) , all available CpG probes and expression probes within +/−1 MB of the target SNP were investigated as candidate QTL associations in frontal cortex and cerebellar tissue samples ., 399 samples were assayed for genome-wide gene expression on Illumina HumanHT-12 v3 Expression Beadchips and 292 samples were assayed using Infinium HumanMethylation27 Beadchips , both per manufacturers protocols in each brain region ., A more in depth description of the sample series comprising the QTL analyses , relevant laboratory procedures and quality requirements may be found in 15 ., The QTL analysis utilized multivariate linear regression models to estimate effects of allele dosages per SNP on expression and methylation levels adjusted for covariates of age at death , gender , the first 2 component vectors from multi-dimensional scaling , post mortem interval ( PMI ) , brain bank from where the samples were provided and in which preparation/hybridization batch the samples were processed ., A total of 670 candidate QTL associations were tested: 87 expression QTLs in the cerebellum samples , 85 expression QTLs in the frontal cortex samples , 249 methylation QTLs in the cerebellum samples and 249 methylation QTLs in the frontal cortex samples ., Multiple test correction was undertaken using false discovery rate adjusted p-values<0 . 05 to dictate significance , with the p-value adjustment undertaken in each series separately , stratified by brain region and assay ., A complete list of all QTL associations tested is included in Table S3 .

Introduction, Results, Discussion, Material and Methods

A previous genome-wide association ( GWA ) meta-analysis of 12 , 386 PD cases and 21 , 026 controls conducted by the International Parkinsons Disease Genomics Consortium ( IPDGC ) discovered or confirmed 11 Parkinsons disease ( PD ) loci ., This first analysis of the two-stage IPDGC study focused on the set of loci that passed genome-wide significance in the first stage GWA scan ., However , the second stage genotyping array , the ImmunoChip , included a larger set of 1 , 920 SNPs selected on the basis of the GWA analysis ., Here , we analyzed this set of 1 , 920 SNPs , and we identified five additional PD risk loci ( combined p<5×10−10 , PARK16/1q32 , STX1B/16p11 , FGF20/8p22 , STBD1/4q21 , and GPNMB/7p15 ) ., Two of these five loci have been suggested by previous association studies ( PARK16/1q32 , FGF20/8p22 ) , and this study provides further support for these findings ., Using a dataset of post-mortem brain samples assayed for gene expression ( n\u200a=\u200a399 ) and methylation ( n\u200a=\u200a292 ) , we identified methylation and expression changes associated with PD risk variants in PARK16/1q32 , GPNMB/7p15 , and STX1B/16p11 loci , hence suggesting potential molecular mechanisms and candidate genes at these risk loci .

This paper describes the largest case-control analysis of Parkinsons disease to date , with a combined sample set of over 12 , 000 cases and 21 , 000 controls ., After combining our findings with an independent replication dataset of more than 3 , 000 cases and 29 , 000 controls , we found five additional PD risk loci in addition to the 11 loci previously identified in earlier consortium efforts ., This successful study further demonstrates the power of the GWA scan experimental design to find new loci contributing to disease risk , even in the context of complex disorders like Parkinsons disease ., These new findings provide insights into the etiology of PD and will promote a better understanding of its pathogenesis .

genome-wide association studies, genetics, biology, genetics of disease, neuroscience, genetics and genomics

journal.pntd.0006758

Predicted short and long-term impact of deworming and water, hygiene, and sanitation on transmission of soil-transmitted helminths

Globally , over 1 billion people are infected with soil-transmitted helminths ( STH ) 1 , the majority of whom are infected with Ascaris lumbricoides ( roundworm ) , Trichuris trichiura ( whipworm ) , and/or hookworm ( Necator americanus and Ancyclostoma spp . ) ., STH are transmitted through the ingestion of soil contaminated with egg-containing faeces ( roundworm and whipworm ) or exposure of skin to free-living larvae ( hookworm ) ., As such , prevalence and intensity of STH infections are strongly inversely correlated with access to and use of improved sources of water , sanitation , and hygiene ( WASH ) 2 , 3 ., World Health Organization ( WHO ) guidelines recommend that STH are controlled by preventive chemotherapy ( PCT ) with albendazole ( ALB ) or mebendazole ( MEB ) targeted at school age children ( SAC ) , pre-school age children , and groups at high risk of morbidity such as women of childbearing age ., The guidelines further recommend that complimentary WASH interventions are implemented to sustain control 4–6 ., Although WASH interventions are expected to help interrupt STH transmission such that PCT can be stopped in the long run , it is unclear at what minimum uptake and effectiveness of WASH and within what time frame we can expect this to happen ., There have been several studies attempting to demonstrate and quantify the impact of different WASH interventions on STH infections ., So far , several randomised controlled trials ( RCT ) implementing WASH programmes in schools have reported a decrease in STH infections as a result of different individual or combined WASH components , with a strong emphasis on individual hygiene behaviours 7–10 ., In contrast , community-based RCTs implemented in India alongside the Indian Total Sanitation Campaign did not detect any benefit resulting from the sanitation intervention , although absence of an effect was attributed to low coverage and use of household latrines 11 , 12 ., The WASH for WORMS ( W4W ) RCT in Timor-Leste aimed to determine the additional benefit of an integrated community-based WASH and deworming intervention on STH infections when compared to deworming alone 13 ., Surprisingly , also here no benefit arose from the WASH intervention with all the STH reduction arising from the PCT 14 ., As such , in the context of calls for integration and scale-up of WASH and PCT experimental evidence for the benefit of WASH is limited ., Mathematical modelling may shed some light on what WASH interventions can be expected to achieve in different settings and at different time scales ., In this paper we predict the impact of WASH interventions on STH in different epidemiological contexts with and without PCT , using a newly developed WASH extension of the established individual-based WORMSIM modelling framework for transmission and control of helminths 15 ., We use the model to first explain the findings from the W4W study ( i . e . no noticeable impact of latrines in context of community-wide PCT ) , then predict the short and long-term impact of different WASH modalities and the relative importance of uptake ( proportion of population that takes up intervention ) and effectiveness ( impact of interventions on individuals that take up interventions ) ., Finally , we investigate the added value of WASH to current control strategies for STH , in particular for sustaining achieved gains in the long run ., WORMSIM is a generalised individual-based modelling framework for transmission and control of helminth infections in humans , including soil-transmitted helminths 15 , and is based on earlier individual-based models for onchocerciasis , schistosomiasis , and lymphatic filariasis 16–18 ., WORMSIM predictions for STH have been previously validated against field data on trends in hookworm and A . lumbricoides infection levels before and during PCT 15 , 19 ., Here we provide a high-level overview of the model; technical details can be found in S1 Text ., A zip archive with the WORMSIM programme is provided in S1 File ., The impact of PCT using ALB was simulated as in recent studies with WORMSIM 15 , 19: i . e . assuming the drug kills 99% of A . lumbricoides , 95% of hookworms , and 60% of T . trichiura ( unless specified otherwise ) ., These percentages were based on faecal egg reduction rates ( ERR ) observed in a set of multi-country studies 20 , 21 ., The frequency of PCT , age range of the target population , coverage of the target population , and pre-control transmission conditions were varied in different simulations ., Based on the self-reported level of latrine uptake in the W4W trial 14 , we use a 70% population-level uptake of WASH interventions as a default value for our simulations , and specify an alternative hypothetical scenario with very high uptake of 95% ., Because we do not know to what extent WASH interventions exactly reduce individuals’ contribution and/or exposure to the environmental reservoir , we take a qualitative approach and define two levels of effectiveness ( 70% and 95% reduction in individuals that take up intervention ) such that we can compare the relative importance of uptake and effectiveness for the impact of WASH interventions on STH transmission ., Explorative simulations with 100% uptake and effectiveness ( which we do not consider realistic levels ) showed little additional impact of WASH in the context of PCT compared to 95% uptake and effectiveness ., Because WORMSIM is a stochastic individual-based model , its predictions vary to some extent with repeated simulations when using the same input parameters ( i . e . representing the potential courses of history in a probabilistic fashion ) ., To predict the expected impact of interventions on STH transmission , we ran 100 repeated simulation for each scenario under consideration and took the mean of repeated simulations ., To predict the probability of interruption of transmission , we ran 1000 repeated simulations and assessed what fraction of the simulations resulted in zero worm prevalence 50 years after stopping PCT ., Fig 1 illustrates the predicted impact of 70% uptake of latrines on infection levels in the context of a community-wide deworming programme implemented at 80% coverage ( as in the W4W trial area ) in a setting with high pre-control prevalence of A . lumbricoides and hookworm infection ( very few T . trichuria eggs were detected in the W4W trial area ) ., In general , prevalence of infection , as detected by a single Kato-Katz faecal slide per person , declined steeply within two years of deworming ( grey line ) ., The additional impact of latrine use was very small ( red line ) , even when assuming an effectiveness of 95% ., This pattern was qualitatively similar when assuming normal or relatively low drug efficacy ( 95% vs . 80% of worms killed per treatment in treated individuals ) , or when using a hypothetical tests that can perfectly detect the density of adult female worms in a host or presence of at least one worm pair ( S1 Fig ) , confirming the observation of little impact of latrine use in the context of community-wide PCT in the W4W trial 14 ., Community-wide hygiene interventions or combined sanitation and hygiene intervention implemented at 70% uptake and 95% effectiveness were predicted to have little noticeable additional impact in the context of community-wide PCT ( blue and purple lines in Fig 1 ) , even when they are implemented and taken up perfectly such that no new infections can occur between PCT rounds ( dashed black line ) ., In contrast , the model predicts that the impact of the same community-wide WASH interventions would have been more readily detectable in the context of school-based deworming , which is logical as school-based deworming allows for the existence of a larger reservoir of infection in untreated individuals , leaving more potential impact for additional interventions ( S2 Fig ) ., Fig 2 shows the predicted impact of WASH interventions in a context without PCT ., For the sake of comparing the impact on different species , we calibrated transmission for each species such that the model produced the same pre-control prevalence of infection of about 45%-50% ( based on single Kato-Katz slide ) ., For short-lived worms like A . lumbricoides and T . trichiura , both sanitation and hygiene can have a significant impact on infection levels within just two to five years ., However , for hookworms the impact of sanitation and hygiene develops much more slowly ( about four times slower ) ., To compare the relative importance of uptake and effectiveness for different WASH modalities , we plot the predicted impact of 70% uptake and 95% effectiveness ( green dashed lines in Fig 2 ) vs . 95% uptake and 70% effectiveness ( red solid lines ) ., The impact of sanitation interventions ( left column of panels ) depends about equally on uptake and effectiveness: the green dashed and solid red lines are identical ., In contrast , the impact of hygiene interventions ( middle column of panels ) depends more on effectiveness than on uptake: 70% uptake combined with 95% effectiveness has a higher impact than 95% uptake combined with only 70% effectiveness , especially for hookworm infections ., This is a direct result of how hygiene interventions change the distribution of worms across hosts: if the worm burden is gradually reduced by 95% in 70% of people , there is reasonable chance that egg production will eventually stop in many of them ., However , when worm burdens are reduced by only 70% in 95% of people , it is more likely that many people will still have at least one mated female worm producing eggs ., Last , the impact of combined sanitation and hygiene interventions depends most on uptake ( red solid line descends lower ( eventually ) than dashed green line ) , which is a direct consequence of our assumption that the two modalities are taken up by exactly the same individuals , creating a maximum synergistic effect in those who take up the interventions ., See S3 Fig for similar figures as Fig 2 , but for a wider range of levels of uptake and effectiveness ., Next , we investigate the potential impact of different WASH modalities in context of the current WHO guidelines for PCT , i . e . annual PCT with ALB or MEB targeted at children for areas with pre-control prevalence of STH infection of 20%-50% in SAC ( as measured by single Kato-Katz slide ) and semi-annual PCT for areas with pre-control prevalence >50% ., The goal of this strategy is to reduce the prevalence of moderate-to-heavy intensity of infection to <1% in children and groups at high risk of morbidity such as women of childbearing age 4–6 ., Although the operational goal is to achieve 75% PCT coverage of risk groups at the national level , we assume here that when implemented , school-based PCT covers 90% of the children in a community ., Fig 3 shows the impact of these guidelines for those areas where school-based PCT is implemented but is scaled down or stopped after five years because of favourable results ( <1%: stop PCT; 1–10%: bi-annual PCT; 10–20%: annual PCT ) , in absence and presence of different WASH modalities ., Both sanitation and hygiene interventions ( red and blue lines ) were predicted to markedly reduce the bounce-back of infection levels after scaling down and to greatly prevent recrudescence of infection after stopping PCT altogether ., Of note is that the impact of hygiene interventions is larger than that of sanitation interventions given the same level of uptake and effectiveness ., We performed a similar comparison for A . lumbricoides and T . trichuris , as well as areas where PCT is implemented community-wide , i . e . targeting the population of age 2 and above ( S4 Fig ) ., We conservatively assume that community-wide PCT is only able to achieve 75% coverage of the target population because of absence and/or refusal to participate ., Because of the higher overall impact of community-wide treatment ( despite lower coverage of target population ) , the decision to stop PCT after five years is taken in a greater proportion of the simulations , and even in situations with pre-control infection prevalence >50% in SAC ( if the same decision criteria for stopping PCT are used as for school-based PCT ) ., Given that the repercussions of stopping PCT too early are higher in areas with high pre-control infection levels ( i . e . high transmission potential ) , WASH is even more important to prevent bounce-back of infection levels in areas with community-wide treatment ., Last , Fig 4 illustrates the impact of uptake and effectiveness of different WASH modalities on probability of elimination hookworm infection after five years of PCT , where elimination is defined as zero worm prevalence 50 years after stopping PCT ., The steep association between probability of elimination and WASH uptake suggests that uptake is a more important driver of elimination that WASH effectiveness , which is in contrast to Fig 2 , where the impact of hygiene interventions on prevalence of infection is mostly driven by effectiveness of the intervention ., This is logical as interruption of transmission is more difficult if a larger sub-group of individuals systematically does not take up interventions ., Exceptions are situations where 70% effectiveness is not enough to achieve elimination because PCT has not driven down infection levels low enough ( first and second panel of second row and all panels in bottom row ) ., These patterns are similar for A . lumbricoides in areas with school-based deworming ( S5 Fig; community-wide PCT quickly wipes out A . lumbricoides ) and for T . trichiura in areas with community-wide deworming ( S6 Fig; school-based PCT is unlikely to interrupt T . trichiura transmission ) , although achieving elimination with high probability requires generally lower levels of uptake in these contexts ( given the same duration of PCT ) ., In this modelling study we confirm the finding from the W4W study that latrine use has little observable impact in the context of semi-annual community-wide deworming 14 ., This is due the strong and quick impact of deworming masking the slower , more long-term impact of WASH , which can still be substantial ., We further show that the impact of WASH interventions on STH transmission highly depends on the worm species , WASH modality , and pre-control endemicity ( higher uptake is required for elimination in high prevalence settings ) ., Also , the impact of WASH on STH infection levels is greatly diminished and slower for lower levels of uptake and effectiveness , which likely explains the lack of effect in the Indian Total Sanitation Campaign , as previously suggested 11 , 12 ., Last , we show that WASH is particularly important to maintain gains when scaling down or stopping PCT , and when that happens , high uptake of WASH will be relatively more important than high effectiveness ., An important limitation of the modelling presented here is that the model quantifications for uptake and effectiveness of WASH in this study were largely hypothetical and not based on direct observations , except for the 70% figure of self-reported latrine use in the W4W trial ., We initially did try to fit the uptake and effectiveness parameters in the model to the W4W trial to reproduce the observed infection levels over time , but this was complicated due to difficulties in interpretation of data on the impact of WASH because of potential counter-intended effects ( e . g . latrines may actually act as sites of high exposure to infection ) and the difficulties of quantifying self-reported human behaviour in general ., Likewise , the use of a relatively new diagnostic tool ( quantitative polymerase chain reaction 22 ) for detection of infection posed difficulties in getting the model to exactly reproduce the data ., We therefore chose to perform a more qualitative modelling study to disentangle the effects of different WASH modalities , to better understand potential interactions between WASH and PCT , and to help explain the patterns observed in the field , realising that not all hygiene and sanitation interventions are necessarily equal in terms of uptake or effectiveness in reality ., If the uptake and/or effectiveness of WASH interventions are lower in field settings than assumed in our simulations , the impact of such interventions will be even more difficult to measure than predicted here ., If on the other hand uptake and/or effectiveness of WASH interventions are higher in reality than assumed here , it will still be difficult to measure their impact in the context of community-wide deworming due to the large impact of PCT on infection levels , even when using a highly accurate test as was done in the W4W trial ., To better demonstrate the benefit and importance of WASH and better inform mathematical models with plausible estimates of the effectiveness and uptake of WASH interventions , we recommend that the impact of WASH interventions is evaluated in settings where expected reinfection rates are considerable , i . e . settings where PCT is implemented only annually ( as opposed to semi-annually ) and/or targeted only at certain risk groups ( e . g . school-age children , as opposed to community-wide PCT ) ( see also Box 1 ) ., It might even be useful to evaluate the impact of WASH in areas where pre-control STH prevalence is so low ( <20% ) that PCT need not be started according to WHO guidelines 6 , although it could be challenging to measure intervention impact in context of low infection prevalence due to relatively low statistical power ., Although reinfection rates are also high in highly endemic areas , this does not guarantee that an effect of WASH , if any , can be detected during community-wide PCT , as was the case for the W4W study 14 ., Although challenging , collection of individual-level longitudinal data on both WASH-related behaviour and infection levels would provide a wealth of information for mathematical models ., Further , in addition to WASH uptake and effectiveness , the epidemiological impact of WASH in trials or observational studies will also depend on the longevity of eggs and or larvae in the local environment , which may vary between studies depending on environmental factors such as temperature , humidity , etc ., Therefore , analyses of such data with mathematical models will require assumptions or actual data on the survival of eggs and larvae; here we assume “default” average life expectancies based on literature , but with more data , the model could be improved to represent specific local situations ., We show that the effects of WASH on STH infection levels is of a lesser magnitude and occurs at a slower rate for hookworm than for A . lumbricoides and T . trichuris , which can be directly explained by the relatively short average lifespan of adult A . lumbricoides and T . trichuris worms ( about one year vs . three years for hookworms ) 23–27 ., Due to their shorter lifespan , and despite the relatively long average lifespan of eggs in the environment ( months vs . weeks for hookworm ) , A . lumbricoides and T . trichuris infection levels in the human population change more quickly in response to changes in the environmental reservoir or exposure to the reservoir than for hookworm ., A second contributing factor is that egg productivity per adult female hookworm 28 is relatively low compared to A . lumbricoides 24 or T . trichiura 29 , which means that for a given species-specific prevalence of egg-positivity , humans need to harbour relatively more ( mated ) female hookworms , and as a result , it takes longer for natural attrition ( or deworming ) to reduce the worm population in size to the point that infected individual stops excreting eggs ., In a recent meta-analysis of observational and intervention studies , Freeman et al 3 found that the association between sanitation and A . lumbricoides infection is stronger than for the other STHs ( odds ratio of 0 . 77 , compared to 1 . 0 for whipworm and 0 . 94 for hookworm ) ., The authors hypothesised that this pattern may be explained by the potent effect of ALB on A . lumbricoides , reasoning that sanitation itself is unlikely to reduce infection over a short period of time and only helps reduce reinfection ., We however show here that, 1 ) potent PCT interventions mask the impact of WASH , and that, 2 ) with sufficient uptake and effectiveness of sanitation in the absence of PCT , A . lumbricoides and T . trichiura infection prevalence decline much faster ( substantially so within a year ) than hookworm prevalence , which in itself may already explain the observation of a difference between hookworm and roundworm in terms of strength of the association with sanitation ., Moreover , drug efficacy is high for both A . lumbricoides ( ERR 99% and 98% for ALB and MEB , respectively ) and hookworm ( ERR 96% and 81% ) 20 , 21 ., Therefore , we expect that the association with sanitation is stronger for A . lumbricoides than for hookworm because of shorter worm lifespan , as well as the fact that the impact of school-based deworming is higher for A . lumbricoides than for hookworm because most of the worms reside in children , in contrast to hookworms , for which densities are highest in ( often untreated ) adults 19 , 30 ., Still , the difference in strength of association with sanitation between A . lumbricoides and T . trichiura may well indeed be partly attributable to low drug efficacy against T . trichiura ( ERR 65% and 63% ) ., Previous WHO guidelines included the option to scale down or stop PCT if sufficient impact has been achieved after five to six years 4 ., These options are no longer mentioned in a recent update of the guidelines 6 , but might still be considered in some places , either in the context of an STH control programme , or when stopping community-wide PCT against lymphatic filariasis , which includes the distribution of ALB ( combined with ivermectin outside Africa , which is also highly efficacious against T . trichiura 31 ) ., A recent modelling study already highlighted the importance of setting up contingency mechanisms when stopping PCT against STH 32 ., Here , we add further evidence about how WASH can strongly reduce the risk and speed of bounce-back of infection levels after stopping PCT , which supports the notion that integration of WASH and PCT policy is important for long-term sustained STH control ., We present the first attempt at modelling the impact of WASH on STH transmission ., Our approach comes with three important limiting assumptions ., The first is that that the assumed effectiveness of WASH interventions is the same for everybody who takes up WASH ., The actual effect of WASH in terms of reduction in contribution and exposure to the environmental reservoir may however vary between individuals due to inter-individual variation in frequency and quality of execution or use of the intervention ., Second , we assume that uptake of hygiene and sanitation is perfectly correlated in individuals , allowing for a maximum synergistic effect between the two WASH modalities ., If in reality individuals have a particular preference for one of the two modalities ( e . g . one subgroup prefers hygiene interventions and another prefers sanitation ) , then such synergistic effects become less prominent ., Third , we assume that uptake and effectiveness of WASH is independent of an individual’s infection status before start of interventions ., In light of the possibility that uptake and effectiveness of WASH is higher in individuals with lower infection status ( better socio-economic status and/or more time , knowledge , and self-efficacy to actually use WASH ) , the impact of WASH in field settings may be lower than currently predicted by our model ., These three assumptions are subject of future studies , which require further extensions of the WASH model concepts ., Future modelling will also cover the impact of school-based WASH in contrast to community-based WASH , which will require detailed modelling of multiple environmental reservoirs in school and household areas ., In conclusion , we show that the impact of WASH interventions on STH transmission highly depends on the worm species , WASH modality , and uptake and effectiveness of the intervention ., Also , the impact of WASH is difficult to measure in the context of ongoing deworming programmes ., Still , we show a clear added benefit of WASH to sustain the gains made by PCT in the long term , such that PCT may be scaled down or even stopped altogether ., All of the above support the notion that WASH and PCT policy should be integrated .

Introduction, Methods, Results, Discussion

Regular preventive chemotherapy ( PCT ) targeting high-risk populations is an effective way to control STH in the short term , but sustainable long-term STH control is expected to require improved access to water , sanitation , and hygiene ( WASH ) ., However , experimental studies have not been able to conclusively demonstrate the benefit of WASH in preventing STH ( re- ) infections ., We investigated the impact of WASH on STH infections during and after PCT using mathematical modelling ., We use the individual-based transmission model WORMSIM to predict the short and long-term impact of WASH on STH transmission in contexts with and without PCT ., We distinguish two WASH modalities: sanitation , which reduces individuals’ contributions to environmental contamination; and hygiene , which reduces individuals’ exposure to infection ., We simulate the impact of varying levels of uptake and effectiveness of each WASH modality , as well as their combined impact ., Clearly , sanitation and hygiene interventions have little observable short-term impact on STH infections levels in the context of PCT ., However , in the long term , both are pivotal to sustain control or eliminate infection levels after scaling down or stopping PCT ., The impact of hygiene is determined more by the effectiveness of the intervention than its overall uptake , whereas the impact of sanitation depends more directly on the product of uptake and the effectiveness ., The impact of WASH interventions on STH transmission highly depends on the worm species , WASH modality , and uptake and effectiveness of the intervention ., Also , the impact of WASH is difficult to measure in the context of ongoing PCT programmes ., Still , we show a clear added benefit of WASH to sustain the gains made by PCT in the long term , such that PCT may be scaled down or even stopped altogether ., To safely stop or scale down PCT , policy for WASH and PCT should be integrated .

Soil-transmitted helminths ( STH ) , which are transmitted via faecal contamination of the environment , still infect over 1 billion people in developing countries because of absence or poor access to improved sources of water , sanitation , and hygiene ( WASH ) ., In this study , we use a mathematical model for transmission and control of STH to investigate to what extent and on what time scale we can expect improved access to WASH to impact STH infection levels ., Our predictions confirm findings from experimental studies that in the context of deworming campaigns , the impact of WASH is difficult to measure , which is due the strong and quick impact of deworming masking the slower , more long-term impact of WASH ., We further show that the impact of WASH interventions on STH transmission highly depends on the worm species , WASH modality , uptake , effectiveness , and pre-control endemicity ., Still , we show a clear added benefit of WASH to sustain the gains made by PCT in the long term , such that PCT may be scaled down or even stopped altogether ., To safely stop or scale down PCT , policy for WASH and PCT should be integrated .

invertebrates, medicine and health sciences, helminths, tropical diseases, hookworms, parasitic diseases, animals, health care, ascaris, ascaris lumbricoides, sanitation, neglected tropical diseases, public and occupational health, trichuris, hygiene, helminth infections, environmental health, eukaryota, trichuriasis, nematoda, biology and life sciences, soil-transmitted helminthiases, organisms

journal.pgen.1004557

Differential Management of the Replication Terminus Regions of the Two Vibrio cholerae Chromosomes during Cell Division

Most bacteria harbour a single chromosome and , in the rare case in which the genetic material is divided on several chromosomes , the extra-numerous ones appear to have derived from horizontally acquired mega-plasmids that subsequently gained essential genes 1 ., This is notably the case for Vibrio cholerae , the agent of the deadly human diarrheal disease cholera , whose genome is divided between a 2 . 961 Mbp ancestral chromosome , chrI , and a 1 . 072 Mbp plasmid-derived chromosome , chrII 2 ., The preferential transcription of chrII genes during colon colonization compared to in vitro growth under aerobic conditions suggests that this genomic organization is important for rapid adaptation to different environments 3 ., Likewise , other bacteria harbouring multipartite genomes can adopt several different life cycles 4 , 5 , 6 , 7: the rhyzobium , the burkholderia and the vibrio , can alternatively spread freely in the environment or interact as symbionts or pathogens with eukaryotic cells; the borrelia are obligate parasites that need to infect several different eukaryotic organisms in the course of their life cycle ., Thus , multipartite genomes seem to offer a selective advantage for the adaptation to very different environmental conditions ., However , the necessary coordination between replication , chromosome segregation and cell scission raises questions on the management of the different chromosomes of such bacteria ., Bacterial chromosomes harbour a single origin of bidirectional replication and are generally circular ., Replication ends in a region opposite of the origin of replication , the terminus region , in which is usually found a specific recombination site dedicated to the resolution of chromosome dimers , dif 8 ., Fluorescent microscopic observation of chromosome segregation in mono-chromosomal bacteria revealed that it is concurrent with replication and starts with the active positioning of sister copies of the origin region into opposite cell halves 9 , 10 , 11 ., As replication progresses along the left and right chromosomal arms , newly replicated loci are progressively segregated towards their future daughter cell positions ., However , the mean time during which sister loci remain together before separation is variable 12 ., In particular , sister copies of the terminus region co-localize at mid-cell until the initiation of cell division in E . coli and P . aeruginosa 9 , 10 , 13 , 14 ., This mode of segregation can participate in the coordination between chromosome segregation and cell division ., Indeed , nucleoid occlusion factors impede the assembly of the cell division machinery until a time when the only genomic DNA left at mid-cell consist of the sister copies of the terminus region in Escherichia coli and Bacillus subtilis 15 , 16; the long co-localization of sister termini at mid-cell is at least in part dictated by the MatP/matS macrodomain organisation system in E . coli 17 , 18; a DNA translocase , FtsK , which is recruited to mid-cell as part of the divisome and which pumps chromosomal DNA in the orientation dictated by repeated polar motifs that point towards dif , the KOPS , promotes the orderly segregation of the DNA within the terminus region of E . coli chromosome 13 , 19 , 20 ., One of the functions of FtsK is to control the resolution of chromosome dimers , which result from homologous recombination events between circular sister chromatids , by the addition of a cross-over between sister dif sites at the time of constriction 21 ., FtsK is also thought to participate in sister chromatid decatenation 22 , 23 and to create a checkpoint to delay constriction until sister terminus regions have been fully segregated 19 , 24 , 25 ., V . cholerae chrI and chrII are circular and harbour a single dif site in the region opposite of their origin of replication , dif1 and dif2 , respectively ( Figure 1A , 26 ) ., Segregation of the two chromosomes is concurrent with replication and both chromosomes adopt a longitudinal organization within the cell 27 ., However , chrII is replicated late in the C period of the cell cycle , when most of chrI has been replicated , and the initiation of its segregation is consequently delayed 28 ., In addition , the origin region of chrI , OriI , locates to the old pole of newborn cells and one OriI sister migrates to the other pole after replication ( Figure 1A , 27 ) ., The origin region of chrII , OriII , locates to mid-cell in newborn cells and the two OriII sisters migrate towards the ¼ and ¾ positions after replication ( Figure 1A , 27 ) ., This is at least in part dictated by the presence of a partition machinery of a chromosomal type on chrI , parABS1 , and a partition system that groups with plasmid and phage machineries on chrII , parABS2 ( Figure 1A , 27 , 29 , 30 ) ., The last chromosomal regions to be segregated are the terminus regions of chrI and chrII , TerI and TerII , respectively ( Figure 1A , 27 ) ., Both TerI and TerII locate at or close to the new pole in newborn cells ( Figure 1A , 27 ) ., Replication termination of the two V . cholerae chromosomes is synchronous 28 and unreplicated TerI and TerII are recruited to mid-cell at approximately the same time ( Figure 1A , 27 ) ., V . cholerae is closely related to E . coli in the phylogenetic tree of bacteria and its genome harbour the same dam co-occurring DNA maintenance machineries as E . coli 31 ., This includes a unique E . coli MatP ortholog and the presence of cognate matS sites in both TerI and TerII ., In addition , a common pair of tyrosine recombinases , XerC and XerD , serves to resolve dimers of each of the two V . cholerae chromosomes despite the sequence divergence of dif1 and dif2 26 ., Dimer resolution is controlled by a unique E . coli FtsK ortholog , whose translocation activity is oriented by KOPS motifs that point towards the dimer resolution site of each of the two chromosomes 26 ., By analogy to E . coli , MatP is thought to maintain sister copies of TerI and TerII at mid-cell and FtsK to promote the orderly segregation of the DNA within TerI and TerII ., Correspondingly , the separation of sister copies of a locus situated at 40 kbp from dif1 seemed coordinated with cell division ( Figure 1A , 27 , 32 ) ., However , sister copies of a locus situated at 49 kbp from dif2 separated before cell division , which questioned the role of FtsK and MatP on TerII segregation ( Figure 1A , 32 ) ., The aim of this work was to identify the contribution of MatP to the segregation dynamics of TerI and TerII ., We show by replication profiling that dif1 and dif2 are located next to the replication terminus of chrI and chrII , respectively ., Simultaneous visualization of the positions of dif1 and dif2 within the cell then allowed us to confirm the late and early separation of TerI and TerII , respectively ., However , we show that TerII sisters keep colliding with each other at mid-cell during constriction by genetically probing the relative frequency of sister chromatid contacts occurring at mid-cell at the time of cell division along the two chromosomes and by time-lapse fluorescent microscopy ., We further show that the frequency of these collisions depends on the MatP/matS macrodomain organization system , possibly because it restricts the movements of TerII within the cell ., We also show that MatP promotes the late mid-cell co-localization of TerI sisters ., However , TerI loci remain in the vicinity of the cell centre and sister chromatid contacts remain frequent in its absence ., Replication profiling of V . cholerae cells by deep sequencing indicated that termination most frequently occurred at a distance of ∼90 kbp and ∼70 kbp from the reference loci that had been used by Srivastava et al . for the simultaneous visualization of the positions of TerI and TerII ( Figure S1A , 32 ) ., It was therefore possible that the behaviour of these loci did not fully reflect TerI and TerII segregation dynamics ., To confirm the segregation pattern of the terminus regions of chrI and chrII , we simultaneously visualized the intracellular location of dif1 and dif2 in cells that were exponentially growing in minimal media ., We used the lacO/LacI-mCherry system to label the dif1 locus and the pMT1 parS/yGFP-ParB system to label the dif2 locus ., Cells were classified according to their length in bins of 0 . 25 µm ., They had a median length of 3 . 2 µm ( Figure S2A ) ., The smallest cells , i . e . the youngest cells , contained a single dif1 spot at one of the two cell poles ( Figure 1B ) ., This pole , which results from the previous division event , is hereafter referred to as the new pole ., The preferential localization of dif1 towards the new pole was used to orientate the cells ., A single dif2 spot was also observed in the youngest cells ( Figure 1B ) ., This spot was located in the younger cell half , at an intermediate position between the dif1 spot and the middle of the cell ( Figure 1B ) ., The polarity of the dif1 and dif2 spots decreased as a function of cell elongation and the median position of each spot reached mid-cell in cells of an intermediate length ( Figure 1B ) ., The majority of the longest cells , i . e . the closest to cell division , displayed a single dif1 spot , which was located at mid-cell and was flanked by two dif2 spots ( Figure 1B ) ., Indeed , <15% of the cells from the 4 . 25 µm–4 . 5 µm bin displayed two dif1 spots whereas >80% of them displayed two dif2 spots ( Figure 1C ) ., In addition , the proportion of cells containing two dif2 spots reached 100% in the cells that were longer than 4 . 5 µm whereas only 50% of these cells displayed two dif1 spots ( Figure 1C , grey points ) ., Marker frequency analysis indicated that the earlier timing of appearance of cells with two dif2 foci was not due to an earlier timing of replication of dif2 compared to dif1 ( Figure S1A ) ., The same pattern of segregation was observed when the dif1 and dif2 labelling systems were switched , excluding any possible artefact linked to the visualization strategy ( Figure S3 ) ., Finally , dif2 sisters were found to segregate further away from each other and from mid-cell than dif1 sisters ( Figure 1G ) ., Taken together , these results suggest that in the vast majority of cases TerII sisters separated before cell division whereas TerI sister separation was delayed until the end of cell division ., We next investigated the influence of the MatP/matS macrodomain organization system on TerI and TerII segregation ., V . cholerae cells in which MatP was disrupted were slightly longer than wild-type cells ., In minimal medium , they had a median length of 3 . 77 µm ( Figure S2B ) ., Nevertheless , growth competition indicated that they lost less than 0 . 23% of fitness per generation ( Figure S4 ) ., The smallest cells had a single dif1 and a single dif2 spot , which were both positioned closer to mid-cell than in wild-type cells ( Figure 1D ) ., This was accompanied by an increase in position variability ( Figure 1D ) ., As a consequence , mid-cell recruitment was no longer directly observable in cells of intermediate lengths ( Figure 1D ) ., In addition , the timing of separation of dif1 spots was now very similar to the timing of separation of dif2 spots ( Figure 1E ) ., Marker frequency analysis indicated that this was not due to a change in the relative replication timing of dif1 and dif2 ( Figure S1B ) ., Many cells of intermediate length now displayed two dif1 and two dif2 spots and most of the cells of the following bins had two dif1 and two dif2 spots ( Figure 1E ) ., This was directly reflected in the proportion of cells displaying a single dif1 spot and a single dif2 spot and the proportion of cells with two dif1 and two dif2 spots in the entire population ( Figure 1F , number of spots ) ., The separation of dif2 sisters remained slightly ahead of the separation of dif1 sisters ( Figure 1E ) , which was reflected in the higher proportion of cells harbouring a single dif1 spot and two dif2 spots than cells harbouring a single dif2 spot and two dif1 spots ( Figure 1F , 3 spots disposition ) ., However , the disposition of spots became more random and many cells now displayed dif2 spots more centrally located than dif1 spots ( Figure 1F , 3 spots disposition and 4 spots disposition ) ., Finally , sister dif sites migrated to opposite cell halves after their separation ( Figure 1D ) and the distances between the sisters of both sites were similar ( Figure 1G ) ., Taken together , these results suggested that MatP contributed to the precise positioning of TerI before and after replication and that it delayed the separation of TerI sisters to the time of cell division ., MatP also contributed to the precise positioning of TerII ., However , it was unable to impede TerII sisters from separating before septum constriction ., As the densities of matS sites in TerI and TerII are very similar , we were intrigued by the apparent inability of MatP to block TerII sister separation ., Lesterlin et al . designed an assay based on the interruption of the lacZ reporter gene by two copies of loxP to detect sister chromatid contacts ( SCC ) behind replication forks 33 ., The assay was based on the proximity of the loxP sites: the cleavage points of the Cre recombinases on each strand of the tandem sites were separated by only 55 bp to prevent intra-molecular recombination ., As a result , a functional lacZ ORF could only be reconstructed via intermolecular recombination events ( Figure 2A ) ., As dif-recombination is under the control of FtsK in V . cholerae 26 , which was expected to restrict it to mid-cell and to the time of septum constriction 21 , we reasoned that 55 bp dif-cassettes could be used to monitor the proximity of TerI and TerII sisters to the cell division machinery at the time of constriction ( Figure 2B ) ., We engineered a strain in which XerC production was under the control of the arabinose promoter to permit the stable inheritance of dif-cassettes ., To help repress any leaky XerC production , we inserted the E . coli lacZ promoter and the E . coli lacI repressor gene in anti-orientation at the end of the xerC ORF ., We also replaced the ATG translation initiation codon by the less favourable TTG codon and removed the ribosomal binding site ( Figure 2B ) ., The dif sites harboured by the first and second chromosomes of the El Tor N16961 strain , dif1 and dif2 , possess divergent overlap regions ( Figure 2C , 26 , 34 ) ., To compare the excision of 55 bp dif1- and dif2-cassettes ( lac2dif1 and lac2dif2 ) , we inserted them at the same genomic position , in place of the dif locus of chromosome II , and monitored the frequency of full blue colonies that were obtained three hours after the induction of XerC production ( Figure 2C ) ., Recombination worked well for both dif sites ( Figure 2D ) ., In both cases , blue colony formation strictly depended on XerC production and on the presence of a fully functional ftsK allele ( Figure 2D ) ., Little or no recombination can occur between dif1 and dif2 thanks to their sequence divergence ( Figure 2C ) ., The use of lac2dif2 on chrI and lac2dif1 on chrII thus prevented any risk of Xer-mediated intrachromosomal rearrangements due to recombination between the dif sites of the cassette and the dimer resolution site of the chromosome during the course of the experiment ( Figure 2E ) ., Therefore , the dimer resolution site of the chromosome could be left , which avoided any artefact in the measured excision frequencies linked to the formation of chromosome dimers by recombination between sister copies of the cassettes ( Figure S5 ) ., The dif sites of the cassettes used on each of the two V . cholerae chromosomes are identical to the dimer resolution site of the other chromosome ., However , this site did not influence the proportion of blue colonies that were formed ( Figure 2E and Figure S6 ) ., Both intramolecular and intermolecular recombination events can generate single dif site products ., In contrast , three dif site products can only be generated via intermolecular recombination ., Such products are transient because they can be converted to single dif products by subsequent intramolecular recombination ( Figure 2A ) ., Nevertheless , we could detect their appearance with 55 bp cassettes , demonstrating that recombination occurred via SCC ( Figure 3A ) ., As a point of comparison , we engineered 1 kbp dif-cassettes , a distance sufficient for intramolecular recombination ., With such cassettes , we did not observe any intermolecular recombination intermediates , suggesting that 1 kbp cassette excision mainly resulted from intramolecular recombination events on separate chromatids ( Figure 3A ) ., FtsK-YFP localized to mid-cell in long cells ( Figure 3B , white arrow ) and at one of the two poles in short cells ( Figure 3B , white arrow head ) ., This was reminiscent of the pattern of localization of the cell division machinery of Caulobacter crescentus , which assembles at mid-cell but remains bound to the new pole after cell scission 35 ., Time-lapse observations confirmed that such a scenario applied to V . cholerae FtsK , demonstrating that it assembled at mid-cell as part of the cell division machinery ( Figure S7A ) ., In addition , treating cells with cephalexin , which blocks septum constriction , led to a dramatic reduction in the level of dif-recombination without affecting the recruitment of FtsK to the cell division apparatus ( Figure 3C ) ., No loss of cell viability was observed during the course of the cephalexin treatment ( Figure S7B ) ., We conclude that dif-recombination occurs during or shortly after septum constriction in V . cholerae ., Finally , deletion of recA did not affect the proportion of excision events that could be detected using 55 bp- and 1 kbp-cassettes , indicating that activation of dif-recombination was independent from chromosome dimer formation in V . cholerae ( Figure 3D ) ., This result is strikingly different from what is observed using dif-cassettes in E . coli 36 , 37 ., The reasons for this difference are the subject of another study ( Gally , Midonet , Demarre and Barre , unpublished results ) ., Taken together , these results demonstrate that the proportion of blue colonies formed following lac2dif1 and lac2dif2 recombination events can be used as a relative measure of the respective frequency of contacts between monomeric sister chromatids that occur at mid-cell at the time of septum constriction in V . cholerae ., Cells in which lac2dif2 were inserted in the immediate vicinity of dif1 yielded a high level ( ∼60% ) of blue colonies , demonstrating dif1 SCC during constriction ( Figure 4A ) , in agreement with the co-localization of dif1 sisters ( Figure 1 ) ., However , interchromatid recombination dropped rapidly when lac2dif2 was not in the immediate vicinity of the dif1 locus ( Figure 4A ) ., The frequency of blue colony formation did not diminish in cells in which recA was deleted , confirming that 55 bp cassette recombination on chrI was not restricted to chromosome dimers ( Figure S8A ) ., Strikingly , we obtained a very high proportion of blue colonies ( ∼90% ) when lac2dif1 was inserted at dif2 ( Figure 4B ) despite the apparent early separation of dif2 sisters ( Figure 1 ) ., In addition , blue colony formation remained high ( ∼45% ) within a 160 kbp region surrounding dif2 , from a position at 9 kb on the left of the dif locus to 152 kb on the right of it ( Figure 4B ) ., The same results were obtained after recA deletion , confirming that TerII SCCs were unlikely due to chromosome dimers ( Figure S8A ) ., Taken together those results suggested that dif2 sisters contacted each other at mid-cell at the time of cell division as frequently as dif1 sisters , despite their apparent early separation ., On chrII , the extent of the region displaying a high frequency of SCC at the time of septum constriction corresponded to the putative MatP domain ( Figure 4B ) ., The only notable exception was next to a matS site that is isolated from the rest of the matS region by the V . cholerae superintegron ( Figure 4B ) ., Correspondingly , we observed more than a 4-fold reduction in blue colony formation within TerII upon matP disruption ( Figure 4C ) ., Indeed , dif2 was the only locus where cassette excision remained above the background level ( Figure 4C ) ., Cassette excision remained independent from chromosome dimer formation ( Figure S8B ) ., In contrast , the disruption of matP only had a very modest , albeit significant , effect on SCCs within TerI ( Figure 4C ) ., The remaining SCCs were still independent from homologous recombination ( Figure S8B ) ., Correspondingly , SCCs occurred in a much smaller region than the putative MatP domain on chrI ( Figure 4B ) ., Taken together , these results suggested that MatP was the main contributor to TerII SCC occurring at mid-cell at the time of cell division ., The high frequency of SCCs detected at dif2 with our genetic assay suggested that dif2 sisters frequently collided at mid-cell during septum constriction despite their early separation ., To directly demonstrate that such collisions occurred , we followed the segregation dynamics of dif2 sisters by time-lapse fluorescence microscopy ., We expected collisions to be transient because two dif2 spots were observed in almost all of the wild type cells longer than 4 . 5 µm ( Figure 1 and Figure S9A ) ., Therefore , we reasoned that short time intervals had to be used between each image acquisition ., However , a balance had to be achieved between the detection of the supposedly transient dif2 collisions and the fraction of the cell cycle during which dif2 spots could be tracked in any given cell due to photobleaching ., With 30 s time intervals , dif2 foci could be observed for 100 min ., A total of 74 wild-type cells were followed , out of which 44 showed a complete cell division event ., In 42 of these cells , i . e . in ∼95% of the observed cell division events , dif2 sisters separated before septum invagination , in agreement with our snapshot analysis ., However , dif2 sister collisions were frequent ( Figure 5A and Movie S1 ) ., As a result , dif2 sisters were found to co-localize at mid-cell at some stage of the cell constriction process in 70% of the cells , which fits with the high frequency of dif2 SCCs observed with the genetic assay ( Figure 5A and Movie S1 ) ., On average , 3 . 2 collisions were observed after the initial separation of the dif2 sisters and before cell fission ., In the majority of cases , re-joining of the dif2 sisters was transient , i . e . co-localization was only observed during 2 consecutive frames ., In some instances , however , dif2 sisters remained co-localized for several minutes ., We also followed 131 matP− cells , out of which 30 displayed a complete analysable cell division event ., In all of these cells , dif2 sisters separated before septum invagination ( Figure 5A and Movie S2 ) ., The positions of the two dif2 sisters were no longer restricted to the ¼–¾ cell region and , in several cases , one of the two dif2 spots located near the old pole at the time of division ( Figure 5A and Movie S2 ) ., Indeed , only 0 . 6 collisions were observed on average in each cell after the initial separation of the dif2 sisters and before cell fission ., These events lasted for a single frame in the vast majority of cases ., Finally , co-localization of the dif2 sisters during septum constriction was only observed once , which fits with the loss of dif2 SCCs monitored with the genetic assay ., Taken together , these results suggested that MatP allowed FtsK to process dif2 sisters during cell division by restricting the range of their movements to the ¼–¾ cell region and that other factors played a similar role for dif1 sisters in its absence ., Possibly the most striking observation of our study was that TerII sisters kept colliding against each other at mid-cell after their initial separation in the cell cycle , up to and after the initiation of the constriction process ( Figure 4 and 5 ) ., During the three hours of our genetic assays , cells underwent ∼8 divisions , as judged by the number of colony forming units at the beginning and at the end of the experiments ., Therefore , the ∼90% frequency of blue colony formation that we observed with a recombination inserted at dif2 corresponded to a rate of 25% of β-galactosidase+ cell formation per generation ., As only one out of the two possible intermolecular recombination events could yield β-galactosidase+ cells ( Figure 2A ) , this result suggested that >50% of SCC occurred between TerII sisters during each cell division event ( Figure 4 ) ., Moreover , we observed the same frequency of blue colony formation with the lacZdif2 probe when it was inserted at the dif2 locus on chrII ( Figure 2D , lac2dif2 ) and when it was inserted at the dif1 locus on chrI ( Figure 4B , dif1 locus ) , suggesting that SCCs at cell division were as frequent within TerII as within TerI ., Accordingly , frequent collisions of dif2 sisters were observed at the time of cell division when following the growth of individual cells by fluorescence microscopy with 30 s time intervals ( Figure 5 ) ., Interchromatid recombination events during constriction were observed in a specific 160 kb region of chrII , which corresponded to the putative MatP domain of the chromosome ( Figure 4 ) ., The relative frequency of interchromatid recombination curve consisted of a plateau with a central peak at the dif2 locus ( Figure 4 ) ., Our results suggested the plateau was due to the action of the MatP/matS system ( Figure 4 ) ., Our snapshot analysis of the positioning of dif1 in wild type and matP− cells indicated that MatP was a major contributor to the organization and management of TerI at the time of cell division , as observed in E . coli ( Figure 1 ) ., However , the relative frequency of interchromatid recombination curve on chrI simply consisted of a sharp peak centred on dif1 with no plateau in the MatP region ( Figure 4 ) ., In addition , the relative frequency of SCCs was not dramatically affected in matP− cells ( Figure 4 ) ., This is in sharp contrast to what we could have expected based on the role of MatP in the formation of a FtsK loading region in E . coli 13 ., Taken together , these observations suggest that other factors than MatP contribute to the management of dif1 sisters at the time of cell division , which partially masked its action in our genetic assay ., We are currently investigating the relative contribution of likely candidates for TerI mid-cell localization using the power of our SCC assay ., We think that these factors might be common to other bacteria in which sister copies of the terminus regions remain at mid-cell for a long period during cell division , such as P . aeruginosa and C . crescentus ., However , they could not , or might not yet , be adapted to the management of the recently acquired chrII of V . cholerae ., As a result , the MatP/matS system was left as the sole contributor for TerII SCCs during cell division , which helped reveal its action ., The disruption of matP had a profound impact on the subcellular localization of dif1 and dif2 ( Figure 1 ) ., In particular , MatP seemed to impede the separation of dif1 sisters until cell division ( Figure 1 ) ., MatP is able to create bridges between two matS sites 38 ., However , we do not think that sister chromatids are tethered together by such bridges since MatP did not impede the separation of dif2 sisters ( Figure 1 ) ., Careful analysis of the location of dif1 and dif2 spots in wild type and matP− cells rather suggested that MatP helped create a molecular leash that confined Ter regions in the ¼–¾ portion of the cell: even though the median positions of dif2 sisters in the cell population indicated their separation before cell division , they did not migrate very far apart from each other and away from mid-cell ( Figure 1 ) ., In particular , ∼90% of dif2 spots were located at a distance of less than a quarter of the cell length in cells longer than 4 . 5 µm ( Figure S9A ) ., Results from our genetic assay suggested that the movements of such sister loci around the median position probably allowed for their frequent collision at mid-cell at the time of cell division ., Even though their medians were equivalent , the distributions of the distances between dif2 sisters in wild type and matP− cells were markedly different ( Figure 1G ) ., Indeed , in matP− cells longer than 4 . 5 µm , only ∼57% of the dif2 spots remained in the ¼–¾ portion of the cell ( Figure S9B ) ., This might be sufficient to explain a large drop in sister collisions ., In contrast , ∼83% of dif1 spots remained in the ¼–¾ portion of the cell , which might explain the low impact of the matP disruption on the frequency of SCC ( Figure S9C and S9D ) ., Further work will be required to investigate the molecular nature of the MatP leash ., An attractive possibility would be that MatP restrains the movement of catenation loops between the two circular chromatid sisters by binding together the matS sites of each sister chromatid ., Our results suggest that multiple redundant factors , including MatP in the enterobacteriaceae and the Vibrios , ensure that sister copies of the terminus region of bacterial chromosomes remain sufficiently close to mid-cell to be processed by FtsK ., In this regard , it is remarkable to observe that , even though initiation of chrII replication responds to the same global cell cycle regulatory networks than chrI initiation 39 , it occurs at a later time point in the cell cycle 28 , which results in synchronous chrI and chrII replication termination ( Figure S1 , 28 ) ., This is likely to participate in delaying TerII sister separation until the time of cell division ., We observed that matP− cells were longer than wild type cells in agreement with the notion that coordination of cell division and chromosome segregation is a key feature of the bacterial cell cycle ( Figure S1 ) ., What is the functional role of this coordination ?, The late segregation of the terminus region might facilitate the action of FtsK on unresolved catenation links or chromosome dimers ., Under laboratory conditions , we did not observe any significant chromosome dimer resolution defect ( Figure S4 ) ., However , these results have to be interpreted with caution since the disorganization induced by the absence of MatP should only slightly delay the time required for FtsK to bring together sister dif sites ., Genetic engineering methods are described in Text S1 ., Bacterial strains and plasmids used in this study are listed in Tables S1 and S2 , respectively ., All V . cholerae strains were derivatives of the El Tor N16961 strain ., A lacO array was inserted adjacent to dif1 and a PMT1 parS was inserted adjacent to dif2 ., LacIE . coli-mCherry and yGFP-ParBpMT1 were produced via the leaky expression of a synthetic operon under the E . coli lacZ promoter that was inserted at the V . cholerae lacZ locus ., A C-terminal fusion between FtsK and a yellow fluorescent protein , FtsK-YFP , was inserted in place of the endogenous V . cholerae ftsK allele to visualize its localisation ., Protocols for Microscopy are detailed in Text S1 ., The snapshot images were analysed using the Matlab-based sofware MicrobeTracker 40 , 41 ., Details for the analysis are described in 27 ., For bright field ( BF ) and fluorescence microscopy 2 µl of an exponentially growing culture sample were placed on a microscope slide coated with a thin agarose layer ( 1% ) made using the growth medium ., The slide was incubated at 30°C during the images acquisition ., The images were acquired with an Evolve 512 EMCCD camera attached to an Axio Observe spinning disk from Zeiss and recorded every 30 seconds with step size of 0 . 4 µm in the Z-axis ( 3 images were acquired for each channel ) ., The BF image 3 is subtracted from the BF image 1 to obtain the phase image ., Blue colony formation assay: 0 . 2 m

Introduction, Results, Discussion, Methods

The replication terminus region ( Ter ) of the unique chromosome of most bacteria locates at mid-cell at the time of cell division ., In several species , this localization participates in the necessary coordination between chromosome segregation and cell division , notably for the selection of the division site , the licensing of the division machinery assembly and the correct alignment of chromosome dimer resolution sites ., The genome of Vibrio cholerae , the agent of the deadly human disease cholera , is divided into two chromosomes , chrI and chrII ., Previous fluorescent microscopy observations suggested that although the Ter regions of chrI and chrII replicate at the same time , chrII sister termini separated before cell division whereas chrI sister termini were maintained together at mid-cell , which raised questions on the management of the two chromosomes during cell division ., Here , we simultaneously visualized the location of the dimer resolution locus of each of the two chromosomes ., Our results confirm the late and early separation of chrI and chrII Ter sisters , respectively ., They further suggest that the MatP/matS macrodomain organization system specifically delays chrI Ter sister separation ., However , TerI loci remain in the vicinity of the cell centre in the absence of MatP and a genetic assay specifically designed to monitor the relative frequency of sister chromatid contacts during constriction suggest that they keep colliding together until the very end of cell division ., In contrast , we found that even though it is not able to impede the separation of chrII Ter sisters before septation , the MatP/matS macrodomain organization system restricts their movement within the cell and permits their frequent interaction during septum constriction .

The genome of Vibrio cholerae is divided into two circular chromosomes , chrI and chrII ., ChrII is derived from a horizontally acquired mega-plasmid , which raised questions on the necessary coordination of the processes that ensure its segregation with the cell division cycle ., Here , we show that the MatP/matS macrodomain organization system impedes the separation of sister copies of the terminus region of chrI before the initiation of septum constriction ., In its absence , however , chrI sister termini remain sufficiently close to mid-cell to be processed by the FtsK cell division translocase ., In contrast , we show that MatP cannot impede the separation of chrII sister termini before constriction ., However , it restricts their movements within the cell , which allows for their processing by FtsK at the time of cell division ., These results suggest that multiple redundant factors , including MatP in the enterobacteriaceae and the Vibrios , ensure that sister copies of the terminus region of bacterial chromosomes remain sufficiently close to mid-cell to be processed by FtsK .

cell biology, genetics, biology and life sciences, microbiology, molecular cell biology

journal.pgen.1003477

The Genomic Signature of Crop-Wild Introgression in Maize

Hybridization and subsequent introgression have long been appreciated as agents of evolution ., Adaptations can be transferred through these processes upon secondary contact of uniquely adapted populations or species , in many instances producing the variation necessary for further diversification 1 ., Early considerations of adaptive introgression discussed its importance in the context of both domesticated and wild species 2 , 3 , viewing both anthropogenic disturbance and naturally heterogeneous environments as ideal settings for hybridization ., More recently , studies of adaptation through introgression have focused primarily on wild species ( 4 , 5 but see 6 , 7 ) ., Well-studied examples include increased hybrid fitness of Darwins finches following environmental changes that favor beak morphology intermediate to that found in extant species 8 , 9 and the introgression of traits related to herbivore resistance 10 and drought escape 11 in species of wild sunflower 12 , 13 ., Molecular and population genetic analyses have also clearly identified instances of adaptive introgression across species at individual loci , including examples such as the RAY locus controlling floral morphology and outcrossing rate in groundsels 14 ) and the optix gene controlling wing color in mimetic butterflies 15 , 16 ., Despite long-standing interest in introgression , however , genome-wide analyses are rare and have been primarily conducted in model systems 17–22 ., Studies of natural introgression in cultivated species have been limited in genomic scope and have largely ignored the issue of historical adaptive introgression , focusing instead on contemporary transgene escape and/or the evolution of weediness 23–27 ., One notable exception is recent work documenting introgression between different groups of cultivated rice in genomic regions containing loci involved in domestication 19 , 28–30 ., Few studies , however , have investigated the potential for introgression to transfer adaptations between crops and natural populations of their wild relatives post-domestication ., Subsequent to domestication , most crops spread from centers of origin into new habitats , often encountering locally adapted populations of their wild progenitors and closely related species ( e . g . , 31–33 ) ., These crop expansions provide compelling opportunities to study evolution through introgressive hybridization ., Here , we use a SNP genotyping array to investigate the genomic signature of gene flow between cultivated maize and its wild relative Zea mays ssp ., mexicana ( hereafter , mexicana ) and examine evidence for adaptive introgression ., Maize was domesticated approximately 9 , 000 BP in southwest Mexico from the lowland teosinte taxon Zea mays ssp ., parviglumis ( hereafter , parviglumis; 34–36 ) ., Following domestication , maize spread to the highlands of central Mexico 34 , 37 , a migration that involved adaptation to thousands of meters of changing elevation and brought maize to substantially cooler ( ∼7°C change in annual temperature ) and drier ( ∼300 mm change in annual precipitation ) climes 38 ., During this migration maize came into sympatry with mexicana , a highland teosinte that diverged from parviglumis ∼60 , 000 BP 39 ., Convincing morphological evidence for introgression between maize and mexicana has been reported 40 , 41 , and traits putatively involved in adaptation to the cooler highland environment such as dark-red and highly-pilose leaf sheaths 42 , 43 are shared between mexicana and highland maize landraces 40 , 44 ., These shared morphological features could suggest adaptive introgression 45 but could also reflect parallel or convergent adaptation to highland climate or retention of ancestral traits 46 ., Though hybrids are frequently observed , phenological isolation due to flowering time differences 40 , 47 and cross-incompatibility loci 48–50 are thought to limit the extent of introgression , particularly acting as barriers to maize pollination of mexicana ., Experimental estimates of maize-mexicana pollination success ( i . e . , production of hybrid seed ) are quite low , ranging from <1–2% depending on the direction of the cross 51 , 52 ., Nevertheless , theory suggests that alleles received through hybridization can persist and spread despite such barriers to gene exchange , particularly when they prove adaptive 53 , 54 ., Molecular analyses over the last few decades have provided increasingly strong evidence for reciprocal introgression between mexicana and highland maize landraces ., Early work identified multiple allozyme alleles common in highland Mexican maize and mexicana but rare in closely related taxa or maize outside of the highlands 55 ., Likewise , sequencing of the putative domestication locus barren stalk1 ( ba1 ) revealed a haplotype unique to mexicana and highland Mexican maize 56 ., Multiple studies have found further support for bidirectional gene flow and have estimated that ∼2–10% of the genome of highland maize is derived from mexicana 34 , 57 and 4–8% of the mexicana genome is derived from maize 58 ., A more recent study including several hundred markers revealed that admixture with mexicana may approach 20% in highland Mexican maize 36 ., Similar to introgression studies in many other plant species ( e . g . , 31 , 59–62 ) , morphological and molecular studies have only provided rough estimates of the extent of introgression between mexicana and maize ., Little is known regarding genome-wide patterns in the extent and directionality of gene flow ., A genomic picture of introgression could greatly expand our understanding of evolution through hybridization , revealing how particular alleles , genes and genomic regions are disproportionately shaped by and/or resistant to these processes 63 , 64 ., Additionally , assessment of introgression in crop species during post-domestication expansion can provide insight into the genetic architecture of adaptation to newly encountered abiotic and biotic conditions ., Here , we provide the most in-depth analysis to date of the genomic extent and directionality of introgression in sympatric collections of maize and its wild relative , mexicana , based on genome-wide single nucleotide polymorphism ( SNP ) data ., We find evidence for pervasive yet asymmetric gene flow in sympatric populations ., Across the genome , several regions introgressed from mexicana into maize are shared across most populations , while little consistency in introgression is observed in gene flow in the opposite direction ., These data , combined with analysis of environmental associations and a growth chamber experiment , suggest that maize colonization of highland environments in Mexico may have been facilitated by adaptive introgression from local mexicana populations ., To assess the extent of hybridization and introgression we collected nine sympatric population pairs of maize and mexicana and one allopatric mexicana population from across the highlands of Mexico ( Table S1; Figure, 1 ) and genotyped 189 individuals for 39 , 029 SNPs ( see Materials and Methods ) ., Genotype data at the same loci were obtained from Chia et al . 65 for a reference allopatric maize population ., Average expected heterozygosity ( HE ) , percent polymorphic loci ( %P ) , and the proportion of privately segregating sites were higher in maize than mexicana ( t-test , p≤0 . 012 for all comparisons , Table S2 ) , likely influenced by the absence of mexicana from the discovery panel used to develop the genotyping platform 66 ., However , substantial variation in diversity was observed across populations within taxa ( e . g . , %P ranged from 52–88% in maize and from 44–79% in mexicana ( Table S2 ) ) and meaningful comparisons can be made at this level ., Our analysis of diversity identified the Ixtlan maize population as an extreme outlier , containing 31% fewer polymorphic markers than any other maize population ., Discussion with farmers during our collection revealed that Ixtlan maize was initially a commercial variety whose seed had been replanted for a number of generations ., Excluding this population , diversity in mexicana populations varied much more substantially than in maize ( e . g . , variance in %P across mexicana populations was 7-fold higher; Table S2 ) At the population level , summary statistics of diversity and differentiation were consistent with sympatric gene flow ( i . e . , local gene flow based on current plant distributions ) between maize and mexicana ( Figure 2 ) ., First , %P was positively correlated between sympatric population pairs ( R2\u200a=\u200a0 . 65; p\u200a=\u200a0 . 016; Figure 2A ) , though this trend could reflect local conditions affecting diversity in both taxa rather than gene flow ., Second , in a subset of populations , the proportion of shared polymorphisms was higher ( Figure 2B ) and pairwise differentiation ( FST ) was lower ( Figure 2C ) between sympatric population pairs than in allopatric comparisons ., Finally , an individual-based STRUCTURE analysis assuming two groups ( K\u200a=\u200a2 ) revealed strong membership of reference allopatric individuals of maize and mexicana in their appropriate groups ( 96% and 99% respectively ) , yet appreciable admixture in sympatric populations ( Figure 2D ) ., Four recent hybrids were identified ( 3 mexicana and 1 maize ) with <60% membership in their respective groups ., STRUCTURE analysis also indicated that gene flow was asymmetric , with more highland maize germplasm derived from mexicana ( 19% versus 12% of mexicana germplasm from maize ) ., Assignment at higher K values continued to indicate admixture in mexicana populations but not in maize , suggesting that gene flow from mexicana into maize may have been more ancient ( Figure S1 ) ., Consistent with this interpretation , median values of the f3 statistic 67 for SNPs genome-wide were negative or zero for 8 of 9 sympatric maize populations ( Figure S2 ) ; only the Ixtlan maize population showed a positive median f3 signifying a lack of admixture ., Collectively , these population-level summaries are suggestive of historical gene flow from mexicana into maize and , in a subset of populations , of ongoing sympatric gene flow from maize into mexicana ., Meaningful information regarding the evolutionary significance of introgression can often be obscured in population-level summaries ., However , the large number of SNPs in our data set allowed us to assess variation in the extent of introgression across the genome ., We made use of two complementary methods ., First , we employed the hidden Markov model of HAPMIX 68 to infer ancestry of chromosomal segments along the genomes of individuals from maize and mexicana populations through comparison to reference allopatric populations ., Subsampling of the reference allopatric populations ( see Materials and Methods ) revealed considerable signal of introgression in the maize reference panel , particularly in low recombination regions of the genome near centromeres ( correction for this signal is illustrated in Figure 3 and Figure S3 ) ., While this signal could represent genuine introgression predating allopatry , it could also indicate potential false positives in genomic regions with high linkage disequilibrium or less data ., We therefore added a complementary analysis using the linkage model of STRUCTURE 69 , 70 to conduct site-by-site assignment across the genomes of mexicana and maize ., Because STRUCTURE takes allele frequencies across all populations into account during assignment , the approach is robust to potential deviations of individual reference populations from ancestral frequencies ., Both methods allowed quantification of introgression along the genome for individual samples ., Rather than investigate every putative introgression , however , we focused further analyses on genomic regions with a high frequency of introgression , requiring an average of one chromosome or 50% assignment to the opposite taxon per individual in a given population ( Figure 3; Figure S3; referred to as “introgressed regions” hereafter ) ., Approximately 19 . 1% and 9 . 8% of the genome met this criterion in the HAPMIX and STRUCTURE scans respectively for mexicana introgression into maize ., In the opposite direction , we observed lower proportions at this threshold ( 11 . 4% in the case of HAPMIX and 9 . 2% using STRUCTURE ) , corroborating asymmetric gene flow favoring mexicana introgression into maize ., Both scans showed a disproportionate number of introgressed regions shared across populations in mexicana-to-maize gene flow ., Roughly 50% of regions introgressed from mexicana into maize were shared across seven or more populations in the HAPMIX scan , whereas only 4% of introgressed regions had this level of sharing from maize into mexicana; similar asymmetry was observed using STRUCTURE ( 12% versus <1% ) ., By comparing composite likelihood scores from HAPMIX across individuals within each population , we were able to characterize relative times since admixture ( see Materials and Methods ) ., We observed qualitative differences between maize and mexicana ., The likelihood of the admixture time parameter began to decrease markedly after an average of 83 generations in mexicana populations , whereas the decrease in maize was much more gradual and did not occur until after an average of 174 generations ( Figure S4; averages exclude Ixtlan ) suggesting older introgression from mexicana into maize ., A notable exception to this trend was observed in the Ixtlan sympatric population pair , where the maize population was likely derived in the recent past from a commercial variety and introgression appeared to be more recent from mexicana into maize ( Figure S4 ) ., For further population genetic characterization , we focused on the subset of introgressed regions identified in both the HAPMIX and STRUCTURE scans , an approach that should be robust to the individual assumptions of the two methods ., These regions spanned an average of 3 . 6% of the genome in the case of mexicana-to-maize introgression and 3 . 2% for maize-to-mexicana introgression ( Figure 3C; Figure S3 ) ., As expected , differentiation between sympatric maize and mexicana was reduced in these introgressed regions in both directions of gene flow ( mean 25% reduction of FST mexicana-to-maize , 33% reduction maize-to-mexicana , t-test , p<0 . 001 for all population-level comparisons of introgressed vs . non-introgressed regions in both directions of gene flow ) ., Introgressed regions also showed more shared and fewer fixed and private SNPs ( Table S3 ) , as well as longer tracts of identity by state ( IBS ) between maize and mexicana ( t-test , p<<0 . 001 ) ., Consistent with these results , diversity in introgressed regions was generally different from non-introgressed regions in the recipient taxon and instead comparable to diversity in non-introgressed regions in the taxon of origin ( Table S3 ) ., In total , we identified nine regions of introgression from mexicana to maize found by both methods and present in ≥7 sympatric population pairs ( Table S4 ) ., Three of these shared regions of introgression span the centromeres of chromosomes 5 , 6 , and 10 ( Figure S3 ) , suggesting that maize from the highlands of Mexico may in fact harbor mexicana centromeric or pericentromeric sequence ., No such shared introgressions were found in the opposite direction of gene flow ( maize into mexicana ) ., Finally , we characterized regions of the genome notably lacking evidence of introgression ., We refer to regions with ≤5% probability of introgression confirmed by both scans in ≥7 populations as being resistant to introgression ( Figure S5 ) ., In both directions of gene flow , we found these genomic regions to have elevated differentiation , decreased diversity , fewer shared variants , more fixed differences , and a higher number of privately segregating SNPs in the opposite taxon ( Table S3 ) ., Two non-mutually exclusive hypotheses of adaptive introgression can be readily discerned for gene flow between mexicana and maize:, 1 ) as its natural habitat was transformed , mexicana received maize alleles conferring adaptation to the agronomic setting and, 2 ) as it diffused to the highlands of central Mexico from the lowlands of southwest Mexico , maize received alleles conferring highland adaptation from mexicana , which was already adapted to these conditions ., To evaluate evidence for the first hypothesis we gauged enrichment of 484 candidate domestication genes 71 in regions of introgression ., We hypothesized that if maize donated alleles adaptive for the agronomic setting to mexicana , we would detect enrichment of domestication loci in regions introgressed from maize into mexicana ., However , compared to the rest of the genome , introgressed regions in both directions of gene flow harbored significantly fewer domestication candidates ( permutation test , p≤0 . 001 ) , while regions resistant to introgression showed an excess of domestication candidates ( permutation test , p\u200a=\u200a0 . 121 maize to mexicana , p\u200a=\u200a0 . 008 mexicana to maize; Figure S5 ) ., For example , two well-characterized domestication genes affecting branching architecture , grassy tillers1 ( gt1; 72 , 73 ) and teosinte branched1 ( tb1; 74 ) showed very little evidence of introgression ( Figure S5 ) ., Introgression also appeared to be rare from maize into mexicana across much of the short arm of chromosome 4 , a span that includes the domestication loci teosinte glume architecture1 ( tga1; 75 ) , sugary1 ( su1; 76 ) and brittle endosperm2 ( bt2; 76 ) and the well characterized pollen-pistil incompatibility locus teosinte crossing barrier1 ( tcb1; 48 ) that serves as a hybridization barrier between maize and mexicana ( Figure S5 ) ., These results suggest selection against introgression at loci that contribute to domestication and reproductive isolation ., Several lines of evidence support the hypothesis that maize received introgression conferring highland adaptation from mexicana ., Across the nine shared introgressed regions , five contained long stretches ( >300 kb ) of zero diversity across seven populations , implying a common introgressed haplotype ( Figure S6 ) ., Given that these regions only have 5–15 SNPs , however , higher-density genotyping might resolve additional haplotypes ., Additionally , we used the method of Coop et al . 77 to detect associations of population allele frequencies with 76 environmental variables ( see Materials and Methods ) ., Environmental variables were reduced in dimensionality to four principal components that captured 95% of environmental variation ., We found that loci associated with the second principal component ( loaded primarily by temperature seasonality ) were significantly enriched ( permutation test , p\u200a=\u200a0 . 017 ) in genomic regions introgressed from mexicana into maize , but no significant enrichment was observed in regions introgressed from maize into mexicana ., We then compared the nine regions of introgression found in ≥7 populations of maize to QTL for anthocyanin content and leaf macrohairs ( putatively adaptive traits under highland conditions ) identified in a previous study from a cross between parviglumis ( lowland teosinte ) and mexicana ( highland teosinte ) 42 ., Six of the introgressed regions overlapped with five of the six genomic regions with QTL detected for these traits ., Two of the shared introgressions that overlapped with QTL are of particular interest due to their previous characterization ., One of these , on chromosome 4 , overlaps with QTL for both pigment intensity and macrohairs 42 , and maps to the same position as a recently identified putative inversion polymorphism showing significant differentiation between parviglumis and mexicana ( 78; Figure 4A ) ., The second region , on chromosome 9 , overlaps with a QTL for macrohairs 42 and includes the macrohairless1 ( mhl1 ) locus 79 that promotes macrohair formation on the leaf blade and sheath of maize ( Figure 4B ) ., The two lowest elevation maize populations in our study ( Puruandiro and Ixtlan ) showed a conspicuous lack of introgression in these two genomic regions ( Figure 4A and 4B ) ., Analysis of pairwise differentiation ( FST ) between these populations and two populations showing fixed introgression in the two genomic regions ( Opopeo and San Pedro; Figure 4A and 4B ) revealed substantial differentiation: the region on chromosome 4 contained the only fixed SNP differences genome-wide ( Puruandiro/Ixtlan versus Opopeo/San Pedro ) and a SNP in the region on chromosome 9 was an extreme FST outlier ., To explore the potential phenotypic effects of these genomic regions we conducted growth chamber experiments including ten maize plants from each of these four populations ., Under temperature and day-length conditions typical of the highlands of Mexico ( see Materials and Methods ) , the leaf sheaths of plants from populations where introgression was detected in the two genomic regions had 21-fold more macrohairs ( t-test , p\u200a=\u200a0 . 0002; Figure 4C and 4D ) , and showed greater pigmentation ( t-test , p\u200a=\u200a6E−06; Figure 4C and 4D ) ., Introgressed plants were also ∼25 cm taller ( t-test , p\u200a=\u200a6E−06; Figure 4D ) , a finding consistent with adaptation to highland conditions and potentially associated with increased fitness ., No significant difference in plant height was observed in a separate experiment under lowland conditions ( t\u200a=\u200atest , p\u200a=\u200a0 . 51 ) , and a significant interaction was observed between introgression status and environmental treatment ( ANOVA , F\u200a=\u200a4 . 151 , p\u200a=\u200a0 . 045 ) , with a disproportionate increase in plant height under lowland conditions in populations lacking introgression ( Figure S7 ) ., While our scans for introgression clearly indicated that mexicana has made genomic contributions to maize landraces in the highlands of Mexico , the broader contribution of mexicana to modern maize lines remained unclear ., Our HAPMIX and STRUCTURE analyses had low power to detect introgression distributed broadly in maize ( see Discussion ) ., Therefore , to assess potential ancestral contribution of mexicana to modern maize , we evaluated patterns of IBS between mexicana , parviglumis 78 and a global diversity panel of 279 modern maize lines 80 , 81 using the program GERMLINE ( 82; Figure 5 , Figure S8 and S9 ) ., Substantial IBS was found between mexicana and modern lines at a number of genomic locations ., To assess whether this IBS merely reflected shared ancestral haplotypes , we compared IBS between modern maize and parviglumis to IBS between modern maize and mexicana on a site-by-site basis , identifying regions in which various maize groups distinguished by Flint-Garcia et al . 81 showed stronger IBS with mexicana relative to parviglumis ( see Materials and Methods; Figure 5A; Figure S8 ) ., As each of the groups identified by Flint-Garcia have distinct evolutionary histories , it is possible that mexicana contributed differentially to the founders of each group ., For example , the tropical-subtropical , non-stiff-stalk , and mixed groups showed more genomic regions with stronger IBS with mexicana ( versus parviglumis ) than found in the stiff-stalk , popcorn , and sweetcorn groups ( ∼31% of sites with greater IBS with mexicana in the first group versus ∼23% in the latter group; Figure 5B and 5C ) ., Despite known pre-zygotic and phenological barriers to hybridization between maize and mexicana 47–50 , we have found evidence consistent with substantial reciprocal introgression ., Based on our population genetic analyses , several observations regarding the nature of this gene flow can be made:, 1 ) Gene flow appears to be ongoing and asymmetric , favoring mexicana introgression into maize ., 2 ) Gene flow from mexicana into maize is generally older than gene flow in the opposite direction ., 3 ) Haplotype diversity in nine genomic regions of mexicana-into-maize introgression shared across ≥7 populations suggests single , ancient introgressions followed by spread across the Mexican highlands ., 4 ) Introgression from mexicana into maize is restricted at domestication loci but enriched at loci putatively involved in highland adaptation ., 5 ) Genomic regions of mexicana/maize IBS within a global diversity panel of maize hint at a possible broader contribution of mexicana to modern improved maize ., Several of these observations are in line with previous research ., For example , the asymmetric gene flow we detect from mexicana to maize is consistent with findings of substantially higher pollination success in this direction 51 ., Asymmetric gene flow would also be expected based on phenology: in Mexico , maize typically flowers earlier than mexicana 47 and pollen shed in both taxa precedes silking ( female flowering ) ., Therefore , when maize silks are receptive , mexicana could potentially be shedding pollen , whereas when mexicana silks are receptive , maize tassels are more likely to be senescent ., Under these conditions , F1 progeny would be more likely to have a maize seed parent and a teosinte pollen parent and subsequent inadvertent planting of F1s in maize fields would bias the direction of gene flow ., Our data also provide support for previous assertions that shared morphological features between mexicana and maize represent adaptations derived from mexicana 45 rather than from maize 41 ., For example , we have found significant environmental correlations in genomic regions of mexicana-to-maize introgression ., We have also observed that overlap with QTL and fine-mapped loci for highland Zea traits ( e . g . , leaf sheath macrohairs and pigmentation ) are predominantly found in the direction of mexicana to maize gene flow ., Two such regions , on chromosomes 4 and 9 , showed particularly strong evidence of introgression ., Moreover , these genomic regions of introgression were more common in higher elevation maize populations in our sample , and maize populations with and without introgression in these regions showed differential morphology and greater plant height ( a proxy for fitness ) when grown under highland conditions ., In contrast , we found little evidence of adaptive introgression in the opposite direction of gene flow ., For example , domestication loci appeared resistant to gene flow from maize into mexicana , contradicting previous suggestions that gene flow from maize may have been required for mexicana to adapt to an agronomic setting 41 ., Instead it appears likely that mexicana , like other wild teosintes 83 , was a ruderal species adapted to open and disturbed environments even before the transformation of its natural habitat by maize cultivation ., Our detection of haplotype sharing between mexicana and a diverse panel of modern maize is consistent with previous findings suggesting the spread of introgressed mexicana haplotypes in maize outside of the highlands of Mexico 71 ., Both the STRUCTURE and HAPMIX methods we used to identify regions of introgression would likely not detect introgression found ubiquitously in modern maize ., Widespread mexicana introgression into maize would result in poor resolution between reference populations of these taxa in the HAPMIX analysis , and extensive haplotype sharing across maize and mexicana would result in a weak signature of introgression in STRUCTURE ., Further analysis of representative panels of mexicana , parviglumis and maize haplotypes at greater marker density should help clearly distinguish mexicana from parviglumis haplotypes and determine whether mexicana haplotypes are indeed widespread in maize ., While our results are consistent with previous research and the historical spread of maize , our power to detect introgression may be limited for a number of reasons ., First , our analysis conservatively focused on regions of introgression identified by two independent methods and shared across individuals within populations , undoubtedly missing a number of genuine instances of more limited gene flow ., Second , our markers were ascertained in a panel consisting entirely of maize ., In addition to inflating the diversity of maize relative to mexicana , this ascertainment scheme likely limited our ability to distinguish among mexicana haplotypes and thus to detect local introgression from mexicana into maize ., Third , the resolution of our data was on average one SNP per 80 kb , which could result in a bias toward detection of more recent introgression and introgression in low recombination regions of the genome ., Finally , mexicana only rarely occurs allopatric from maize 40 , and most populations have likely experienced gene flow at some point in time , thus complicating estimation of ancestral mexicana haplotypes and allele frequencies ., Many aspects of mexicanas contribution to highland adaptation in maize remain to be resolved ., While our growth chamber experiment was suggestive of adaptive introgression , the loci conferring these traits are still ambiguous ., Repetition of these experiments with mexicana/lowland maize near-isogenic introgression lines will be necessary to bolster the case for adaptive introgression ., Additionally , a particularly interesting comparison can be made between highland maize in central Mexico , a geographic region sympatric with mexicana , and highland maize in the Andes of South America where no inter-fertile wild Zea species can be found ., Future research should address whether highland adaptation in South American maize occurred in parallel to maize from Mexico 37 or whether pre-adapted highland maize was transported through Central America as some have suggested 84 ., The potential for adaptive introgression during crop expansion is of course not limited to maize ., Data from several crops ( e . g . , rice 19 , 85 , barley 86 , 87 , common bean 88 , and wheat 32 , 89 ) suggest defined centers of origin within a broader distribution of wild relatives ., The distributions of these crop-wild pairs span continents and a wide range of environments , and many are known to hybridize ( for a review , see 24 ) ., The methods we have applied here to maize and mexicana can therefore be replicated widely , perhaps revealing unexpected aspects of crop evolution and providing insight regarding the genetic architecture of local adaptation based on conserved regions of introgression ., Crops and related wild taxa can also be seen more broadly as models for the study of evolution through hybridization ., If crops are viewed as human-facilitated invasive species , clear connections can be made to theoretical work on introgression during invasion and range expansion ., For example , our finding of asymmetric gene flow from mexicana into maize is consistent with simulations showing that invaders should receive much higher levels of introgression from local species than occurs in the opposite direction due to differences in population density at the time of invasion 90 , 91 ., Theoretical research has also explored the divergence threshold for successful hybridization and introgression 53 , 92 ., Crop expansions are ideal systems to test such predictions because , as ancient agriculturalists moved crops away from their centers of origin , these domesticates came into sympatry with relatives spanning a range of divergence times ., For example , parviglumis , the progenitor of maize , has a divergence time from mexicana estimated at 60 , 000 years , from other members of the genus on the order of 100 , 000–300 , 000 years , and from the outgroup Tripsacum dactyloides of approximately 1 million years 39 ., While parviglumis is currently physically isolated from these taxa and likely was at the time of domestication 38 , maize has subsequently come into sympatry with virtually all of its close relatives , providing extensive opportunities for hybridization ., These newly-formed hybrid zones can be seen as tes

Introduction, Results, Discussion, Materials and Methods

The evolutionary significance of hybridization and subsequent introgression has long been appreciated , but evaluation of the genome-wide effects of these phenomena has only recently become possible ., Crop-wild study systems represent ideal opportunities to examine evolution through hybridization ., For example , maize and the conspecific wild teosinte Zea mays ssp ., mexicana ( hereafter , mexicana ) are known to hybridize in the fields of highland Mexico ., Despite widespread evidence of gene flow , maize and mexicana maintain distinct morphologies and have done so in sympatry for thousands of years ., Neither the genomic extent nor the evolutionary importance of introgression between these taxa is understood ., In this study we assessed patterns of genome-wide introgression based on 39 , 029 single nucleotide polymorphisms genotyped in 189 individuals from nine sympatric maize-mexicana populations and reference allopatric populations ., While portions of the maize and mexicana genomes appeared resistant to introgression ( notably near known cross-incompatibility and domestication loci ) , we detected widespread evidence for introgression in both directions of gene flow ., Through further characterization of these genomic regions and preliminary growth chamber experiments , we found evidence suggestive of the incorporation of adaptive mexicana alleles into maize during its expansion to the highlands of central Mexico ., In contrast , very little evidence was found for adaptive introgression from maize to mexicana ., The methods we have applied here can be replicated widely , and such analyses have the potential to greatly inform our understanding of evolution through introgressive hybridization ., Crop species , due to their exceptional genomic resources and frequent histories of spread into sympatry with relatives , should be particularly influential in these studies .

Hybridization and introgression have been shown to play a critical role in the evolution of species ., These processes can generate the diversity necessary for novel adaptations and continued diversification of taxa ., Previous research has suggested that not all regions of a genome are equally permeable to introgression ., We have conducted one of the first genome-wide assessments of patterns of reciprocal introgression in plant populations ., We found evidence that suggests domesticated maize received adaptation to highland conditions from a wild relative , teosinte , during its spread to the high elevations of central Mexico ., Gene flow appeared asymmetric , favoring teosinte introgression into maize , and was widespread across populations at putatively adaptive loci ., In contrast , genomic regions near known domestication and cross-incompatibility loci appeared particularly resistant to introgression in both directions of gene flow ., Crop-wild study systems should play an important role in future studies of introgression due to their well-developed genomic resources and histories of reciprocal gene flow during crop expansion .

genome evolution, ecology, evolutionary biology, genetics, plant genetics, population genetics, biology, genomics, gene flow, plant ecology, computational biology, evolutionary genetics, crop genetics

journal.pgen.1004746

COE Loss-of-Function Analysis Reveals a Genetic Program Underlying Maintenance and Regeneration of the Nervous System in Planarians

The Collier/Olfactory-1/Early B-cell factor ( COE ) family of transcription factors is necessary for animal development ., COE proteins possess an atypical HLH domain and a unique zinc finger DNA binding domain conserved across metazoans 1 ., Invertebrates encode a single homolog of COE , with roles in mesoderm and ectoderm development 2 , 3 , whereas vertebrates have four COE paralogs with functions in diverse cell types including B-cells and adipocytes 4 ., In the central nervous system ( CNS ) , COE regulates neuronal differentiation , migration , axon guidance , and dendritogenesis during development 2 , 3 , 5–13 and maintains neuronal identity throughout adulthood 14 , 15 ., COE proteins have also been proposed to function as tumor suppressors 16 and are associated with cancers such as acute lymphoblastic leukemia and glioblastoma 17–20 ., However , the specific genetic programs regulated by these genes in adult stem cells and mature neurons remain poorly understood ., Stem cells can be studied to determine how transcriptional regulators orchestrate developmental processes or cause disease 21 ., An excellent animal model to investigate stem cell regulation in vivo is the freshwater planarian Schmidtea mediterranea 22 ., S . mediterranea has the ability to regenerate all tissue types from a population of adult stem cells ( called neoblasts ) ., These cells constitute approximately 10–20% of all the cells in the animal and include pluripotent 23 and lineage-committed neoblasts 24–29 ., The planarian CNS is composed of two cephalic ganglia and a pair of ventral nerve cords that run along the length of the animal , which are comprised of molecularly diverse neuronal subtypes that are regenerated within days after injury or amputation 30–32 ., Functional analysis of transcription factors in planarians using RNA interference ( RNAi ) has begun to identify regulatory molecules required for the generation and maintenance of specific neuronal subpopulations in the CNS such as serotonergic and cholinergic neurons 24–27 , 33–35 ., Thus , planarians are outstanding organisms to study basic mechanisms that underlie stem cell-based maintenance and regeneration of the adult CNS ., A previous functional screen for transcription factors encoding a helix-loop-helix domain identified a planarian coe homolog that is expressed in a small population of neural-committed stem cells ( approximately 4–7% of the neoblast pool ) and in neurons 24 ., We showed that animals fed dsRNA designed to silence coe expression ( coe ( RNAi ) animals ) regenerated abnormal brains; furthermore , uninjured coe ( RNAi ) planarians displayed behavioral defects and reduced expression of neural subtype-specific genes 24 ., In this study , we sought to identify genes regulated by coe with roles in CNS renewal by comparing the transcriptome profiles of uninjured control and coe ( RNAi ) animals , uncovering differentially expressed genes with predicted roles in CNS function ., We validated a subset of these genes by testing for loss of expression after coe knockdown and visualizing their expression in coe+ cells ., These analyses revealed a set of nine candidate targets of coe in adult neurons , many of which are important for neuronal subtype identity ( e . g . , ion channels , neuropeptides , and neurotransmitters ) ., In addition , our findings demonstrate that coe functions to drive gene expression in multiple neuronal classes , including excitatory and inhibitory neurons ., To gain insights into the roles candidate COE targets play in CNS turnover and repair , we analyzed the function of downregulated transcripts using RNAi ., Our functional screen identified several genes required for CNS regeneration , including homologs of a voltage-gated sodium channel α-subunit ( scna-2 ) and the transcription factor pou4l-1 ., Our results suggest that COE is required for the expression of neural-specific genes in differentiating and mature neurons , a function that is essential to maintain CNS architecture and regulate neuronal regeneration ., Using an optimized whole-mount in situ hybridization protocol ( WISH ) ( see Materials and Methods ) , we found that coe mRNA was primarily restricted to neurons in S . mediterranea ( Fig . 1A ) ., In agreement with our previous findings 24 , we also observed coe transcripts in a subset of cycling stem cells ( h2b+ ) ( Fig . 1B–C ) ., We previously reported that coe ( RNAi ) animals regenerate cephalic ganglia that fail to connect at the anterior commissure and have significantly smaller brains with fewer cpp-1+ , npp-4+ , and npy-2+ neurons when compared to the controls 24 ., This defect is not restricted to the anterior portion of the animal ., Additional experiments showed coe ( RNAi ) animals do not properly regenerate their ventral nerve cords ( Fig . S1A–B ) ., Moreover , analysis of the brain patterning defect using anti-VC-1 , a marker of the photoreceptor neurons and their axons , revealed that the optic chiasm failed to connect at the midline in coe ( RNAi ) animals ( Fig . S1C ) ., These data demonstrate that coe is essential for neuronal regeneration at both anterior and posterior facing wounds and that coe regulates genes required for reestablishing midline patterning following brain amputation ., In addition , we previously noted that silencing of coe in intact uninjured animals results in a reduction of ChAT+ and pc2+ neurons near the anterior commissure and a loss of cpp-1+ neurons ., Following the 6th feeding of coe dsRNA , 100% of the animals exhibited impaired negative phototaxis 24 ., To investigate the specificity of the coe knockdown phenotype on the CNS , we examined the effect of coe RNAi on the intestine and muscle as representative endodermal or mesodermal tissues , respectively ., We hybridized uninjured control and coe ( RNAi ) animals with riboprobes specific to ChAT ( as a positive control ) , mat 36 , and collagen 37 ., As expected , we observed a decrease in ChAT+ neurons in the head 24 and noted a decrease in ChAT expression throughout the animal ( Fig . 2A ) ; by contrast , we did not observe a change in the spatial distribution of mat or collagen following coe knockdown ( Fig . 2B–C ) ., To quantify the effect of coe RNAi treatments on the expression of ChAT , mat and collagen , we measured relative mRNA levels by reverse transcription quantitative PCR ( RT-qPCR ) ., First , we confirmed coe knockdown led to a significant decrease in the relative expression of coe mRNA ( down 60%±16% compared to the controls; Fig . 2D ) ., Measurement of ChAT , mat and collagen from coe ( RNAi ) planarians revealed that ChAT mRNA levels were significantly down ( 45%±15% ) compared to control animals; in contrast to ChAT , the relative mRNA levels of mat or collagen were not affected by coe RNAi treatment ( Fig . 2D ) ., Combined with our previous work 24 , these results strongly suggest that coe knockdown specifically affects gene transcription in the nervous system and does not cause obvious defects in other tissues such as the intestine or muscle ., Furthermore , our results are consistent with reports demonstrating that COE is required to maintain cholinergic and peptidergic neuronal subtype-specific gene expression in Caenorhabditis elegans and Drosophila melanogaster 14 , 15 ., To investigate if the inhibition of coe perturbs nervous system architecture downstream of gene expression changes , we labeled neuronal cell bodies and their projections using anti-CRMP-2 , which labels a subset of neuronal cell bodies and their axon projections , and anti-β-tubulin to visualize nerve projections ( Fig . 3A–C ) ., In coe ( RNAi ) animals , we observed a striking decrease in axon projections labeled by anti-CRMP-2 and anti-β-tubulin compared to the controls; however , expression of CRMP-2 was retained in the cell bodies ( Fig . 3C ) ., In addition , when we labeled sensory neurons using cintillo 38 , coe ( RNAi ) animals exhibited significantly fewer cintillo+ cells ( Fig . 3D ) ., Our results strongly suggest that nervous system architecture is severely reduced or lost in the absence of coe ., These structural defects likely underlie the behavioral abnormalities observed in coe-deficient planarians ., Although COE has been shown to drive differentiation of several classes of neurons during development 39 , the transcriptional programs controlled by this transcription factor in adult nervous system function are poorly defined ., We reasoned that the CNS-specific coe RNAi phenotype in intact planarians represents an excellent opportunity to identify gene expression programs controlled by COE in the post-embryonic nervous system ., Thus , we used comparative mRNA sequencing ( RNA-seq; see Materials and Methods ) to sequence mRNAs isolated from uninjured controls and coe ( RNAi ) animals one week after the 6th RNAi treatment , which was the point in time we consistently observed behavioral defects and loss of neural-specific gene expression in 100% of coe-deficient animals and did not detect overt defects in other tissues ( Fig . 2 ) ., RNA-seq analysis identified 909 differentially expressed genes; 397 were downregulated , and 512 were upregulated ( Table S1 ) ., Functional annotation using DAVID software showed that the set of downregulated genes was significantly enriched for Gene Ontology ( GO ) terms associated with “ion channel , ” “neuronal activities , ” “nerve-nerve synaptic transmission , ” “voltage-gated ion channel , ” and “cell adhesion molecule”; by contrast , the upregulated genes were enriched for GO terms associated with “cytoskeletal protein” and “muscle development” ( Table 1 ) ., coe mRNAs were not detected in a muscle pattern ( Fig . 1 ) , nor did we detect overt phenotypes associated with muscle differentiation ( Fig . 2 ) ., However , the RNA-seq data raised the possibility that coe might negatively regulate mesoderm specification , which is required for muscle development 3 , 40 ., It is possible upregulation of muscle genes is an indirect consequence of a loss of nervous system influence such as cholinergic transmission and/or neuropeptide regulation ., Previous studies have demonstrated cholinergic neurotransmission is required for coordinated muscle contractions in planarians 41–43 ., Thus , we speculate that loss of nervous system modulation disrupts muscle homeostasis and leads to changes in expression of muscle-related genes ., Although our experiments do not definitively assign the role of COE in muscle differentiation or maintenance , our data do clearly indicate that coe is required for expression of nervous system-specific genes in adult planarians ., Based on the annotation of differentially expressed genes , we hypothesized that genes predicted to play roles in nervous system functions in the downregulated category likely include direct COE targets ., To test our hypothesis and validate genes found in our RNA-seq dataset , we selected 65 genes that were dramatically downregulated , associated with neural functions , or annotated as transcription factor homologs ., First , we performed WISH to determine the tissue-specific pattern of expression of all 65 genes ( representative examples are shown in Fig . 4 ) ., As we expected , the most prominent mRNA expression pattern was in the nervous system ( 26 of 65 genes; see Table S2 ) , similar to ChAT and cpp-1 , which we had previously found to be putative downstream targets of COE 24 ., In addition , we observed genes that were expressed broadly in the nervous system ( such as neural cell adhesion molecule-2 ( ncam-2 ) , vesicle-associated membrane protein like-1 ( vamp ) , gamma-aminobutyric acid receptor subunit beta like-1 ( gbrb-1 ) , and voltage-gated sodium channel alpha-1 ( scna-1 ) ) or in discrete neuronal subpopulations ( such as secreted peptide prohormone-19 , -18 , -2 ( spp-19 , -18 , -2 ) , neuropeptide like ( npl ) , voltage-gated sodium channel alpha-2 ( scna-2 ) , and caveolin-1 ( cav-1 ) ) ( Fig . 4A–J ) ., Our list also included transcripts that labeled subsets of neurons in the brain ( such as netrin-1 ) ( Fig . 4K ) 44 ., In addition , we found that the transcription factors iroquios-1 ( irx-1 ) and pou class 4 transcription factor 4 like-1 ( pou4l-1 ) were expressed at or near the cephalic ganglia ( Fig . 4L–M ) , and their mRNA was detected in ChAT+ neurons by fluorescent in situ hybridization ( FISH ) ( Fig . S2 ) ., Next , we tested the effect of coe RNAi on the expression of 33 genes that could be visualized in discrete cell populations by WISH ., Knockdown of coe led to a marked reduction in the expression of 31 genes ( Table S2; representative results are shown in Fig . 4A′–H′ , K′–M′ ) ; for two genes , scna-2 and cav-1 , we observed a loss of expression at the midline ( Fig . 4I′–J′ ) ., Furthermore , we quantified the number of cells labeled by spp-19 , spp-18 , and npl probes ., As expected , we found there was a significant reduction in the number of spp-19+ , spp-18+ , and npl+ cells following coe RNAi ( Fig . 4N ) ., As an additional test to validate the in situ hybridization results , we measured the relative expression levels of downregulated genes in control and coe RNAi-treated planarians using RT-qPCR ( Fig . S3A ) ., All of the genes we tested showed a decrease in relative expression following coe RNAi ( 9 of 14 genes were significantly downregulated; P<0 . 05 , Students t-test ) ., By contrast , when we measured the relative expression of CNS-expressed genes that were not on our list of differentially expressed genes , none were significantly reduced ( 11 of 11 genes; Fig . S3B–C ) ., Although some of the control genes we selected were reduced near levels comparable to some genes downregulated following coe RNAi ( e . g . , ncam2 , vamp , and gbrb1; Fig . S3A ) , we noted that isotig13897 and npp-2 30 , which are transcripts detected in subsets of neurons or throughout the CNS , respectively , remained unchanged ( Fig . S3B–C ) ., It is possible that some changes in gene expression associated with coe RNAi are consequence of a reduction in nervous system tissue ., We proceeded to perform double-FISH to coe and validated genes to determine if any were potential genetic targets of COE ., Of the 17 genes we were able to reliably detect by FISH ( 33 genes were tested; see Table S2 ) , 11 were expressed in coe+ cells ( representative results are shown in Fig . 5 and Fig . S4 ) , including ChAT and cpp-1 24 ., Together , these results identified nine novel candidate targets of COE in the nervous system , including genes important for maintaining neuronal subtype identity such as ion channels , ion channel receptors , and neuropeptide genes ( Table 2 ) ., In addition , our data suggest that COE is essential to maintain genetic programs in multiple classes of adult neuronal subtypes including excitatory ( cholinergic ) and inhibitory ( GABAergic ) neurons ., Our RNA-seq dataset revealed that coe is essential to maintain the expression of hundreds of genes in the adult animal ., This change in the neuronal gene expression landscape led to abnormal CNS structure and behavior ., To identify genes downstream of coe that contribute to CNS differentiation , we took advantage of the experimental ease in examination of gene function in planarian regeneration and analyzed the role of 11 downregulated genes that were expressed in neurons or predicted to encode transcription factors ( Table 3 ) ., Following RNAi , animals were amputated pre- and post-pharyngeally and allowed to regenerate for 10 days ., We found that 6 out of 11 genes resulted in defective brain regeneration ( see Table 3 ) ; scna-2 , pou4l-1 , and nkx2l caused the strongest phenotypes ., Compared to the controls , scna-2 ( RNAi ) animals had less eye pigmentation or developed a single eyespot; nkx2l ( RNAi ) animals exhibited photoreceptor defects; and pou4l-1 ( RNAi ) animals had less photoreceptor pigment ( Fig . 6A–D ) ., To examine CNS architecture , we stained scna-2 , nkx2l , and pou4l-1 RNAi treated planarians with anti-SYNAPSIN and the coe-regulated genes ChAT and npl ., Although subtle , all three showed abnormalities in brain morphology ( Fig . 6A–D ) ., However , when we measured the area of the brain stained by anti-SYNAPSIN , only scna-2 and pou4l-1 RNAi animals had a significant reduction in neuropil density ( Fig . 6E ) ., Consistent with this observation , the ChAT+ brain areas were smaller in scna-2 ( RNAi ) and pou4l-1 ( RNAi ) animals ( Fig . 6F ) but not in nkx2l ( RNAi ) animals ., The smaller brain phenotype was accompanied by fewer npl+ neurons in scna-2 ( RNAi ) animals; however , despite their smaller brains , pou4l-1 ( RNAi ) animals regenerated significantly more npl+ cells than controls ( Fig . 6G ) ., These findings demonstrate that scna-2 is required for CNS regeneration and highlight the importance of ion channels in neurogenesis regulation during CNS development , maintenance , and repair 45–47 ., Interestingly , these data suggest that pou4l-1 plays a role in the specification of certain neuronal lineages ., It is possible that in the absence of pou4l-1 , planarians regenerate the incorrect proportion of neuronal subtypes and have disorganized brains , but this possibility will require further analysis with additional neuronal subtype-specific markers ., By contrast , our results suggest nkx2l is not required for CNS regeneration per se ., Following coe RNAi , nkx2l expression was reduced by in situ hybridization and RT-qPCR ( Table S2 and Fig . S3A ) , but nkx2l , which is primarily expressed in stem cells and in progeny 48 , was not detected in the nervous system ( Fig . S5A ) ., We hypothesize nkx2l functions in early regeneration to establish patterning , which is consistent with the observation that nkx2l ( RNAi ) planarians fail to regenerate properly patterned head ( Fig . 6C ) and tail tissues ( Fig . S5B ) ., It is noteworthy that several transcription factors that we identified in our screen are putative COE targets in Xenopus development , including irx-1 , tal , pou4l-1 , and nkx2l 39 ., Of these genes , we found that expression of pou4l-1 was important for CNS regeneration and nkx2l was involved in patterning ., NKX and POU orthologs play critical roles during CNS development of invertebrate and vertebrate organisms 49–51 ., These data suggest that regulatory genes downstream of COE are conserved and have roles in CNS regeneration ., However , it will be important to experimentally resolve whether these transcription factors are bona fide targets of COE in planarians or other animals such as Xenopus ., COE proteins are known to function as terminal selectors of neuronal identity in adult organisms 14 , 15 , 52 , yet the neuronal subtypes and specific genetic programs regulated by COE in the adult CNS are not well understood ., In this study , we exploited the high rate of tissue turnover and regenerative capacity of planarians to expand our understanding of how COE may function in the post-embryonic nervous system ., We combined RNAi with RNA-seq analysis and identified a set of differentially expressed genes associated with nervous system biological roles ., Expression analysis of a subset of these genes revealed novel candidate targets of coe in planarian neurons ( Fig . 7A ) , some of which underscored coes essential role in maintaining expression of genes vital for neuronal subtype identity and function ( such as neurotransmitter receptors , ion channels , and neuropeptide encoding genes ) ( Fig . 7A–B ) ., Decoding which transcriptional changes are direct or indirect consequences of coe loss in the planarian model will be vital to further elucidate how mutations in COE proteins cause or contribute to disease pathologies in the CNS ., The next step will be to find direct COE binding sites genome-wide using in silico and chromatin immunoprecipitation ( ChIP ) approaches and combining these findings with our differential expression data ., In addition , molecular profiling of coe+ cell populations ( such as stem cells , postmitotic progeny , and neurons ) will be essential to determine how coe function alters in cell type-specific contexts ., In conclusion , our study demonstrates the importance of COE family proteins in neuronal turnover and repair of the adult CNS and broadens our understanding of the regulatory programs governed by these factors ., Asexual Schmidtea mediterranea ( CIW4 ) were reared in 1× Instant Ocean Salts ( 0 . 83 mM MgSO4 , 0 . 9 mM CaCl2 , 0 . 04 mM KHCO3 , 0 . 9 mM NaHCO3 , and 0 . 21 g/L Instant Ocean Aquarium Salt diluted in ultra-pure water ) at 20°C ., Animals were starved for one week , and those ranging between 2–5 mm in length were used for experimentation ., Animals were administered six feedings of bacterially expressed dsRNA complementary to the indicated gene over three weeks as previously described 53; gfp dsRNA was fed as a control ., Unless otherwise indicated , all intact RNAi animals were fixed seven days following the 6th dsRNA treatment ., For regeneration experiments , planarians were amputated pre- and post-pharyngeally 24 hours following the 6th dsRNA feeding ., Animals were processed for colorimetric whole-mount in situ hybridization using the protocol described in 54 ., Fluorescent in situ hybridization experiments were performed as described in 24 , 54 and developed using Tyramide Signal Amplification ( TSA ) as described in 55 ., Briefly , animals were incubated for 5 min . in borate buffer ( 100 mM borate pH 8 . 5 , 0 . 1% Tween-20 ) and then developed in TSA Reaction Buffer ( borate buffer , 2% dextran sulfate , 0 . 1% Tween-20 , 0 . 003% H2O2 ) , containing fluor-tyramide and 4-iodophenylboronic acid for 30 min ., For double-FISH , animals were quenched in 1% H2O2 for 1 hour ., For γ-irradiation experiments , animals were fixed 6 days following a 100 Gy treatment , a time point when both stem cells and postmitotic progenitors are ablated ., Accession numbers for the sequences used in this study are listed in Table S3 ., For immunostaining with anti-SYNORF1 ( 1∶400 , 3C11 , DSHB ) or anti-VC-1 ( 1∶10 , 000; kindly provided by Hidefumi Orii ) , animals were fixed with Carnoys solution 56 ., For anti-CRMP-2 ( 1∶50 , 9393S , Cell Signaling ) or anti-β-TUBULIN ( 1∶1000; E7 , DSHB ) labeling , animals were fixed with formaldehyde , processed without a reduction step , and labeled using TSA 54 ., One week after the final dsRNA treatment , RNA was extracted from three independent control and coe ( RNAi ) animal groups using Trizol ( Life Technologies ) ., RNA samples were treated with DNase using the Turbo DNA-free Kit ( Life Technologies ) and purified using the RNeasy MinElute Cleanup kit ( Qiagen ) ., Sequencing libraries were synthesized using the TruSeq RNA Sample Prep Kit v2 and sequenced on a HiSeq 2000 System ( Illumina ) ., More than 12 million 100-bp single-end reads were generated for each sample ., Sequenced reads were submitted to the Sequence Read Archive ( NCBI ) under the accession number PRJNA235907 ., Reads were mapped to the planarian genome using TopHat 57; gene models were predicted using a published transcriptome 58 , 59 ., Differentially expressed genes were identified using the R Bioconductor package edgeR 60 with cutoffs of logCPM score ≥0 and FDR≤0 . 05 ., Changes in gene expression detected by RNA-seq were represented as linear fold changes over controls ., For the differentially expressed Schmidtea mediterranea transcripts , we performed BLASTX against the human UniProt database ( cutoff<1×10−4 ) ; human accession numbers were then used to assign Gene Ontology terms and perform clustering analysis using DAVID software 61 , 62 with the “Panther_BP_all” and “Panther_MF_all” gene annotation settings and an Enrichment Score cutoff >1 . 3 ., For validation studies , transcript sequences were analyzed by BLASTX against protein sequences from human , mouse , fly , and nematode and identified as the top BLAST hit ( Table S3 ) ., Sequences were obtained from a cDNA collection 63 or cloned into pJC53 . 2 30 or pPR244 64 using gene specific primers ., GenBank accession numbers and the primers used in this study are listed in Table S3 ., Total RNA was extracted and purified as described above ., cDNA was synthesized using the iScript cDNA Synthesis Kit ( BioRad ) ., Reverse transcription quantitative PCR was performed on a Bio-Rad CFX Connect Real-Time System using SsoAdvanced SYBR Green Supermix ( Bio-Rad ) with a two-step cycling protocol and annealing/extension temperature of 58 . 5°C ., At least three biological replicates and two technical replicates were performed for each experiment ., The relative amount of each cDNA target was normalized to Smed-β-tubulin ( accession no . DN305397 ) ., The normalized relative changes in gene expression , standard deviations , and t-tests were calculated in Bio-Rad CFX Manager Software v3 . 0 ., Primers are listed in Table S3 ., Images of live animals and whole mount in situ hybridization samples were acquired using a Leica DFC450 camera mounted on a Leica M205 stereomicroscope ., Fluorescent images were acquired with a Zeiss Axio Observer . Z1 equipped with an Axiocam MRm camera and ApoTome; images are displayed as maximum image projections from ten 1-µm optical sections ., For all experiments , we counted cells by hand using ImageJ Software 65 , and biological replicates ( n≥3 ) were averaged and shown as mean ± standard deviation ., The number of cintillo+ , spp-19+ , spp-18+ , and npl+ cells ( Fig . 4N ) was normalized to animal length ( mm ) ., We used anti-SYNAPSIN staining and ChAT expression to determine brain area ( Fig . 6E–F ) , normalized to animal length ( µm ) ., To quantify npl+ brain-specific neurons following amputation , npl+ cells were counted in the cephalic ganglia and normalized to the average total brain area ( Fig . 6G ) ., When comparing two groups , we used a Students t-test and significance was accepted at P<0 . 05 .

Introduction, Results/Discussion, Materials and Methods

Members of the COE family of transcription factors are required for central nervous system ( CNS ) development ., However , the function of COE in the post-embryonic CNS remains largely unknown ., An excellent model for investigating gene function in the adult CNS is the freshwater planarian ., This animal is capable of regenerating neurons from an adult pluripotent stem cell population and regaining normal function ., We previously showed that planarian coe is expressed in differentiating and mature neurons and that its function is required for proper CNS regeneration ., Here , we show that coe is essential to maintain nervous system architecture and patterning in intact ( uninjured ) planarians ., We took advantage of the robust phenotype in intact animals to investigate the genetic programs coe regulates in the CNS ., We compared the transcriptional profiles of control and coe RNAi planarians using RNA sequencing and identified approximately 900 differentially expressed genes in coe knockdown animals , including 397 downregulated genes that were enriched for nervous system functional annotations ., Next , we validated a subset of the downregulated transcripts by analyzing their expression in coe-deficient planarians and testing if the mRNAs could be detected in coe+ cells ., These experiments revealed novel candidate targets of coe in the CNS such as ion channel , neuropeptide , and neurotransmitter genes ., Finally , to determine if loss of any of the validated transcripts underscores the coe knockdown phenotype , we knocked down their expression by RNAi and uncovered a set of coe-regulated genes implicated in CNS regeneration and patterning , including orthologs of sodium channel alpha-subunit and pou4 ., Our study broadens the knowledge of gene expression programs regulated by COE that are required for maintenance of neural subtypes and nervous system architecture in adult animals .

COE transcription factors are conserved across widely divergent animals and are crucial for organismal development ., COE genes also play roles in adult animals and have been implicated in central nervous system ( CNS ) diseases; however , the function of COE in the post-embryonic CNS remains poorly understood ., Planarian regeneration provides an excellent model to study the function of transcription factors in cell differentiation and in terminally differentiated cells ., In planarians , coe is expressed in differentiating and mature neurons , and its function is required for CNS regeneration ., In this study , we show that coe is required to maintain structure and function of the CNS in uninjured planarians ., We took advantage of this phenotype to identify genes regulated by coe by comparing global gene expression changes between control and coe mRNA-deficient planarians ., This approach revealed downregulated genes downstream of coe with biological roles in CNS function ., Expression analysis of downregulated genes uncovered previously unknown candidate targets of coe in the CNS ., Furthermore , functional analysis of downstream targets identified coe-regulated genes required for CNS regeneration ., These results demonstrate that the roles of COE in stem cell specification and neuronal function are active and indispensable during CNS renewal in adult animals .

developmental biology, neurobiology of disease and regeneration, medicine and health sciences, stem cells, animal cells, cell biology, regeneration, neurology, biology and life sciences, cellular types, morphogenesis, neuroscience, adult stem cells

journal.pntd.0004848

Lipophosphoglycans from Leishmania amazonensis Strains Display Immunomodulatory Properties via TLR4 and Do Not Affect Sand Fly Infection

The major cell surface glycoconjugate of Leishmania is the lipophosphoglycan ( LPG ) , implicated in a wide range of functions , both in vertebrate and invertebrate hosts 7 ., In the invertebrate host , LPG variations are important for Leishmania specificity to the sand fly 8 , where attachment of the parasite to a midgut receptor is a crucial event 9 ., In the vertebrate host , the main functions of this virulence factor during the earlier steps of infection include: protect the parasite from complement-mediated lysis , attachment and entry into macrophages 10 , able to inhibit phagolysosomal fusion 11 , modulation of nitric oxide ( NO ) production 12 and inhibition of protein kinase C ( PKC ) 13 ., Interestingly , although L . major LPG mutants ( lpg1- ) were highly susceptible to complement mediated lysis , they were able to invade macrophages reinforcing the role of other molecules and the host defenses during the interaction 11 ., Many functions have been attributed to L . amazonensis LPG including induction of neutrophil extracellular traps ( NETs ) 14 , induction of protein kinase R ( PKR ) 15 , triggering and killing of the parasite via Leukotriene B4 ( LTB4 ) 16 ., Although L . amazonensis LPG is important in many steps of host infection , its role during the interaction with macrophages and sand flies remains unknown ., LPG structures have been described for several dermotropic and viscerotropic Leishmania 17–26 ., LPGs have a conserved glycan core region of Gal ( α1 , 6 ) Gal ( α1 , 3 ) Galf ( β1 , 3 ) Glc ( α1 ) -PO4Man ( α1 , 3 ) Man ( α1 , 4 ) -GlcN ( α1 ) linked to a 1-O-alkyl-2-lyso-phosphatidylinositol anchor ., The salient feature of LPG is another conserved domain consisting of the Gal ( β1 , 4 ) Man ( α1 ) -PO4 backbone of repeat units ( n = ~15–30 ) ., The distinguishing feature of LPGs that is responsible for the polymorphisms among Leishmania spp ., is variable sugar composition and sequence of branching sugars attached to the repeat units and cap structure 27 ., For example , the LPG of Leishmania major ( Friedlin ) has β-1 , 3 galactosyl side-chains , often terminated with arabinose , whereas the LPGs of Leishmania donovani ( Mongi ) and L . infantum ( PP75 and BH46 strains ) possess β-glucoses in their repeat units 17 , 20 , 24 ., However , there is no available information on the degree of variability in the LPG structure for L . amazonensis ., The L . major LPG was identified as potent agonist of Toll-like receptor 2 ( TLR2 ) in human natural killer ( NK ) cells and murine macrophages , triggering the production of TNF-α and IFN-γ through MyD88 28 , 29 ., Recently , the LPGs of two New World species ( L . infantum and Leishmania braziliensis ) differentially activated TLR2 ., In this case , L . braziliensis LPG was more pro-inflammatory being able to induce the translocation of NF-κB to the nucleus 30 ., As a part of a wider project on the glycobiology of New World species of Leishmania , we evaluated the role of L . amazonensis LPGs ( PH8 and Josefa strains ) during the interaction with host cells and the sand fly L . migonei ., The present study might help to improve our understanding on the immune modulation mediated by glycoconjugates of L . amazonensis , the etiological agent of diffuse cutaneous leishmaniasis ( DCL ) ., The animals were kept in the Animal Facility of the Centro de Pesquisas René Rachou/FIOCRUZ ., All animals were handled in strict accordance with animal practice as defined by Internal Ethics Committee in Animal Experimentation ( CEUA ) of Fundação Oswaldo Cruz ( FIOCRUZ ) , Belo Horizonte , Minas Gerais ( MG ) , Brazil ( Protocol P-82/11-4 ) ., This protocol followed the guidelines of CONCEA/MCT , the maximum ethics committee of Brazil ., Knockout mice handling protocol was approved by the National Commission of Biosafety ( CTNBio ) ( protocol #01200 . 006193/2001-16 ) ., World Health Organization Reference strains of L . amazonensis ( IFLA/BR/1967/PH8 and MHOM/BR/75/Josefa ) were used ., The PH8 strain was originally isolated from the sand fly L . flaviscutellata from Pará State , Brazil , and the Josefa strain was isolated from a human case from Bahia State , Brazil ., Promastigotes were cultured in M199 medium supplemented with 10% fetal bovine serum ( FBS ) , penicillin 100 units/mL , streptomycin 50 μg/mL , 12 . 5 mM glutamine , 0 . 1 M adenine , 0 . 0005% hemin , and 40 mM Hepes , pH 7 . 4 at 26°C until late log phase 21 ., Parasites were seeded in triplicate ( 1 x 105 cells/mL ) , and growth curves of PH8 and Josefa strains were determined daily using a Neubauer improved haemocytometer until cells reached a stationary phase ., Both strains exhibited a similar division profile reaching stationary phase after 7 days of culture ., For this reason the 6th day was chosen for harvesting parasites for LPG extraction and molecular typing ( S1A Fig ) ., For molecular typing , genomic DNA was extracted from log-phase Leishmania using the phenol/chloroform method ( 1:1 ) for amplification of the HSP70 fragment prior to digestion with HaeIII as previously described 31 ., Positive controls included DNA from L . braziliensis ( MHOM/BR/75/M2903 ) , L . infantum ( MHOM/BR/74/PP75 ) , Leishmania guyanensis ( MHOM/BR/75/M4147 ) and L . amazonensis ( IFLA/BR/67/PH8 ) ., After PCR-RFLP both L . amazonensis strains were confirmed ( S1B Fig ) ., For optimal LPG extraction , late log phase cells were harvested and washed twice with PBS prior to extraction of LPGs ( Fig 1 ) ., The LPG extraction was performed as described elsewhere with solvent E ( H2O/ethanol/diethylether/pyridine/NH4OH; 15:15:5:1:0 . 017 ) after a sequential organic solvent extraction 32 ., For purification , the solvent E extract was dried under N2 evaporation , resuspended in 2 mL of 0 . 1 M acetic acid/0 . 1 M NaCl , and applied onto a column with 2 mL of phenyl-Sepharose , equilibrated in the same buffer ., The column was washed with 6 mL of 0 . 1 M acetic acid/0 . 1 M NaCl , then 1 mL of 0 . 1 M acetic acid and finally 1 mL of endotoxin free water ., The LPGs were eluted with 4 mL of solvent E then dried under N2 evaporation ., LPG concentrations were determined as described elsewhere 33 ., Prior to use on in vitro cells cultures , LPGs were diluted in RPMI ., All solutions were prepared in sterile , LPS-free distilled water ( Sanobiol , Campinas , Brazil ) ., All extractions and purifications procedures are depicted in Fig 1 ., Purified LPGs ( 5 μg ) were subjected to dot-blot , blocked ( 1 h ) in 5% milk in PBS and probed for 1 h with monoclonal antibody ( mAb ) CA7AE ( 1:1000 ) , that recognizes the unsubstituted Gal ( β1 , 4 ) Man repeat units 34; mAb LT22 ( 1:1000 ) that recognizes β-glucose side chains and WIC 79 . 3 ( 1:1000 ) that recognizes β-galactose side chains 21 , 35 ., After three washes in PBS ( 5 min ) , the membrane was incubated for 1 h with anti-mouse IgG conjugated with peroxidase ( 1:5 , 000 ) and the reaction was visualized using luminol ., Thioglycollate-elicited macrophages were extracted from C57BL/6 and C57BL/6 knockouts TLR2 ( -/- ) and TLR4 ( -/- ) by peritoneal washing with ice cold RPMI and enriched by plastic adherence ( 1 h , 37°C , 5% CO2 ) ., Cells ( 3 x 105 cells/well ) were washed with fresh RPMI then culture in RPMI , 2 mM glutamine , 50 U/mL of penicillin and 50 μg/mL streptomycin supplemented with 10% FBS in 96-well culture plates ( 37°C , 5% CO2 ) ., Cells were primed with interferon-gamma ( IFN-γ ) ( 3 IU/mL ) for 18 h prior to incubation with LPGs from both strains ( 10 μg/mL ) , live stationary Leishmania parasites ( MOI 10:1 ) and lipopolysaccharide ( LPS: 100 ng/mL ) 30 , 36 ., For CBA multiplex cytokine detection , cells were plated , primed as describe above and incubated with LPGs and live stationary promastigotes ( MOI 10:1 ) for 48 h ., LPS was added as a positive control and medium as negative control ., Supernatants were collected and IL-1β , IL-6 , IL-10 , IL-12p40 and TNF-α were determined using BD CBA Mouse Cytokine assay kits according to the manufacturer’s specifications ( BD Biosciences , CA , USA ) ., Flow cytometry measurements were performed on a FACSCalibur flow cytometry ( BD Bioscience , Mountain View , CA , USA ) ., Cell-QuestTM software package provided by the manufacturer was used for data acquisition and the FlowJo software 7 . 6 . 4 ( Tree Star Inc . , Ashland , OR , USA ) was used for data analysis ., A total 1 , 500 events were acquired for each preparation ., Results are representative of six experiments in duplicate ., Nitrite concentrations were determinate by Griess reaction ( Griess Reagent System , 2009 ) ., For MAPKs , peritoneal murine macrophages were obtained as described above ., They were applied on 24 wells tissue culture plates ( 106 cells/well ) for 18 h prior to assay ., The cells were washed with warm RPMI and incubated with LPG from both species for different times ( 5 , 15 , 30 , 45 and 60 min ) or with medium ( negative control ) or E . coli extracts ( 100 ng/mL , only 45 minutes ) as positive control ., p-p38 , p-JNK , p-IκBα and total p38 were assayed as previously described 25 ., p-IκBα antibody was provided by Dr . L . P . de Sousa ., NF-κB translocation using CHO reporter lines ( a kind gift by M . A . Campos ) was determined as described elsewhere 30 ., CHO reporter cells were plated ( 1 x 105 cells/well ) in 24-well tissue culture dishes and the LPG ( 0 . 02 and 0 . 2 μg/mL ) from both strains was added in a total volume of 0 . 25 mL medium/well ., The cells were examined by flow cytometry ( BD Biosciences , CA , USA ) and the analyses were performed using CellQuestTM software ., Lutzomyia migonei ( Baturite strain ) sand flies were kept under laboratory conditions and were fed on 30% sucrose solution for 3–4 days prior to experiments ., The insects were artificially fed using a chick skin membrane in a glass-feeder device ., The chick skin membrane was provided by the Animal Facility of Centro de Pesquisas René Rachou/FIOCRUZ under the Protocol LW 30/10 ., Heparinized mouse blood ( drawn intracardially from Balb/C ) , with penicillin ( 100 U/mL ) and streptomycin ( 100 μg/mL ) ( 37°C ) containing 2 x 107/mL logarithmic phase promastigotes ( PH8 and Josefa strains ) offered for 5 h under dark conditions 5 ., Blood engorged flies were separated and maintained at 26°C with 30% sucrose ., Engorged sand flies had their midguts dissected on days 2 and 4 post feeding ., The midguts were homogenized in 30 μl of PBS and the number of viable promastigotes determined by counting under a Neubauer improved haemocytometer 24 ., For nitrite , cytokine measurements and in vivo sand fly experiments , the Shapiro Wilk test was conducted to test the null hypothesis that data were sampled from a Gaussian distribution 37 ., For the non-parametric distribution , it was performed the Mann-Whitney test ., Data were analyzed using GraphPad Prism 5 . 0 software ( Graph Prism Inc . , San Diego , Ca ) ., P < 0 . 05 was considered significant ., The purified LPGs from L . amazonensis PH8 and Josefa strains were differentially recognized by the mAbs CA7AE and LT22 ( S2 Fig ) ., LPG from PH8 strain was recognized by CA7AE and LT22 as well as the positive control represented by L . infantum ( BH46 ) ., However , a different recognition profile was observed for the Josefa strain since its LPG was weakly recognized by LT22 but not by CA7AE , indicating the presence of side-chains branching-off the repeat units ., Because CA7AE recognizes Gal ( β1 , 4 ) Man unsubstituted repeat units in LPG 34 , these results indicate that at least some of the repeat units are indeed unsubstituted in the LPG of PH8 strain ., On the other hand , the presence of side-chains suggestive of glucoses , due to LT22 reactivity , was detected in the LPGs of PH8 and Josefa strains ., However , LT22 also recognized the galactose-branched repeat units of L . major ( strains FV1 and LV39 ) indicating cross-reactivity of the antibodies , thus suggesting the presence of either glucose or galactose as side chains ( S2 Fig ) ., These data suggested an intraspecific polymorphism in the LPGs of L . amazonensis strains ., We investigated whether LPGs purified from different strains could have an impact on the parasite’s interaction with host cells , the ability to elicit NO and cytokine production by murine macrophages ., LPGs from both strains were incubated with murine peritoneal macrophages from C57BL/6 and respective knockouts for TLR2 ( -/- ) and TLR4 ( -/- ) ., We did not detect any production of the cytokines IL-1β , IL-10 and IL-12 ( S3A–S3C Fig ) ., Both LPGs and respective parasites were able to activate through TLR4 , resulting in NO , TNF-α and IL-6 production ( Fig 2A–2C ) ( P < 0 . 05 ) ., As expected , LPS ( positive control ) activated TLR4 in the TLR2 ( -/- ) ( Fig 2A–2C ) ., No difference in MAPKs phosphorylation ( p38 and JNK ) and p-IκBα was observed after incubation with LPGs from both strains ., In peritoneal murine macrophages this activation was mainly via TLR4 ( Fig 3A and 3B ) ., We also evaluated if the LPGs from these strains were able to translocate NF-κB in CHO cells ., No activation of NF-κB was detected in those cells ( Fig 4 ) ., In vivo midgut infections of the sand flies were determined on days 2 and 4 post feeding , in order to evaluate the number of parasites after the blood meal digestion , as well as , after its excretion on day 3 , where non-attached parasites are lost ., Although a higher parasite density was detected for PH8 strain on day 2 ( P < 0 . 05 ) , no statistical differences in the parasite densities from both L . amazonensis strains were observed on day 4 , and both strains were able to colonize L . migonei midgut ( P > 0 . 05 , Fig 5 ) ., Leishmania amazonensis , etiologic agent of the cutaneous and anergic diffuse leishmaniasis , is characterized by disseminated non-ulcerative skin lesions and constantly proportion of negative delayed hypersensitivity skin-test ( DTH ) , resulting in a high resistance of this disease to any type of chemotherapy 1 , 38 , 39 ., In the Old and New World , parasite glycoconjugates have being implicated in a variety of events during parasite-host interactions 40 , 41 ., More recently , the role of LPG and GIPLs in the L . braziliensis and L . infantum was determined , suggesting that two distinct LPGs were able to differentially modulate macrophage functions 30 , 41 ., Regarding L . mexicana complex , from where L . amazonensis is a member , a recently study has demonstrated the inflammatory role of LPG 42 ., This glycoconjugate naturally exposed to the host immune system could contribute to the maintenance of infection by interfering with the assembly immune response , like modulation of cytokine production and non-activation of effectors cells ., In the present work , we investigated whether LPGs from two L . amazonensis strains would account for differences in the interaction with macrophages and L . migonei ., LPG polymorphisms are common in the composition of branching sugars attached to the conserved repeat units of its backbone ., While in the Old World species , a wide spectrum of sugar composition and structure is commonly observed , in New World species only glucose residues in the side chains of Leishmania were documented to date 17 , 21 , 23 , 24 , 43 ., Our preliminary characterization of the repeat units using specific antibodies suggested the existence of intraspecies polymorphism in L . amazonensis LPGs with differences in the side-chains and in the level of glycosylation ., The LPG of PH8 strain strongly reacted with CA7AE , that recognizes the basic backbone of the repeat units is Gal ( β ) Man-PO4 21 , 34 ., However , Josefa LPG did not reacted with this antibody , thus suggesting the existence of sugars as side-chains in the repeat units ., This feature is commonly found in the LPG of L . major reference strain FV1 , which does not react with CA7AE 17 ., In order to evaluate the quality of the sugars branching-off the repeat units , LT22 and WIC . 79 . 3 antibodies were used to detect the presence of glucose and galactose , respectively 21 , 35 ., Based on L . major LPGs used as controls , they were either recognized by those antibodies , suggesting cross-reactivity ., Moreover , those data reinforced the presence of either glucoses or galactoses as side-chains in L . amazonensis LPGs ., A fully detailed biochemical analysis must await the results of further investigations ., Understanding variations and the LPG structures are crucial for the comprehension of the mechanisms of how parasites survive under extremely adverse conditions ., Although the role of LPG in the interaction with the vertebrate host immune system has been studied , it is still unclear how its polymorphism affects the parasite survival ., L . amazonensis LPG induces release of NETs and LTB4 production by neutrophils , thus contributing to diminish parasite burden in the Leishmania inoculation site 14 , 16 ., Additionally , L . mexicana LPG induce TNF-α and IL-10 in monocytes , modulates IL-12 production and diminishes NF-κB nuclear translocation 44 ., Here we show that LPGs from both L . amazonensis strains stimulates NO and cytokine production ( TNF-α and IL-6 ) by peritoneal murine macrophages via TLR4 ., A similar cytokine production was also observed for other species such as L . braziliensis LPG , another important dermotropic species ., However , this activation was primarily via TLR2 30 ., The NO production by macrophages play a central role in determining intracellular killing of Leishmania 45 and the intact structure of LPG appears to be important for this activation 12 , 29 ., In many models , NO synthesis is dependent on a combination of IFN-γ and TNF-α via TLR-dependent mechanisms as an important leishmanicidal effector complex to macrophages 46 ., In conclusion , the preliminary variations in the sugar motifs of LPG , did not result in any difference in macrophage activation/signaling thus suggesting the role of conserved motifs such as the lipid anchor 29 ., Previous studies have demonstrated that different macrophage receptors mediate the uptake and phagocytosis of Leishmania ., The early recognition of pathogens by cells capable of synthesizing cytokines is crucial for the adequate control of intracellular pathogens ., Gene knockout studies in mice have suggested that TLR signaling is essential for the immune response against Leishmania parasites ., Moreover , Leishmania LPGs and GIPLs are agonists of TLR2 and TLR4 28–30 , 41 , 42 ., Glyconjugates can modulate the host immune response and their activity seems to be structure dependent ., The L . braziliensis LPG exerts a pro-inflammatory interaction with TLR2 , inducing the production of NO and cytokines ( IL-1β , TNF-α and IL-6 ) ., On the other hand , the L . infantum LPG was shown to be immunosuppressive and did not induce NO , cytokines and NF-κB translocation 30 ., Our results indicate that LPG from both L . amazonensis strains induce the production of NO and cytokines in IFN-γ-primed macrophages via TLR4 ., However in other members of the L . mexicana complex , L . mexicana LPG activates either TLR2 or TLR4 leading to ERK and p38 MAPK phosphorylation and production of cytokines in human macrophages 42 ., Thus , although it has been shown that LPG of Leishmania activates TLRs and that the engagement of these receptors is important for the infection , the complete intracellular processes that are involved in this activation remain unknown ., Here we bring some light into the effects of LPG on MAPK and NF-κB signaling , a kinase and transcription factor known for their crucial role in immune defense against pathogens 44 , 47–49 ., According to previous reports , infection by L . amazonensis altered phosphorylation of ERK1/2 in response to LPS in murine macrophages 50 and also activates a transcriptional repressor of the NF-κB 48 , 51 ., Consistent with those observations , here LPGs from both L . amazonensis strains also activated p-IκBα , a NF-κB translocation inhibitor , via TLR4 ., Since no further NF-κB translocation was detected in the CHO cells , a possible mechanism that has been suggested favors its inhibition by p50/p50 NF-κB homodimer 55 ., Moreover , L . donovani and L . major infection caused inactivation of ERK1/2 and p38 , respectively , which was accompanied by the inhibition of transcription factors also modulation of cytokine production 52 , 53 ., In contrast to GIPLs ( with fail to activate MAPKs ) 41 , our data show that LPG from both L . amazonensis strains is equally activating MAPKs ( p38 and JNK ) and p-IκBα in peritoneal murine macrophages via TLR4 ( Fig 3 ) ., On the other hand , these LPGs do not activate the NF-κB translocation ., These and our results strongly suggest that Leishmania species have distinct mechanism of modulating the signaling pathways during immunopathological events ., The role of LPG during the interaction with the invertebrate host is a very controversial subject and it has been extensively investigated using in vitro and in vivo models 8 , 21 , 24 , 54 , 55 ., Although the in vitro system has limitations 56 , this model provided important evidence for parasite attachment in the sand fly midgut using many restricted and specific vector as classified elsewhere 57 , 58 ., For example , successful binding to the midgut was reported using the Old World pairs L . major/Phlebotomus papatasi 8 , 54 , L . major/Phlebotomus duboscqi 59 and L . tropica/Phlebotomus sergenti 60 ., Perhaps , due its similarity to L . major LPG , who also possesses terminal β-galactosyl residues , L . turanica LPG may also be important for development in P . papatasi 61 , 62 ., Moreover , the role of LPG has been questioned in permissive vectors such as Lutzomyia longipalpis and Phlebotomus perniciosus , where LPG mutants of L . mexicana and L . major were able to sustain infection in those vectors 63 ., Recently , an alternative mechanism was suggested that flagellar protein FLAG1/SMP1 has been also implicated as an attachment binding candidate for specific and restricted vectors ., In this work , a competitive binding assays using an antibody against FLAG1/SMP1 inhibited interaction using the pair L . major and P . papatasi ., However , no effect was observed for permissive L . longipalpis 64 ., The significance of LPG modifications was investigated during in vivo interaction of L . amazonensis with L . migonei ., Although L . amazonensis is naturally transmitted by L . flaviscutellata , the absence of a colony led us to use an alternative sand fly , which had been previously shown to successfully harbor this parasite and L . braziliensis 5 ., Since this species , although suspected , is not yet considered a natural proven vector of L . amazonensis , a high parasite doses was artificially offered to the sand flies ., In spite of a loss after the 3rd day , parasite multiplication inside the alimentary tract of the L . migonei was successful for both L . amazonensis strains ., To survive , the parasites need avoid a number of barriers including the lethal effects of digestive enzymes in the early blood-fed midgut and the excretion with the digested blood meal 5 , 7 , 65 , 66 ., The strong correlation between the excretion of blood meal and the sudden loss of promastigotes suggests that the inability of Leishmania strains to persist in an inappropriate sand fly is related to their failure to remain anchored to the gut wall via specific attachment sites 22 , 67 ., Nevertheless , L . migonei was able to sustain infection with both of the L . amazonensis strains tested , regardless of the type of LPG ., It seems likely that L . migonei together with L . longipalpis might be considered a permissive vector as previously suggested 57 , 58 , 68 ., However , the fully development of those two L . amazonensis strains should be further investigated ., Some studies have determined that polymorphisms in the phosphoglycan domains of LPG might be crucial for Leishmania promastigotes to attach to the midgut and to maintain vector infection after blood meal excretion 9 ., Additional support is based on the altered behavior of LPG deficient L . donovani and L . major mutant promastigotes ( lpg- ) who showed diminished capacity to maintain infection within the sand fly midgut 54 , 69 ., Furthermore , it was recently presented the occurrence of intraspecies polymorphism in L . infantum LPG ., Also , the biological role of the three LPG types ( I , II and III ) was studied during the interaction with the vector L . longipalpis 24 ., Consistent with our results , all strains could successfully sustain infection in this vector , indicating that LPG polymorphisms did not affect this process ., In spite of having a strong evidence for the existence of a midgut receptor for LPG , there is no current information in L . migonei ., Indeed , the only known receptor was described for L . major , a galectin receptor found in the midgur of P . papatasi binding to LPG β-galactose residues 9 , 70 ., The existence of midgut glycoproteins bearing terminal N-acetylgalactosamine in sand fly was also suggested as a putative parasite ligand 71 ., Here we describe for the first time the immunomodulary properties of two LPGs isolated from different hosts ., Those LPGs were equally able to trigger NO and cytokine ( TNF-α and IL-6 ) production via TLR4 ., The preliminary differences in carbohydrate structure did not seem to affect the interaction of these strains with macrophages and the sand fly vector .

Introduction, Materials and Methods, Results, Discussion

The immunomodulatory properties of lipophosphoglycans ( LPG ) from New World species of Leishmania have been assessed in Leishmania infantum and Leishmania braziliensis , the causative agents of visceral and cutaneous leishmaniasis , respectively ., This glycoconjugate is highly polymorphic among species with variation in sugars that branch off the conserved Gal ( β1 , 4 ) Man ( α1 ) -PO4 backbone of repeat units ., Here , the immunomodulatory activity of LPGs from Leishmania amazonensis , the causative agent of diffuse cutaneous leishmaniasis , was evaluated in two strains from Brazil ., One strain ( PH8 ) was originally isolated from the sand fly and the other ( Josefa ) was isolated from a human case ., The ability of purified LPGs from both strains was investigated during in vitro interaction with peritoneal murine macrophages and CHO cells and in vivo infection with Lutzomyia migonei ., In peritoneal murine macrophages , the LPGs from both strains activated TLR4 ., Both LPGs equally activate MAPKs and the NF-κB inhibitor p-IκBα , but were not able to translocate NF-κB ., In vivo experiments with sand flies showed that both stains were able to sustain infection in L . migonei ., A preliminary biochemical analysis indicates intraspecies variation in the LPG sugar moieties ., However , they did not result in different activation profiles of the innate immune system ., Also those polymorphisms did not affect infectivity to the sand fly .

Leishmania amazonensis , a member of the Leishmania mexicana complex , is the causative agent of localized cutaneous leishmaniasis ( LCL ) and anergic diffuse cutaneous leishmaniasis ( ADCL ) 1 , 2 ., It is widely distributed throughout the Amazon basin , where it infects a wide range of terrestrial rodents and , less frequently , marsupials ., Its main vector is Lutzomyia flaviscutellata ( Diptera: Psychodidae ) widely distributed in South America and a recent study has predicted its expansion towards South of Brazil 3 ., Moreover , Lutzomyia migonei ( França , 1920 ) can also harbor the infection of this species 4 , 5 ., Although its transmission to man is very uncommon , L . amazonensis triggers an incurable and disseminated form of cutaneous leishmaniasis 2 , 6 ., However , most of the mechanisms involved in L . amazonensis pathogenesis are still unknown , especially those related to surface molecules ., Glycoconjugates have been extensively characterized as important for the establishment of infection as they protect the parasite from the early action of the host immune system and therefore acting as invasive/evasive strategies ., Consequently , we here present the role of lipophosphoglycan ( LPG ) of L . amazonensis in the interaction with vertebrate and invertebrate hosts .

blood cells, innate immune system, medicine and health sciences, immune cells, immune physiology, cytokines, pathology and laboratory medicine, immunology, sand flies, parasitic diseases, parasitic protozoans, developmental biology, protozoans, leishmania, molecular development, insect vectors, white blood cells, animal cells, epidemiology, pathogenesis, disease vectors, immune system, leishmania infantum, cell biology, leishmania major, host-pathogen interactions, physiology, biology and life sciences, cellular types, macrophages, organisms

journal.ppat.1000076

The Evolutionary Genetics and Emergence of Avian Influenza Viruses in Wild Birds

Low pathogenic ( LP ) , antigenically diverse influenza A viruses are widely distributed in wild avian species around the world ., They are maintained by asymptomatic infections , most frequently documented in aquatic birds of the orders Anseriformes and Charadriformes ., As such , wild birds represent major natural reservoirs for influenza A viruses 1–11 and at least 105 species of the more than 9000 species of wild birds have been identified as harboring influenza A viruses 8 , 12 , 13 ., These influenza A viruses , commonly referred to as avian influenza viruses ( AIV ) , possess antigenically and genetically diverse hemagglutinin ( HA ) 14 and neuraminidase ( NA ) subtypes , which includes all known influenza A virus HA ( H1–H16 ) and NA ( N1–N9 ) subtypes ., At least 103 of the possible 144 type A influenza A virus HA-NA combinations have been found in wild birds 8 , 15 ., AIV maintained in wild birds have been associated with stable host switch events to novel hosts including domestic gallinaceous poultry , horses , swine , and humans leading to the emergence of influenza A lineages transmissible in the new host ., Adaptation to domestic poultry species is the most frequent 16–26 ., Sporadically , strains of poultry-adapted H5 or H7 AIV evolve into highly pathogenic ( HP ) AIV usually through acquisition of an insertional mutation resulting in a polybasic amino acid cleavage site within the HA 15 , 25 ., The current panzootic of Asian-lineage HP H5N1 AIV appears to be unique in the era of modern influenza virology , resulting in the deaths of millions of poultry in 64 countries on three continents either from infection or culling ., There are also significant zoonotic implications of this panzootic , with 379 documented cases in humans , resulting in 239 deaths in 14 countries since 2003 ( as of April 2008 27 ) ., The Asian lineages of HP H5N1 AIV have also caused symptomatic , even lethal , infections of wild birds in Asia and Europe , suggesting that migratory wild birds could be involved in the spread of this avian panzootic 28–31 ., Concern is heightened since wild birds are also likely to be the reservoir of influenza A viruses that switch hosts and stably adapt to mammals including horses , swine , and humans 3 ., The last three human influenza pandemic viruses all contained two or more novel genes that were very similar to those found in wild birds 16 , 20 , 32 , 33 ., Despite the recent expansion of AIV surveillance 7 , 8 , 10 , 34 , 35 and genomic data 5 , 36–38 , fundamental questions remain concerning the ecology and evolution of these viruses ., Prominent among these are:, ( i ) the structure of genetic diversity of AIV in wild birds , including the role played by inter-hemispheric migration ,, ( ii ) the frequency and pattern of segment reassortment , and, ( iii ) the evolutionary processes that determine the antigenic structure of AIV , maintained as discrete HA and NA subtypes ., Herein , we address these questions using the largest data set of complete AIV genomes compiled to date ., The complete genomes of 167 influenza A viruses isolated from 14 species of wild Anseriformes in 4 locations in the U . S . ( Alaska , Maryland , Missouri , and Ohio ) were sequenced; viral isolates consisted of 29 HA and NA combinations , including 11 HA subtypes ( H1–H8 , H10–H12 ) and all 9 neuraminidase subtypes ( N1–N9 ) ., These sequences were collected as part of an ongoing AIV surveillance project at The Ohio State University and collaborators in other states ( 1986–2005 ) using previously described protocols 39 , and more than double the number of complete U . S . -origin avian influenza virus genomes available in GenBank ., In total , 1340 viral gene segment sequences ( 2 , 226 , 085 nucleotides ) were determined ( Table S1 ) and are listed on the Influenza Virus Resource website ( http://www . ncbi . nlm . nih . gov/genomes/FLU/Database/shipment . cgi ) ., Cloacal samples from wild birds frequently show evidence of mixed infections with influenza viruses of different subtypes by serologic analysis 39–41 ., Therefore , the isolates chosen for sequence analysis were subjected to sequential limiting dilutions ( SLD ) 39 ., The amplification and sequencing pipeline employed a ‘universal’ molecular subtyping strategy in which every sample was amplified with sets of overlapping primers representing all HA and NA subtypes ., In this manner , samples without clear prior subtype information , and/or mixed samples , could be accurately analyzed ., Despite performing SLD , 4 samples were shown by sequence analysis to represent a mixed infection ( yielding sequence with more than one HA and/or NA subtype ., In addition 40 samples had mismatches between the initial antigenic subtyping results ( determined on first- or second-egg-passage isolates prior to SLD ) and the subtype determined by sequence analysis of cDNA ( following one SLD of low-egg-passage isolates ) which suggests the possibility of minor populations of antigenically distinct viruses in the low-passage isolate that outgrew the dominant antigenic population in a foreign host system during the SLD or that mixed infections in first egg passage stock caused difficulty in initial subtyping and a dominant strain emerged during SLD ( see table of viral isolates at http://www . ncbi . nlm . nih . gov/genomes/FLU/Database/shipment . cgi to examine the discordant results observed ) ., Thus , up to 44 of 167 ( 26% ) of isolates potentially represent mixed infections in the initial cloacal sample ., Given the SLD procedure , the true rate of mixed infection , as defined by the presence of >1 HA and/or NA subtype , was likely to be even higher , although mis-serotyping cannot also be ruled out ., Sequencing viral genomes directly from primary cloacal material would be the only way to assess the mixed infection frequency , in a manner unbiased by culture , but no such studies have yet been attempted to our knowledge ., For a more comprehensive analysis of AIV diversity , the AIV genomes from this study were compared to other AIV genomes available on GenBank 38 ., In total , 452 HA sequences and 473 NA sequences , representative of the global diversity of AIV , were used in phylogenetic analyses ., For the internal protein genes ( PB2 , PB1 , PA , NP , M , NS ) , a subset of 407 complete globally-sampled AIV genomes was used to assess the degree of linkage among gene segments ., Phylogenetic trees for the HA alignment ( Figures 1a and S1 ) and NA alignment ( Figure 1b and S2 ) are shown here ., Phylogenetic trees for the six other gene segments are presented in Figures S3 , S4 , S5 , S6 , S7 and S8 ., The topology of the HA phylogeny reflects the antigenically defined subtypes , with some higher-order clustering among them ( e . g . , H1 , H2 , H5 and H6; H7 , H10 and H15; Figures 1a and S1 ) , as seen previously in smaller studies 14 , 42 ., Although most subtypes are found in numerous avian species and occupy wide global distributions , this phylogenetic structure indicates that HA subtypes did not originate in a single radiation ., More striking was the high level of genetic diversity between different subtypes; the average amino acid identity of 120 inter-subtype comparisons of full-length HA was 45 . 5% ., As expected , inter-subtype comparisons of the HA1 domain exhibited more diversity , with an average inter-subtype identity of 38 . 5% ., In contrast , intra-subtype identity is high ( averaging >92% ) , even when comparing sequences from different hemispheres ., Hence , the genetic structure of the AIV HA is characterized by highly divergent subtypes that harbor relatively little internal genetic diversity ., However , 4 subtype comparisons show considerably less divergence ( 76–79% identity ) ; H4 vs . H14 , H7 vs . H15 , H13 vs . H16 , and H2 vs . H5 , indicating that they separated more recently ( Figure 1; see below ) ., A similar phylogenetic structure was seen in the NA ( Figure 1b and S2 ) , again with evidence for higher-order clustering ( e . g . , N6 and N9; N1 and N4 ) ., In contrast to the HA , however , levels of genetic divergence among the NA types are more uniform , with the 9 subtypes exhibiting an average inter-subtype identity of 43 . 6% ( with an average intra-subtype identity of >89% ) and no clear outliers ., Hence , no new ( detected ) NA types have been created in the recent evolutionary past ., This correlates with the more uniform distribution of NA than HA subtypes in wild bird AIV isolates 43 ., The topology of the NS segment phylogeny was also of note , being characterized by the deep divergence among the A and B alleles as described 44 ( Figure S8 ) ., Almost every HA and NA subtype of AIV contain both the A and B NS alleles , without evidence of ‘intermediate’ lineages expected under random genetic drift , strongly suggesting that the two alleles are subject to some form of balancing selection ., The NS1 protein has pleiotropic functions during infection in mammalian cells , and plays an important role in down-regulating the type I interferon response 45 ., Supporting these results are the elevated rates of nonsynonymous to synonymous substitution per site ( ratio dN/dS ) observed for the NS1 gene in both avian and human influenza viruses 46 suggesting that the NS1 protein has experienced adaptive evolution in both host types ., Whether this selection relates to the role the NS1 protein plays in its interaction in the type I interferon pathway is currently unclear ., Far less genetic diversity is observed in the 5 remaining AIV gene segments ( PB2 , PB1 , PA , NP , and M - Figures S3 , S4 , S5 , S6 and S7 ) ., Indeed , the extent of diversity in these genes is less than that within a single HA or NA subtype , with average pairwise identities ranging from 95–99% ., Our phylogenetic analysis also revealed a clear separation of AIV sequences sampled from the Eastern and Western Hemispheres , as previously noted ( 3 , 19 ) , indicating that there is relatively little gene flow between overlapping Eastern and Western Hemisphere flyways ., However , despite this strong biogeographic split , mixing of hemispheric AIV gene pools clearly occurs at a low level ( see below ) ., To assess the frequency and pattern of reassortment in AIV , we compared the extent of topological similarity ( congruence ) among phylogenetic trees of each internal segment ., This analysis revealed a remarkably frequent occurrence of reassortment , supporting previous studies on smaller data sets 37 , 47 ., For example , 5 H4N6 AIV isolates were recovered from mallards sampled at the same location in Ohio on the same morning and in the same trap ( Figure 2 ) ., For the internal genes , these viruses contained 4 different genome ‘constellations’ , with only 1 pair of viruses sharing the same constellation ., In the data set as a whole , the large number of different subtype combinations recovered highlights the frequency of reassortment ( Figures 1b and S2 ) , and provides little evidence for the elevated fitness of specific HA/NA combinations in AIV isolates from wild birds ., That the majority of HA/NA combinations have been recovered 8 , 15 also strongly supports the high frequency of reassortment involving these surface protein genes ., Thus , while there is strong evidence of frequent reassortment between HA and NA , we also sought to assess the extent of reassortment among the less commonly studied internal gene segments ., A maximum likelihood test of phylogenetic congruence 48 revealed that although the topologies of the internal segment trees are more similar to each other than expected by chance , so that the segments are not in complete linkage equilibrium ( in which case they would be no more similar in topology than two random trees ) , the difference among them is extensive , indicative of extremely frequent reassortment and with little clear linkage among specific segments ( Figure 3 ) ., Of the 6 internal segments , NS exhibited the least linkage to other genes , falling closest to the random distribution ( i . e . possessed the greatest phylogenetic incongruence ) ., This is compatible with the deep A and B allelic polymorphism in this segment ., In contrast , the M segment showed the greatest phylogenetic similarly , albeit slight , to the other segments ., Overall , however , the relationships between segments are better described by their dissimilarity than their congruence ., Occasional AIV isolates demonstrated hemispheric mixing with reassortment ., As reported previously , the majority of such mixing occurs in shorebirds and gulls 36 ( with the exception of Eurasian lineage H6 HA genes distributed widely in North American Anseriformes 5 as also revealed in this study ) ., Interestingly , no completely Eurasian-lineage AIV genome has been reported in North America , or vice versa 9 , 49 ., This suggests that birds initially carrying AIV between the hemispheric flyways have not been identified in surveillance efforts ., Most mixed isolates possess only one gene segment derived from the other hemisphere , indicating that there is little or no survival advantage for such hemispheric crossovers in the new gene pool ., Since Asian lineage HP H5N1 AIV have been isolated from wild birds in Eurasia 50 , concern has been raised over the importation of the virus into North America via migratory birds ., Our analyses suggest that enhanced surveillance in gulls and other shorebirds may be warranted , and that with frequent reassortment ( see below ) , entire Asian HP H5N1 AIV isolate genome constellations may not be detected in these surveys ., Overall , 25 of 407 ( 6% ) AIV genomes show evidence of hemispheric mixing , with the phylogenies suggesting a general pattern of viral gene flow from Eurasia to North America: 5 North American isolates possessed two Eurasian-lineage internal gene segments , and 20 carried a single segment ., North American isolates possessing a Eurasian-lineage M segment were the most common , seen in 18 isolates ( Figure S7 ) , followed by 8 with a Eurasian PB2 segment ( Figure S3 ) , four with a Eurasian PB1 segment ( Figure S4 ) , and 1 with a Eurasian PA segment ( Figure S5 ) ., The 18 Eurasian M segments and the 8 Eurasian PB2 segments each form monophyletic groups , suggesting single introductions to North America ., In each case , sequences from domestic ducks in China and turkeys in Europe were the closest relatives ., It is therefore theoretically possible that some of these introductions may have been derived from imported poultry rather than migratory birds ., In contrast , 3 of the 4 Eurasian PB1 and the single Eurasian PA segment in North American AIV contained genes whose closest relatives were in viruses found in red-necked stints from Australia ., These small waders are widely migratory , with a range from Siberia to Australasia , and occasionally in Europe and North America ., Interestingly , 23 of 25 such mixed genomes were observed in shorebirds along the U . S . Atlantic coast ., Unfortunately , no complete AIV genomes are available from shorebirds on the U . S . Pacific coast for comparison ., In theory , two evolutionary models can explain the global pattern of AIV diversity , analogous to the allopatric and sympatric models of speciation ., Under the allopatric model , the HA and NA subtypes correspond to viral lineages that became geographically isolated , resulting in a gradual accumulation of amino acid changes among them ., Because of physical separation through geographical divergence , there is no requirement for natural selection to reinforce the partition of HA and NA diversity into discrete subtypes by preferentially favoring mutations at antigenic sites ., In contrast , under the sympatric model , the discrete HA and NA subtypes originate within the same spatial population , such that natural selection must have reinforced speciation; subtypes that were too antigenically similar would be selected against because of cross-protective immune responses ., Therefore , mutations would accumulate first at key antigenic sites , allowing subtypes to quickly diversify in the absence of herd immunity ., The AIV genomic data available here suggest a complex interplay of evolutionary processes ., That discrete HA and NA subtypes , as well as the 2 divergent NS alleles , are maintained in the face of frequent reassortment strongly suggests that each represents a peak on a fitness landscape shaped by cross-immunity ( Figure 4a ) ., Under this hypothesis , ‘intermediate’ HA/NA/NS alleles would be selected against because they generate more widespread herd immunity , corresponding to fitness valleys ., Indeed , it is the likely lack of immunological cross-protection at the subtype level that allows the frequent mixed infections described here ( although mixed infections may also occur in young , immunologically naïve birds ) ., Further , in most cases these divergent HA , NA and NS alleles circulate in the same bird species in the same geographical regions , compatible with their divergence under sympatry ., In addition , 3 of the most closely related pairs of HA subtypes contain an HA that is rarely isolated or limited geographically or by host species restriction , implying that their dispersion is inhibited by existing immunity; H14 has only been isolated rarely in Southern Russia , H15 only in Australia , and H16 has only been described in gulls ., The possible exception is H2–H5 , where both subtypes have been isolated from a variety of bird species in a global distribution ., Although these may represent more recent occurrences of allopatric speciation , antigenic cross-reactivity between the H2–H5 , H7–H15 , H4–H14 pairs was recently demonstrated 51 , again compatible with the sympatric model ., Further support for possible cross-immunity between these subtypes would require experimental challenge studies ., In contrast to the extensive genetic diversity seen in HA , NA and NS , the 5 remaining internal gene segments encode proteins that are highly conserved at the amino acid level , indicating that they are subject to widespread purifying selection ., The fitness landscape for these genes is therefore not determined by cross-immunity , but by functional viability , with less selective pressure to fix advantageous mutations ( Figure 4b ) ., Further , given such strong conservation of amino acid sequence , large-scale reassortment is permitted as it will normally involve the exchange of functionally equivalent segments , with little impact on overall fitness ., These data also suggest that the cross-immunity provided by these proteins is minimal ., Together , these global genomic data provide new insight into the different evolutionary dynamics exhibited by influenza A viruses in their natural wild bird hosts and in those viruses stably adapted to novel species ( e . g . , domestic gallinaceous poultry , horses , swine , and humans ) ., Based on these analyses , we hypothesize that AIV in wild birds exists as a large pool of functionally equivalent , and so often inter-changeable , gene segments that form transient genome constellations , without the strong selective pressure to be maintained as linked genomes ., Rather than favoring successive changes in single subtypes , geographic and ecologic partitioning within birds , particularly within the different flyways , coupled with complex patterns of herd immunity , has resulted in an intricate fitness landscape comprising multiple fitness peaks of HA , NA and NS alleles , interspersed by valleys of low fitness which prevent the generation of intermediate forms ( Figure 4a ) ., In contrast , stable host switching involves the acquisition of a number of ( as yet ) poorly characterized mutations 24 , 33 , 52 , 53 that serve to separate an individual , clonally derived influenza virus strain from the large wild bird AIV gene pool ., Because adaptation to a new host likely limits the ability of these viruses to return to the wild bird AIV gene pool 24 , 54 , these emergent viruses must evolve as distinct eight-segment genome configurations within the new host ., The ability of recent HP H5N1 AIV to cause spillover infections in wild birds is an unprecedented exception ., Further , because humans represent a large and spatially mixed population , natural selection is able to act efficiently on individual subtypes 55 ., Hence , a limited number of subtypes circulate within humans and evolve by antigenic drift to escape population immunity ., Notably , the recent Asian lineage HP H5N1 AIV strains are intermediate between these two contrasting influenza ecobiologies; a combination of large poultry populations allows natural selection to effectively drive rapid antigenic and genetic change within a single subtype 46 , 56 , while reassortment with the wild bird AIV gene pool facilitates the generation of new genome constellations 57–59 ., Similar patterns have also been observed with the widely circulating H9N2 and H6N1 viruses in gallinaceous poultry in Eurasia 60 , 61 ., Previous analyses have also shown that recent HP H5N1 viruses had the highest evolutionary rates and selection pressures ( dN/dS ratios ) as compared to other AIV lineages 46 ., Consequently , these results underscore the importance of determining the mechanistic basis of how H5N1 has spread so successfully among a diverse range of both domestic and wild bird species ., The genomes of 167 influenza A virus isolates recovered from 14 species of wild Anseriformes located in four U . S . states ( Alaska , Maryland , Missouri , Ohio ) were sequenced for this study; viral isolates consisted of 29 hemagglutinin ( HA ) and neuraminidase ( NA ) combinations , including H1N1 , H1N6 , H1N9 , H2N1 , H3N1 , H3N2 , H3N6 , H3N8 , H4N2 , H4N6 , H4N8 , H5N2 , H6N1 , H6N2 , H6N5 , H6N6 , H6N8 , H7N3 , H7N8 , H8N4 , H10N7 , H10N8 , H11N1 , H11N2 , H11N3 , H11N6 , H11N8 , H11N9 , H12N5 ., Cloacal swabs were collected as previously described 39 from 1986–2005 as part of The Ohio State Universitys ongoing influenza A virus surveillance activities and in collaboration with many researchers in other states since 2001 ., A table listing the details of each isolate are available from the Influenza Virus Resource page ( http://www . ncbi . nlm . nih . gov/genomes/FLU/Database/shipment . cgi ) ., Avian influenza viruses were originally isolated using standard viral isolation procedures after 1–2 passages in 10-day-old embryonated chicken eggs ( ECEs ) 62 ., Type A influenza virus was confirmed using commercially available diagnostic assays ( Directigen Flu A Assay , Becton Dickinson Microbiology Systems , Cockeysville , MD ) and isolates were subtyped at the National Veterinary Services Laboratories ( NVSL ) , Animal and Plant Health Inspection Service , United States Department of Agriculture , Ames , Iowa , using standard hemagglutinin inhibition and neuraminidase inhibition testing procedures 51 ., Isolates for this investigation were generally selected from the viral archives based on antigenic diversity , clustering of recoveries , no evidence of antigenically mixed subtypes , and distribution over time ., First- or second-egg-passage isolates in chorioallantoic fluid ( CAF ) were rapidly thawed from −80°C to room temperature , vortexed for 30 seconds and centrifuged at 1500 rpm for 10 minutes ., Approximately 0 . 5 ml of CAF was drawn from the vial using a 26-gauge needle and subsequently passed through a 25 mm , 0 . 2 µm filter ., Following filtration , a 10−1 CAF stock dilution was obtained by adding 0 . 2 ml filtered CAF to 1 . 8 ml Brain Heart Infusion Broth containing penicillin and streptomycin and vortexed for 30 seconds ., Serial dilutions ( 10−6 maximum ) were performed and 0 . 1 ml of each dilution was inoculated into each of four 10-day-old ECEs ., After approximately 48 hours of incubation at 35°C/60% humidity , the inoculated eggs were chilled overnight and CAF was harvested from each egg and tested for hemagglutinating activity ., The CAF from the last dilution positive for hemagglutinating activity was tested for the presence of type A influenza virus using the Directigen Flu A or Synbiotics Flu Detect Antigen Capture Test Strips™ ( Synbiotics Corp . , San Diego , CA ) ., Hemagglutination titer assays were performed and CAF aliquots from the most dilute influenza A positive samples were stored at −80°C ., If no endpoint titer was determined , the 10−6 CAF dilution was stored at −80°C and the procedure repeated utilizing 10−4 to 10−9 sequential dilutions ., Viral RNA was isolated from allantoic fluid using Trizol® Reagent ( Invitrogen Corp . , Carlsbad , CA ) and transcribed into 20 µl of cDNA for a subset of samples 63 ., Segment-specific universal primers designed to amplify partial and/or full-segments were initially used in RT-PCR assays to assess vRNA quality and RT-PCR primer specificity and sensitivity ., Additionally , M13 sequencing tags ( F primer: GTAAAACGACGGCCAG; R primer: CAGGAAACAGCTATGAC ) were added to each primer set for ease of sequencing RT-PCR products in both forward and reverse directions ., For initiation of a high-throughput sequencing pipeline , a universal strategy for primer design was employed to ensure detection of multiple viral infections within a single sample ., Primers were designed to semi-conserved areas of the six internal segments ., For the segments encoding the external proteins , primers were designed from alignments of subsets of the 16 HA and 9 NA avian subtypes ., Alignments were generated with MUSCLE 64 and visualized with BioEdit 65 ., An M13 sequence tag was added to the 5′ end of each primer to be used for sequencing ., Four sequencing reactions per run were analyzed on an agarose gel for quality control purposes ., The sequence success rate of each primer pair was analyzed relative to the HA and NA subtype ., Primers that did not perform well were altered or replaced ., All primers and RT-PCR assay cycling conditions are available upon request ., Influenza A virus isolates were amplified with the OneStep RT-PCR kit ( Qiagen , Inc . , Valencia , CA ) ., Amplicons were sequenced in both the forward and reverse directions ., Each amplicon was sequenced from each end using M13 primers ( F primer: TGTAAAACGACGGCCAGT; R primer: CAGGAAACAGCTATGACC ) ., Sequencing reactions were performed using Big Dye Terminator chemistry ( Applied Biosystems , Foster City , CA ) with 2 µl of template cDNA ., Additional RT-PCR and sequencing was performed to close gaps and to increase coverage in low coverage or ambiguous regions ., Sequencing reactions were analyzed on a 3730 ABI sequencer and sequences were assembled in a software pipeline developed specifically for this project ., Once genomic sequence was obtained for an individual sample , reads for each segment were downloaded , trimmed to remove amplicon primer-linker sequence and low quality sequence , and assembled ., A small genome assembly suite called Elvira ( http://elvira . sourceforge . net/ ) , based on the open-source Minimus assembler , was developed to automate these tasks ., The Elvira software delivers exceptions including failed reads , failed amplicons , and insufficient coverage to a reference sequence ( as obtained from GenBank ) , ambiguous consensus sequence calls , and low coverage areas ., The avian influenza A sequences ( with GenBank Accession numbers ) produced from this ongoing study are available at http://www . ncbi . nlm . nih . gov/genomes/FLU/Database/shipment . cgi ., The first 167 avian influenza genomes from this collection were submitted to GenBank and included in this study ., The genomes of avian influenza virus newly determined here were combined with those already available on GenBank , particularly from recent large-scale surveys of viral biodiversity 38 ., Sequences from viruses isolated before 1970 , which may have been subjected to extensive laboratory passage , were excluded as were the large numbers of H5N1 sequences collected in recent years ( a sample of H5N1 genomes , 1997–2005 , were included for analysis ) ., In total , 452 HA sequences and 473 NA sequences were used in analyses ., For the internal protein-encoding segments ( PB2 , PB1 , PA , NP , M , NS ) , a total of 407 genomes were analyzed ( by considering a common data set we were able to investigate patterns of segment linkage , see below ) ., For each data set , sequence alignments of the coding regions were created using MUSCLE 64 and adjusted manually using Se-Al 66 according to their amino acid sequence ., In the case of HA and NA , some regions of the inter-subtype sequence alignment were extremely divergent such that they could not be aligned with certainty ( HA signal peptide and cleavage site insertions in HPAI H5 or H7 , and variable small stalk deletions in NA ) ., Because of their potential to generate phylogenetic error , these small regions of ambiguity were deleted ., This resulted in the following sequence alignments used for evolutionary analysis: PB2\u200a=\u200a2277 nt; PB1\u200a=\u200a2271 nt; PA\u200a=\u200a2148 nt; HA\u200a=\u200a1683 nt; NP\u200a=\u200a1494 nt; NA\u200a=\u200a1257 nt; M\u200a=\u200a979 nt; NS\u200a=\u200a835 nt ., All sequence alignments are available from the authors on request ., Nucleotide and amino acid identity was calculated using Megalign ( Lasergene 7 . 2 , DNAStar , Madison , WI ) ., Using these alignments , maximum likelihood ( ML ) trees were inferred using PAUP* 67 , based on the best-fit models of nucleotide substitution models determined by MODELTEST 68 ., In most cases , the preferred model of nucleotide substitution was GTR+I+Γ4 , or a close relative ., For each of these trees , the reliability of all phylogenetic groupings was determined through a bootstrap resampling analysis ( 1000 pseudo-replicates of neighbor-joining trees estimated under the ML substitution model ) ., We employed a maximum likelihood method to assess the extent of phylogenetic congruence , indicative of reassortment 48 ., To reduce any bias in phylogenetic structure caused by geographic segregation , only isolates from North American flyways were used in analyses of the internal gene segments ., Briefly , ML trees for each internal gene segment were estimated as described above ., Next , the log likelihood ( -LnL ) of each of the ML trees was estimated on each gene segment data set in turn , optimizing branch lengths under the ML substitution model in every case ., The topological similarity between each gene segment tree on each data set was then determined by compared the difference in likelihood among them ( Δ-LnL ) ., Clearly , the greater the similarity in topology ( congruence ) among the trees for each segment , the closer their likelihood scores and so the more likely they are to be linked ., To put the distribution of Δ-LnL values in context , we constructed 500 random trees for each data set and optimized their branch lengths in the same manner ., If any of the Δ-LnL values among the ML trees falls within the random distribution then we can conclude that the gene segments in question are in complete linkage equilibrium ., All these analyses were conducted using PAUP* package 67 .

Introduction, Results/Discussion, Materials and Methods

We surveyed the genetic diversity among avian influenza virus ( AIV ) in wild birds , comprising 167 complete viral genomes from 14 bird species sampled in four locations across the United States ., These isolates represented 29 type A influenza virus hemagglutinin ( HA ) and neuraminidase ( NA ) subtype combinations , with up to 26% of isolates showing evidence of mixed subtype infection ., Through a phylogenetic analysis of the largest data set of AIV genomes compiled to date , we were able to document a remarkably high rate of genome reassortment , with no clear pattern of gene segment association and occasional inter-hemisphere gene segment migration and reassortment ., From this , we propose that AIV in wild birds forms transient “genome constellations , ” continually reshuffled by reassortment , in contrast to the spread of a limited number of stable genome constellations that characterizes the evolution of mammalian-adapted influenza A viruses .

Influenza A viruses are an extremely divergent group of RNA viruses that infect in a variety of warm-blooded animals , including birds , horses , pigs , and humans ., Since they contain a segmented RNA genome , mixed infection can lead to genetic reassortment ., It is thought that the natural reservoir of influenza A viruses is the wild bird population ., Influenza A viruses can switch hosts and cause novel outbreaks in new species ., Influenza viruses containing genes derived from bird influenza viruses caused the last three influenza pandemics in humans ., In this study , we surveyed the genetic diversity among influenza A viruses in wild birds ., Through a phylogenetic analysis of the largest data set of wild bird influenza genomes compiled to date , we were able to document a remarkably high rate of genome reassortment , with no clear pattern of gene segment association and occasional inter-hemisphere gene segment migration and reassortment ., From this , we propose that influenza viruses in wild birds forms transient “genome constellations , ” continually reshuffled by reassortment , in contrast to the spread of a limited number of stable genome constellations that characterizes the evolution of mammalian-adapted influenza A viruses .

evolutionary biology/microbial evolution and genomics, virology/virus evolution and symbiosis, evolutionary biology/bioinformatics, evolutionary biology/evolutionary ecology

journal.pcbi.1002614

Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records: Novel Myopathy Associated Drug Interactions

Drug-drug interactions ( DDIs ) are a major cause of morbidity and mortality and lead to increased health care costs 1–3 ., DDIs are responsible for nearly 3% of all hospital admissions 4 and 4 . 8% of admissions in the elderly 1 ., And with new drugs entering the market at a rapid pace ( 35 novel drugs approved by the FDA in 2011 ) , identification of new clinically significant drug interactions is essential ., DDIs are also a common cause of medical errors , representing 3% to 5% of all inpatient medication errors 5 ., These numbers may actually underestimate the true public health burden of drug interactions as they reflect only well-established DDIs ., Several methodological approaches are currently used to identify and characterize new DDIs ., In vitro pharmacology experiments use intact cells ( e . g . hepatocytes ) , microsomal protein fractions , or recombinant systems to investigate drug interaction mechanisms ., The FDA provides comprehensive recommendations for in vitro study designs , including recommended probe substrates and inhibitors for various metabolism enzymes and transporters 6 ., The drug interaction mechanisms and parameters obtained from these in vitro experiments can be extrapolated to predict in vivo changes in drug exposure ., For example , a physiologically based pharmacokinetics model was developed to predict the clinical effect of mechanism based inhibition of CYP3A by clarithromycin from in vitro data 7 ., However , in vitro experiments alone often cannot determine whether a given drug interaction will affect drug efficacy or lead to a clinically significant adverse drug reaction ( ADR ) ., In vivo clinical pharmacology studies utilize either randomized or cross-over designs to evaluate the effect on an interaction on drug exposure ., Drug exposure change serves as a biomarker for the direct DDI effect , though drug exposure change may or may not lead to clinically significant change in efficacy or ADRs ., The FDA provides well-documented guidance for conducting in vivo clinical pharmacology DDI studies 6 ., If well-established probe substrates and inhibitors are used , involvement of specific drug metabolism or transport pathway can be demonstrated by in vivo clinical studies ., For example , using selective probe substrates of OATPs ( pravastatin ) and CYP3A ( midazolam ) and probe inhibitors of OATPs ( rifampicin ) and CYP3A ( itraconazole ) , it was shown that hepatic uptake via OATPs made the dominant contribution to the hepatic clearance of atorvastatin in an in vivo clinical PK study 8 ., However , due to overlap in substrate selectivity , an in vivo DDI study alone will often not provide mechanistic insight into the DDI ., Finally , in populo pharmacoepidemiology studies use a population-based approach to investigate the effect of a DDI on drug efficacy and ADRs ., For example , the interactions between warfarin and several antibiotics were evaluated for increased risk of gastrointestinal bleeding and hospitalization in a series of case-control and case-crossover studies using US Medicaid data 9 ., Indeed , epidemiological studies using large clinical datasets can identify potentially interacting drugs within a population , but these studies alone are insufficient to characterize pharmacologic mechanisms or patient-level physiologic effects ., The aforementioned in vitro , in vivo , and in populo research methods are complementary in characterizing new drug-drug interactions ., Yet these methods are all limited by their relatively small scale ., Such studies usually focus on a few drug pairs for one or a limited number of metabolizing enzymes or transporters a time ., Performing large scale screening for novel drug interactions requires higher throughput strategies ., Literature mining and data mining have become powerful tools for knowledge discovery in biomedical informatics , and are particularly useful for hypothesis generation ., A recent notable example in clinical pharmacology is the successful detection of novel DDIs through mining of the FDAs Adverse Event Reporting System 10 ., In this study , pravastatin and paroxetine were found to have a synergistic effect on increasing blood glucose ., This finding was validated in three large electronic medical record ( EMR ) databases ., While a ground-breaking success , this approach provides little evidence regarding the mechanism of the interaction ., In this paper , we present a novel approach using literature mining for screening of potential DDIs based on mechanistic properties , followed by EMR-based validation to identify those interactions that are clinically significant ., We focus on clinically and statistically significant DDIs that increase the risk of myopathy ., Our initial drug dictionary consisted of 6937 drugs ., Of these , 1492 drugs were validated as FDA approved drugs ( Figure 1 ) ., Among these 1492 drugs , our text mining approach identified 232 drugs , as either CYP substrates or inhibitors ( Table S1 ) ., Recall rate ( i . e . the proportion of true positives identified by the text mining method among all the true positives ) and accuracy ( i . e . the proportion of true positives among the text mined results ) were used to evaluate the text mining performance ., The recall rate of this text mining analysis was 0 . 97 , with the information retrieval ( IR ) step being rate-limiting ., In the information extraction ( IE ) step , the two initial curators agreed on 78% of cases ., The third curator was able to establish DDI relevance and extract information in the 22% of cases which were in disagreement ., The third curator also confirmed 100% accuracy among 20% of randomly chosen abstracts that the first two curators had agreed upon ., Therefore , the accuracy of our text mining analysis reached 100% ., These drugs metabolism and inhibition enzymes were experimentally determined by probe substrates and inhibitors recommended by the FDA Drug-Drug Interaction guidelines ., Their categorizations are reported in Table S1 ., Out of the 149 CYP substrates identified , 102 ( 68% ) , were substrates of CYP3A4/5 ., This was consistent with the literature that about half of the drugs on the market which undergo metabolism are metabolized by CYP3A 11 ., A total of 59 drugs were found to undergo metabolism by more than one CYP enzyme ., We also identified 123 CYP inhibitors , with CYP3A4/5 , CYP2D6 , CYP2C9 , CYP1A2 , and CYP2C19 having comparable numbers of inhibitors , ( 48 , 39 , 39 , 39 , 31 respectively ) ., Fewer inhibitors were identified for other enzymes ., Fifty inhibitors were found to inhibit more than one enzyme ., Among 232 drugs with known metabolism and/or inhibition enzyme information ( Figure 1 ) , 13 , 197 drug interaction pairs were predicted based on their pertinent CYP enzymes ( Figure 2 ) ., Among these 13 , 197 predicted DDIs , 3670 DDI pairs were prescribed as co-medications in actual patients within the Common Data Model ( CDM ) dataset ., In other words , these 3670 predicted DDI pairs may have potential real-world clinical implication ., Among those 3670 predicted DDI pairs from in vitro studies , text mining identified 196 pairs with published clinical drug-drug interaction study results ., These in vivo studies tested whether a substrate drugs exposure ( i . e . systemic drug concentration ) was increased when co-administrating with an inhibitor ., The recall rate of this text mining analysis was 0 . 94 ., The accuracy of this text mining analysis reached 100% , after manual IE from two curators and validation from the third ., Among these 196 in vivo validated DDI pairs , 123 of them were found to have significant DDIs ( Figure 2 ) , i . e . drug exposure increased significantly ( P<0 . 05 ) , and it increased by more than 2 fold ., The additional 73 pairs were considered not to be clinically significant DDIs ., In our CDM dataset , there were medication records on 817 , 059 patients ., Among these patients , 59 , 572 ( 7 . 2% ) experienced myopathy events ( Table 1 ) ., Two major subcategories of myopathy: myalgia and myositis/muscle weakness accounted for more than 95% of the cases ., There were 53 rhabdomyolysis cases ., In the cohort of individuals suffering a myopathy event , the average age was 40 . 2 year ( SD\u200a=\u200a23 years ) ; 59 . 1% were female , and the average medication frequency was 3 . 8 ( SD\u200a=\u200a2 . 5 ) ., However , 65 . 8% of the race data were missing ., In our initial data analysis , we found that females had higher myopathy risk than males ( 8 . 6% vs 5 . 4% , p<2e-16 , Table 2 ) ; and each one year increase in age was associated with 0 . 15% higher myopathy risk ( p<2e-16 ) ., These results were consistent with the literature 12 ., The 3670 DDI pairs identified in the CDM database were tested using the additive model , i . e . whether an inhibitor would increase the myopathy risk of the substrate compared to the substrate alone ., Both age and sex were justified in the logistic regression ., The p-value threshold was chosen as 0 . 05/3670\u200a=\u200a0 . 0000136 after Bonferroni justification , with OR greater than 1 ., There were 124 and 287 significant DDI pairs for CYP2D6 and CYP3A4/5 enzymes , respectively ( Figure 3 and Table S2 ) ., The other enzymes had fewer significant DDI pairs ., Pathway enrichment analysis suggested similar results , i . e . CYP2D6 and CYP3A4/5 enzymes had more significant DDI pairs than the other enzymes , p\u200a=\u200a8E-8 and 4E-2 respectively ., Although this DDI analysis was confounded by the other co-medication variables , it was indeed a global description of DDI effects from various CYP enzymes ., This global analysis provided us a picture of the metabolism enzymes that were most important in understanding the increased myopathy risk associated with DDIs ., In order to remove the effect of myopathy risk of the inhibitor itself , a synergistic DDI test was conducted to determine whether substrate and inhibitor together have higher risk than the combined additive risk when the substrate or inhibitor is taken alone ., Both age and sex were justified as covariates ., DDI pairs were removed if either one of the drugs was prescribed to treat symptoms of myopathy ., We set the significance threshold as p\u200a=\u200a0 . 0000136 , as justified the multiple primary hypotheses on 3670 predicted DDI pairs ., Table 3 presents the five significant synergistic DDI pairs: ( loratadine , simvastatin ) , ( loratadine , alprazolam ) , ( loratadine , duloxetine ) , ( loratadine , ropinirole ) , and ( promethazine , tegaserod ) ., Their relative risks were ( 1 . 69 , 1 . 86 , 1 . 94 , 3 . 21 , 3 . 00 ) respectively , the p-values were ( 2 . 03E-07 , 2 . 44E-08 , 5 . 60E-07 , 2 . 60E-07 , 2 . 60E-07 , 8 . 22E-07 ) respectively , and their associated enzymes were primarily CYP3A4/5 and CYP2D6 ., Additional analyses of myopathy were performed for these five DDI pairs ., In the first myopathy analysis , the total number of medications ordered during the drug exposure window was added as a covariate in the logistic regression ., This variable was used as a surrogate marker for the comorbidities of a patient ., The average number of medications used by individuals during the drug exposure window was 3 . 6 with SD\u200a=\u200a2 . 4 ., Table 4 presents the five DDI effects on myopathy after adjusting for the total number of medications ., Compared to table 3 , all the single drug myopathy risks and drug combination risks were reduced after justifying for the number of co-medications ., The DDI evidence became even more significant ( p-values less than 3e-12 ) , and risk ratios became even bigger , between 2 . 72 and 7 . 00 ., The medication frequency itself was also associated with increased myopthay risk ., The addition of one co-medication was associated with an increased myopathy risk between 0 . 6% and 0 . 9% in testing the 5 DDI pairs ., All p-values are less than 2e-16 ., In the second myopathy analysis , only the first myopathy events were considered , because co-medications administered after the first myopathy event but before the follow-up myopathy events were potential confounders ., In other words , it was difficult to justify whether the co-medication drug exposure resulted from the myopathy or caused myopathy ., Table S3 presents the data analysis for the DDI pairs: ( loratadine , simvastatin ) , ( loratadine , alprazolam ) , ( loratadine , ropinirole ) , ( loratadine , duloxetine ) , and ( promethazine , tegaserod ) ., Their relative risks are ( 1 . 34 , 1 . 38 , 1 . 38 , 1 . 81 , 1 . 70 ) respectively , the p-values are ( 3 . 20E-03 , 2 . 1E-05 , 9 . 4E-04 , 3 . 1E-03 , 2 . 3E-03 ) respectively ., This analysis based on first myopathy event with these five selected DDI pairs confirmed the trend of our previous synergistic DDI analysis ., Unlike DDI signal detection from AERS by Dr . Altmans group 10 , we enriched our EMR signal detection by focusing on CYP-mediated DDIs that were mined and predicted from PubMed abstracts ., There are multiple recent publications on drug interaction text mining ., Two automatic literature mining systems were developed to predict drug interactions based on their associated metabolism enzymes 13 , 14 ., An evidential approach was developed to differentiate in vitro and in vivo DDI studies , curate drug metabolism and inhibition enzymes , and predict DDIs based on their pertinent enzymes 15 ., Our text mining approach took advantage of these two methods , i . e . metabolism based DDI prediction; and emphasized the text mining performance more stringently ., The IR step of our method is an automatic algorithm , which has high recall rate ( 0 . 97 ) ; while the IE step is a manual curation step , with high precision ( 100% ) ., In addition , we implemented CYP enzyme probe substrates and inhibitors from the FDA guidance into the literature mining method ., This strategy supplies information on the potential mechanism for the predicted DDIs ., Our current text mining method focuses on pharmacokinetic-based drug interaction literature , i . e . reported substrate drug exposure changed by drug interaction ., Text mining which focuses on pharmacodynamics ( PD ) DDI literature has been recently discussed 16 , 17 ., PD DDI literature reports the drug efficacy or side-effect changes , but it usually does not report drug exposure change ., Among the 13197 predicted DDIs from in vitro PK study literature mining , 3670 of them may have clinical relevance , i . e . they were taken as co-medications by at least some of the 2 . 2 million patients in our clinical dataset ., However , only 196 of them ( 5 . 3% ) have been tested in clinical pharmacokinetic DDI trials ., Among these 196 clinically tested DDIs , 123 of them ( 62 . 7% ) showed significant substrate drug exposure increase when co-administrated with the inhibitor ., This striking finding calls for further evaluation of those predicted DDIs that have not been subjected to rigorous study ., As a matter of fact , all five DDI pairs which showed an increased myopathy risk in our pharmaco-epidemiology study lack clinical pharmacokinetic studies ., The FDA labels of all 7 of the drugs which comprise the five significant DDI pairs report myopathy related side effects ( Table S4 ) ., This evidence confirms the myopathy risk for each individual drug ., In order to understand the mechanisms of each interaction , we further explored literature regarding those agents ., In Figure 4 and Table S5 , we integrated information on the metabolism and inhibition enzymes of those 7 drugs from a full-text based literature review of reported in vitro studies of the drugs ., Table 5 presented the DDI potency prediction for the five DDI pairs ., Loratadine ( substrate ) and simvastatin ( inhibitor ) were predicted to have a strong DDI through the CYP3A4/5 enzyme ., Tegaserod ( substrate and inhibitor ) and promethazine ( substrate and inhibitor ) were predicted to have strong DDI through the CYP2D6 enzyme ., Their interactions are mixed inhibition and auto-inhibition ., The other four drug pairs were predicted to have moderate DDIs: loratadine ( inhibitor ) and omeprazole ( substrate ) interact through both the CYP2C19 and CYP3A4/5 enzymes; loratadine ( inhibitor ) and alprazolam ( substrate ) interact through CYP3A4/5; loratadine ( substrate ) and duloxetine ( inhibitor ) interact through the CYP2D6 enzyme; and loratadine ( inhibitor ) and ropinirole ( substrate ) interaction is through CYP3A4/5 ., Two DDI data analysis strategies were implemented to identify drug-drug interactions associated with an increased risk for myopathy ., The first approach employed an additive model coupled with a CYP metabolism pathway enrichment analysis ., This strategy stems from the newly formed discovery nature of bioinformatics research , i . e . to search for commonality among many hypothesis tests ., The second strategy employed a synergistic model coupled with extensive confounder justification ., This strategy follows the more stringent pharmaco-epidemiology considerations , which heavily controls for false positives ., Unlike the additive model , the synergistic model can justify the myopathic risk effect from an inhibitor in the presence of other potential confounders ., Therefore , the additive model would potentially identify more false positive DDIs ., However , the additive model is more powerful than the synergistic model in identifying the true positive DDIs ., Many more DDIs were identified by the additive model based DDI analysis than by the synergistic strategy ., Because pathway enrichment analysis allows more flexibility toward false positive DDIs , the additive model identified CYP3A4/5 and CYP2D6 enzymes as they have the enriched DDI pairs ., Although the synergistic model DDI analysis only inferred five significant DDI pairs , upon additional literature review , it was found that these pairs also showed mechanistic involvement of CYP2D6 and CYP3A4/5 enzymes ., The consistency of the mechanistic interpretations of the two separate DDI analysis strategies delivers an encouraging message: the bioinformatics approach and the pharamco-epidemiology approach are complementary and mutually supportive ., Our synergistic DDI test is a very stringent approach , compared to the additive approach used by the other investigators 9 , 18 , 19 ., We recognize that our synergistic DDI test may exclude some true DDIs ., It assumes that all myopathy is the result of drug administration , and patients who dont take the DDI drugs wont have myopathy ., However , there is a background rate of myopathy in patients that is not due to either of the two drugs in a specific DDI ., If the patients who dont take drugs have a baseline risk of myopathy , the relative risk estimated through our synergistic DDI test will be smaller than the true relative risk ., In our follow-up sensitivity analysis , medication frequency was justified in the DDI analysis ., This factor would also account for a portion of baseline myopathy risk ., Another potential approach to estimate the baseline myopathy risk is to identify a control patient group that matches the demographics , co-morbidity , and co-medication distributions of the group exposed to the DDIs ., This approach deserves further investigation ., Like many pharmaco-epidemiology studies using observational data , our analysis of the DDI effect on myopathy has several limitations ., Creating an accurate phenotypic definition using billing codes may be unreliable , with both false-positives and false-negatives likely to occur ., Our dataset also lacked clinical notes from which more detailed symptom data could be extracted ., Further research including validation with manual chart review is necessary to establish optimal phenotypic definitions for myopathy , as well as more granular definitions for myotoxicity and rhabdomyolysis ., Further research including validation with manual chart review is necessary to establish optimal phenotypic definitions for myopathy , as well as more granular definitions for myotoxicity and rhabdomyolysis using a combination of ICD9 codes , lab tests , and clinical notes ., Another limitation of our analysis is that it is subject to several potential population bias introduced by the EMR database itself ., Our retrospective observational data do not allow for controlling many potential covariates that a traditional prospective study offers ., In particular , the race data is not complete in our database ., It is also equally challenging to design a prospective study to validate our results from a pharmaco-epidemiology study ., Clinical pharmacokinetic studies or further in vitro metabolism/inhibition studies of the selected DDI pairs found to increase myopathy may provide further validation of an interaction between the drugs ., We are also looking forward to validating our results in another large EMR database ., Our text mining and DDI prediction is CYP metabolism enzyme based ., Therefore , our interpretation of the five significant drug interactions focuses only on CYP drug-drug interaction mechanisms ., However , this does not preclude the involvement of other DDI mechanisms , such as drug transporter interactions or pharmacodynamic interactions ., In a recent GWAS study , expression of the OATP1B1 transporter was shown to predict myopathy risk associated with simvastatin 20 ., Therefore , it is possible that loratadine interacts with simvastatin through this or other transporter mechanisms ., Studies are currently underway to further characterize the mechanisms of the five identified DDIs ., The concomitant use of CYP3A metabolized statins ( atorvastatin , lovastatin and simvastatin ) with strong CYP3A inhibitiors ( e . g . ketoconazole and itraconazole ) reportedly increases risk of statin-induced myopathy ., In addition , case reports of increased myopathy in transplant recipients being treated with tacrolimus or cyclosporine 21 argue for the avoidance of this combination ., The interaction between statins and fibrates , specifically gemfibrozil , leading to increased risk of myopathy is well recognized 22 ., Gemfibrozil is a substrate of CYP3A but not a potent inhibitor ., Thus , it is likely that this interaction occurs through pharmacodynamic , not pharmacokinetic , based interactions ., Although these interactions are widely reported , we found no increased risk of myopathy with concomitant use of ketoconazole , itraconazole , tacrolimus , or gemfibrozil within the CDM database ., Their related myopathy risks of these DDIs are reported in Table 6 ., This finding is likely due to the limitation of our data analysis , in which we define concomitant drug administration by prescription orders that occur within a predefined timeframe ., As these drug interactions are well-known , it is likely that although the two drugs may have been ordered within the predetermined time window , the individual may have discontinued one medication before starting the second ., For some drugs that are used short-term , e . g . ketoconazole , it will be difficult to identify true concomitant use from prescription records ., As a matter of fact , among these statin DDI pairs in Table 6 , less than 110 patients took both drugs within the pre-defined one month interval in each pair ., This limited our power to detect significant DDIs to less than 15% , if we anticipate a 1 . 5-fold RR of DDI myopathy ., Provided that medication data in our CDM is relatively new , between 2004 and 2009 , it is likely that clinicians were aware of potential interactions and thus suggested patients avoid co-administration of these interacting drugs ., As described in the introduction , an in vitro , an in vivo , or an in populo pharmacologic study alone cannot cover the whole spectrum of mechanistic and clinically significant DDI research ., These studies usually focus on a few drug pairs for one or a limited number of metabolizing enzymes or transporters at a time ., In this paper , we combined a literature discovery approach and a large EMR database validation method for novel DDI prediction and clinical significance assessment ., The scale of our research covered all FDA approved drugs ., The literature based discovery approach predicted new DDIs and their associated CYP-mediated metabolism enzymes ., The clinical significance of these interactions was then assessed in large database of electronic medical records ., This translational bioinformatics approach successfully identified five DDI pairs associated with increased myopathy risk ., Compared to traditional in vitro , in vivo , and in populo DDI studies , our proposed translational bioinformatics approach covers a broader spectrum and identifies risk on a larger scale ., It certainly motivates more in vitro studies to investigate alternative DDI mechanisms and more clinical pharmacokinetics study to investigate the clinical significance of these DDIs ., The Indiana Network for Patient Care ( INPC ) is a heath information exchange data repository containing medical records on over 11 million patients throughout the state of Indiana ., The Common Data Model ( CDM ) is a derivation of the INPC containing coded prescription medications , diagnosis , and observation data on 2 . 2 million patients between 2004 and 2009 ., The CDM contains over 60 million drug dispensing events , 140 million patient diagnoses , and 360 million clinical observations such as laboratory values ., These data have been anonymized and architected specifically for research on adverse drug reactions through collaboration with the Observational Medical Outcomes Partnership project 23 ., This CDM model is a de-identified eletronic medical record database ., All the research work has IRB approval ., Our drug dictionary consists of 6 , 837 drugs names that include all brand/generic/drug group names ., They were primarily derived from DrugBank 24 ., We then excluded non-approved and experimental drugs , and focused only on FDA approved therapeutic agents , which left 1492 unique drug generic names for the mining purpose ( Figure 1 ) ., The INPC CDM data set has 54490 unique drug “Concept IDs” ., A Concept ID in the CDM typically maps to an RxNorm clinical drug ( e . g . , simvastatin 20 mg ) or ingredient ( simvastatin ) ., Some Concept IDs may contain multiple drug components ( e . g . , lisinopril/hydrochlorothiazide ) ., Our drug dictionary was mapped to CDM Concept IDs using regular expression matching and manual review ., In total , 1293 unique drugs identified from DrugBank were mapped successfully , while 199 drugs could not be matched ., The unmatched drugs were categorized as follows: banned drugs , illicit drugs , organic compounds , herbicide/insecticides , functional group derivatives , herbal extract , DrugBank drugs not covered by CDM , and literature only drug names ., In our CDM dataset , 817059 patients had medication records available ., Literature mining was conducted on 10 CYP enzymes: ( CYP1A2 , CYP2A6 , CYP2B6 , CYP2C8 , CYP2C9 , CYP2C19 , CYP2D6 , CYP2E1 , CYP3A4/CYP3A5 ) ( Figure 5 ) ., Please note that these CYPs cover all the major ones , but not all of the CYPs ., A probe substrate of enzyme E is defined as being selectively metabolized by enzyme E; while a probe inhibitor of enzyme E selectively inhibits enzyme Es metabolism activity ., CYP probe drugs and inhibitors for the DDI text mining approach were selected as those drugs well-established as probes or inhibitors by DDI researchers and defined in the FDA guidance 6 ., The in vitro CYP enzyme substrate and inhibitor text mining and the DDI prediction was divided into the following steps ., In vivo DDI text mining was conducted on those predicted DDI pairs from in vitro DDI text mining ( Figure s1 ) ., It is broken down the following steps ., Demographic variables , age and sex , were justified in the DDI association analyses ., The total number of different medications ordered during the one month drug exposure window was used as a covariate in the logistic regression ., It serves as a surrogate of the patients overall health status , and justifies for myopathy effects from medications other than the hypothesized DDI drug pair ., It is recognized that an individual patient can experience multiple myopathy events ., Our drug-condition model considered two situations: all myopathy events and the first myopathy event ., The advantage of selecting the first myopathy event is that it is not confounded with other medications taken between the first and the follow-up myopathy events ., However , limiting the data to first myopathy even reduces the sample size , and thus the power to identify a DDI ., DDI pairs , in which at least one drug was prescribed to treat symptoms of myopathy ( e . g . narcotic and non-steroidal analgesics ) , were excluded from the DDI tests ., However , the patients prescribed these drugs are kept in the data analysis .

Introduction, Results, Discussion, Methods

Drug-drug interactions ( DDIs ) are a common cause of adverse drug events ., In this paper , we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs ., We predicted an initial set of 13197 potential DDIs based on substrates and inhibitors of cytochrome P450 ( CYP ) metabolism enzymes identified from published in vitro pharmacology experiments ., Using a clinical repository of over 800 , 000 patients , we narrowed this theoretical set of DDIs to 3670 drug pairs actually taken by patients ., Finally , we sought to identify novel combinations that synergistically increased the risk of myopathy ., Five pairs were identified with their p-values less than 1E-06: loratadine and simvastatin ( relative risk or RR\u200a=\u200a1 . 69 ) ; loratadine and alprazolam ( RR\u200a=\u200a1 . 86 ) ; loratadine and duloxetine ( RR\u200a=\u200a1 . 94 ) ; loratadine and ropinirole ( RR\u200a=\u200a3 . 21 ) ; and promethazine and tegaserod ( RR\u200a=\u200a3 . 00 ) ., When taken together , each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone ., Based on additional literature data on in vitro drug metabolism and inhibition potency , loratadine and simvastatin and tegaserod and promethazine were predicted to have a strong DDI through the CYP3A4 and CYP2D6 enzymes , respectively ., This new translational biomedical informatics approach supports not only detection of new clinically significant DDI signals , but also evaluation of their potential molecular mechanisms .

Drug-drug interactions are a common cause of adverse drug events ., In this paper , we developed an automated search algorithm which can predict new drug interactions based on published literature ., Using a large electronic medical record database , we then analyzed the correlation between concurrent use of these potentially interacting drugs and the incidence of myopathy as an adverse drug event ., Myopathy comprises a range of musculoskeletal conditions including muscle pain , weakness , and tissue breakdown ( rhabdomyolysis ) ., Our statistical analysis identified 5 drug interaction pairs: ( loratadine , simvastatin ) , ( loratadine , alprazolam ) , ( loratadine , duloxetine ) , ( loratadine , ropinirole ) , and ( promethazine , tegaserod ) ., When taken together , each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone ., Further investigation suggests that two major drug metabolism proteins , CYP2D6 and CYP3A4 , are involved with these five drug pairs interactions ., Overall , our method is robust in that it can incorporate all published literature , all FDA approved drugs , and very large clinical datasets to generate predictions of clinically significant interactions ., The interactions can then be further validated in future cell-based experiments and/or clinical studies .

medicine, clinical pharmacology, pharmacoepidemiology, drugs and devices, statistics, text mining, drug information, mathematics, pharmacology, biostatistics, information technology, drug metabolism, adverse reactions, pharmacokinetics, computer science, natural language processing, drug interactions, statistical methods

journal.pcbi.1002045

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

Spatial cognition requires long-term neural representations of the spatiotemporal, properties of the environment 1 ., These representations are encoded in terms of multimodal, descriptions of the animal-environment interaction during active exploration ., Exploiting these contextual representations ( e . g . through reward-based learning ) can, produce goal-oriented behavior under different environmental conditions and across, subsequent visits to the environment ., The complexity of the learned neural, representations has to be adapted to the complexity of the spatial task and ,, consequently , to the flexibility of the navigation strategies used to solve it 2 , 3 ., Spatial, navigation planning —defined here as the ability to mentally, evaluate alternative sequences of actions to infer optimal trajectories to a, goal— is among the most flexible navigation strategies 3 ., It can enable animals to solve, hidden-goal tasks even in the presence of dynamically blocked pathways ( e . g ., detour navigation tasks , 4 ) ., Experimental and theoretical, works have identified three main types of representations suitable for spatial, navigation planning , namely route-based , topological , and metrical maps 2 , 3 , 5–7 ., Route-based, representations encode sequences of place-action-place associations independently, from each other , which does not guarantee optimal goal-oriented behavior ( e . g . in, terms of capability of either finding the shortest pathway or solving detour tasks ) ., Topological maps merge routes into a common goal-independent representation that can, be understood as a graph whose nodes and edges encode spatial locations and their, connectivity relations , respectively 2 ., Topological maps provide, compact representations that can generate coarse spatial codes suitable to support, navigation planning in complex environments ., Metrics-based maps go beyond pure, topology in the sense they embed the metrical relations between environmental places, and/or cues —i . e . their distances and angles— within an allocentric, ( i . e . world centered ) reference frame 5 ., Here , we model a spatial memory, system that primarily learns topological maps ., In addition , the resultant, representation also encodes directional-related information , allowing some, geometrical regularities of the environment to be captured ., The encoding of metric, information favors the computation of novel pathways ( e . g . shortcuts ) even through, unvisited regions of the environment ., In contrast to the qualitative but operational, space code provided by topological maps , metrical representations form more precise, descriptions of the environment that are available only at specific locations until, the environment has been extensively explored 5 ., However , purely metric, representations are prone to errors affecting distance and angle estimations ( e . g ., path integration 8 ) ., Behavioral and neurophysiological data suggest the, coexistence of multiple memory systems that , by being instrumental in the encoding, of routes , topological maps and metrical information , cooperate to subserve, goal-oriented navigation planning 9 ., An important question is how these representations can be encoded by neural, populations within the brain ., Similar to other high-level functions , spatial, cognition involves parallel information processing mediated by a network of brain, structures that interact to promote effective spatial behavior 3 , 9–11 ., An extensive body of, experimental work has investigated the neural bases of spatial cognition , and a, significant amount of evidence points towards a prominent role of the hippocampal, formation 12 ., This limbic region has been thought to mediate spatial, learning functions ever since location-selective neurons —namely hippocampal, place cells, 1 , and, entorhinal grid cells, 13— and, orientation-selective neurons —namely head-direction cells, 14— were, observed by means of electrophysiological recordings from freely moving rats ., Yet ,, the role of the hippocampal formation in goal representation and reward-dependent, navigation planning remains unclear 15 ., On the one hand , the hippocampus has been proposed to, encode topological-like representations suitable for action sequence learning 16 ( see 15 for a review of, models ) ., This hypothesis mainly relies on the recurrent dynamics generated by the, CA3 collaterals of the hippocampus 17 ., On the other hand , the hippocampal space code is likely, to be highly redundant and distributed 18 , which does not seem adequate, for learning compact topological representations of high-dimensional spatial, contexts ., Also , the experimental evidence for high-level spatial representations, mediated by a network of neocortical areas ( e . g . the posterior parietal cortex 19 and the, prefrontal cortex 20 ) suggests the existence of an extra-hippocampal action, planning system shared among multiple brain regions 21 , 22 ., The model presented here, relies on the hypothesis of a distributed spatial cognition system in which the, hippocampal formation would contribute to navigation planning by conveying redundant, spatial representations to higher associative areas , and a cortical network would, elaborate more compact representations of the spatial context —accounting for, motivation-dependent memories , action cost/risk constraints , and temporal sequences, of goal-directed behavioral responses 23 ., Among the cortical areas involved in map building and action planning , the prefrontal, cortex ( PFC ) is likely to play a central role , as suggested by anatomical PFC lesion, studies showing impaired navigation planning in rats 24 , 25 and neuroimaging studies 26 , 27 ., Also , the, anatomo-functional properties of the PFC seem appropriate to encode multimodal, contextual memories that are not merely based on spatial correlates ., The PFC, receives direct projections from sub-cortical structures ( e . g . the hippocampus 28 , the thalamus, 29 , the, amygdala 30 and, the ventral tegmental area 31 ) , and indirect connections from the basal ganglia through, the basal ganglia - thalamocortical loops 32 ., These projections convey, multidimensional information onto the PFC , including ( but not limited to ) emotional, and motivational inputs 33 , reward-dependent modulation 34 , and action-related signals, 32 ., The PFC, seems then well suited to, ( i ) process manifold spatial information, 35 ,, ( ii ) encode the motivational values associated to, spatiotemporal events 15 , and, ( iii ) perform supra-modal decision, making 36 , 37 ., Also , the PFC, may be involved in integrating events in the temporal domain at multiple time scales, 38 ., Indeed ,, its recurrent dynamics , regulated by the modulatory action of dopaminergic, afferents , may maintain patterns of activity over long time scales 39 ., Finally , the, PFC is likely to be critical to detecting cross-temporal contingencies , which is, relevant to the temporal organization of behavioral responses , and to the encoding, of retrospective and prospective memories 38 ., This article presents a neurocomputational model of the PFC columnar organization, 40 and, focuses on its possible role in spatial navigation planning ., The cortical column, model generates compact topological maps from afferent redundant spatial, representations encoded by the hippocampal place cell activity patterns as modeled, by Sheynikhovich et al . 41 ., The model exploits the multimodal coding property, offered by the possibility to refine the cortical architecture by adding a sublevel, to the column , i . e . the minicolumn ., It also exploits the recurrent nature of the, columnar organization to learn multilevel topological maps accounting for structural, regularities of the environment ( such as maze alleys and arms ) ., It shows how, specific connectivity principles regulated by unsupervised Hebbian mechanisms for, synaptic adaptation can mediate the learning of topological neural representations, in the PFC ., Then , the model uses the underlying topological maps to plan, goal-directed pathways through a neural implementation of a simple breadth-first, graph search mechanism called activation diffusion or spreading activation 42–44 ., The, activation diffusion process is based on the propagation of a reward-dependent, signal from the goal state through the entire topological network ., This propagation, process enables the system to generate action sequences ( i . e . trajectories ) from the, current position towards the goal ., We show how the modeled anatomo-functional, interaction between the hippocampal formation and the prefrontal cortex can enable, simulated rats to learn detour navigation tasks such as Tolman & Honziks, task 4 ., The, model presented here aims at shedding some light on the link between single-cell, activity and behavioral responses ., We perform a set of statistical and information, theoretical analyses to characterize the encoding properties of hippocampal and PFC, neuronal activity —in terms of both main correlates ( e . g . location ,, distance-to-goal , and prospective coding ) and functional time course changes ., We, interpret and validate the results of these analyses against available experimental, data ( e . g . extracellular electrophysiological recordings of PFC units ) ., Cortical maps consist of local circuits —i . e . the cortical columns 40—, that share common features in sensory , motor and associative areas , and thus, reflect the modular nature of cortical organization and function 45 ., Cortical columns can be divided in six main layers including: layer I , which, mostly contains axons and dendrites; layers II-III , called, supragranular layers , which are specialized in, cortico-cortical connections to both adjacent and distant cortical zones; layer, IV , which receives sensory inputs from sub-cortical structures ( mainly the, thalamus ) or from columns of cortical areas involved in earlier stages of, sensory processing; and layers V–VI , called infragranular, layers , which send outputs to sub-cortical brain areas ( e . g . to the striatum and, the thalamus ) regulating the ascending information flow through feedback, connections ., According to the cytoarchitectonic properties of the rat medial PFC, 32 , no, layer IV is considered in the model of cortical column described henceforth ., Neuroanatomical findings ( see 45 for a review; see, 46 ,, 47 for, anatomical data on rat PFC ) suggest that columns can be further divided into, several minicolumns , each of which consists of a population of, interconnected neurons 48 ., Thus , a column can be seen as an ensemble of, interrelated minicolumns receiving inputs from cortical and sub-cortical areas ., It processes these afferent signals and projects the responses both within and, outside the cortical network ., This twofold columnar organization has been, suggested to subserve efficient computation and information processing 45 , 49 ., Several, models have been proposed to study the cortical columnar architecture , from, early theories on cortical organization 50–52 to recent, computational approaches ( e . g . the blue brain project 53 ) ., These models either, provide a detailed description of the intrinsic organization of the column in, relation to cytological properties and cell differentiation or focus on purely, functional aspects of columnar operations ., The approach presented here attempts to relate the columnar organization to, decision making and behavioral responses using a highly simplified neural, architecture which does not account for cell diversity and biophysical, properties of PFC neurons ., Fig ., 1A shows an overview of the model architecture based on this notion, of cortical column organization ., As aforementioned , the underlying hypothesis is, that the PFC network may mediate a sparsification of the hippocampal place, ( ) representation to encode topological maps and subserve, goal-directed action planning ., The model exploits the anatomical excitatory, projections from hippocampus to PFC 28 to convey the redundant, state-space representation, to the columnar PFC network , where a sparse state-action, code is learned ., Within a column , each minicolumn becomes, selective to a specific state-action pair , with actions, representing allocentric motion directions to perform, transitions between two states ., Each column is, thus composed by a population of minicolumns that represent all the state-action, pairs experienced by the animal at a location, ., This architecture is consistent with data showing that, minicolumns inside a column have similar selectivity properties 54 and that some, PFC units encode purely cue information while others respond to cue-response, associations 55 ., The model employs the excitatory collaterals between minicolumns 45 , 56 to learn, multilevel topological representations ., Egocentric self-motion information, ( provided by proprioceptive inputs ) biases the selectivity properties of a, subpopulation of columns to capture morphological regularities of the, environment ., Unsupervised learning also modulates the recurrent projections, between minicolumns to form forward and reverse associations between states ., During planning , the spreading of a reward signal from the column selective for, the goal through the entire network mediates the retrieval of goal-directed, pathways ., Then , a local competition between minicolumns allows the most, appropriate goal-directed action to be inferred ., The following sections provide a functional description of the model columnar, structure , connectivity and input-output functional properties ., A more, comprehensive account –including equations , parameter settings and, explanatory figures– can be found in Supplementary Text, S1 ., We demonstrate the ability of the model to learn topological representations and, plan goal-oriented trajectories by considering a navigation task: the Tolman, & Honziks detour task ., The behavioral responses of, simulated rats are constraint by intersecting alleys , which , in contrast to open, field mazes , generate clear decision points and permit dynamic blocking of, goal-directed pathways ., We first examined the behavioral responses of simulated animals, solving the 1∶1 version of Tolman & Honziks task ( see Sec ., sec:tolmantask and Fig . 2, for details on the experimental apparatus and protocol ) ., The qualitative and, quantitative results shown on Figs ., 3A and B , respectively , demonstrate that the model reproduced the, behavioral observations originally reported by Tolman & Honzik 4 ., During the first 12 training trials ( Day 1 ) the simulated, animals learned the topology of the maze and planned their navigation, trajectories in the absence of blocks A and B . Similar to Tolman &, Honziks findings , the model selected the shortest pathway P1 significantly, more than alternative paths P2 and P3 ( ANOVA , ; Figs . 3A , B left column ) ., During the following 156 training trials ( Days 2–14 ) , a, block at location A forced the animals to update their topological maps, dynamically , and plan a detour to the goal ., The results reported by Tolman &, Honzik provided strong evidence for a preference for the shortest, detour path P2 ., Consistently , we observed a significantly, larger number of transits through P2 compared to P3 ( ANOVA ,, ; Figs ., 3A , B central column ) ., The simulated protocol included 7 probe trials ( Day 15 ) during, which the block A was removed whereas a block at location B was added ., This, manipulation aimed at testing the “insight” working hypothesis:, after a first run through the shortest path P1 and after having encountered the, unexpected block B , will animals try P2 ( wrong behavior ) or will they go, directly through P3 ( correct behavior ) ?, In agreement with Tolman &, Honziks findings , simulated animals behaved as predicted by the insight, hypothesis , i . e . they tended to select the longer but effective P3 significantly, more often than P2 ( ANOVA , ; see Figs . 3A , B , right column ) ., The, patterns of path selection during this task is explained by the ability of the, model to choose shortest paths ., When a block is added into the environment , the, goal propagation signal is also blocked at the level of the column network , and, hence the simulated animals choose the shortest unblocked, pathways ., We then tested the robustness of the above behavioral results with respect to the, size of the environment ., We considered a 4∶1 scaled version of Tolman, & Honziks maze and we compared the performances of, simulated animals with intact, populations ( “control” group ) against those, of simulated animals lacking the, cortical population ( “no, ” group ) ., The latter group did not have the, multilevel encoding property provided by the – recurrent dynamics, ( see Sec . Recurrent cortical processing for multilevel topological mapping ) ., Fig . 3C compares the, average path selection responses of the two simulated groups across the, different phases of the protocol ., During Day 1 ( i . e . no blocks, in the maze ) both groups selected the shortest path P1 significantly more often, ( ANOVA , ; Fig ., 3C left ) ., However , the action selection policy of subjects without, began to suffer from mistakes due to the enlarged, environment , as suggested by lower median value corresponding to P1 ., During, Days 2–14 ( with block A ) , the group without, did not succeed in solving the detour, task , because no significant preference was observed between P2 ( shortest, pathway ) and P3 ( ANOVA , ; Fig . 3C center ) ., By contrast ,, control animals coped with the larger environmental size successfully ( i . e . P2, was selected significantly more often than P3 , ANOVA ,, ) ., During the probe trials of Day 15, ( with a block at B but not at A ) , the group without, was impaired in discriminating between P2 and P3 ( ANOVA ,, ; Fig ., 3C right ) , whereas control subjects behaved accordingly to the, insight hypothesis ( i . e . they selected the longer but effective P3 significantly, more than P2; ANOVA , ) ., The better, performances of control subjects were due to the fact that back-propagating the, goal signal through the cortical network benefited from the higher-level, representation encoded by the population and, from the - interaction during, planning ( see Supplementary Text S1 Sec ., Exploiting the topological, representation for navigation planning , Fig . S2 ) ., Indeed , an intact, population allowed the goal signal to decay with a, slower rate compared to , due to the, smaller number of intermediate columns in ( i . e . planning, could benefit from a more compact topological representation ) ., Henceforth we demonstrate how the modeled neural processes can be interpreted as, elements of a functional network mediating spatial learning and decision making ., We show that the neural activity patterns of all types of neurons in the, cortical model are biologically plausible in the light of PFC, electrophysiological data 20 , 35 , 59–66 ., We studied to what extent the neural populations of the model ( i . e ., , ,, , and, neurons ) could be quantitatively segregated on the basis, of a set of statistical measures ., We then compared the results to those obtained, by applying the same clustering analysis to a population of neurons recorded, from the medial PFC of navigating rats ( see Materials and Methods Sec ., Statistical analysis of neural, activities ) ., We first gathered all non-silent simulated neurons recorded during the 4∶1, version of Tolman & Honziks task ., All types of units ( i . e ., , ,, , ,, ) were pulled together in a data set ., We characterized, each neurons discharge by measuring its mean firing rate , standard, deviation , skewness , lifetime kurtosis , spatial information per spike and, spatial mutual information ( see Supplementary Text S2 ) ., Then , we performed a principal component analysis ( PCA ) on the multidimensional, space containing the values provided by these measures per each neuron ( see, Figs ., S4 A , C for details ) ., Fig . 9A shows the resulting data distribution in the space defined, by the first three principal components ., Interestingly , model neurons with, different functional roles tended to occupy distinct regions of the PCA space ., For instance , neurons , whose function in, the model is to propagate goal information and code for the distance to the, goal , were located within the same portion of the PCA space ( blue and cyan, crosses and circles ) ., All neurons , which primarily, code for spatial locations , were also clustered within the PCA space ( red, crosses ) ., Neurons ( and also, ) , responsible for forward signal propagation and local, decision making , respectively , were aggregated within the same region ( gray and, black crosses , and black circles ) ., Finally , neurons, , mainly involved in high-level mapping and navigation, planning , were also separated from other units in PCA space ( gray and red, circles ) ., Figs ., 9B , C , D display the mean, values , averaged over each population of the model , of, three statistical measures ( out of six ) used to perform the PCA ., These diagrams, can help understanding the data point distribution of Fig . 9A ., When considering the mean spatial, information per spike ( Fig ., 9B ) , at least three groups could be observed: neurons whose activity, had nearly no spatial correlate ( ) , neurons, conveying intermediate amounts of spatial information, ( and ) , and neurons with, larger spatial information values ( ) ., The mean firing, rate parameter ( Fig . 9C ), allowed two distinct groups to be clearly identified: one with low average, firing ( neurons ) , and one with, high firing rates ( neurons ) ., Together with, Fig . 9A , this diagram, can help understanding why neurons , which had almost, no spatial correlate and very high firing rates compared to other populations of, the model , were well segregated within the same region of the PCA space ( Fig ., 9A , blue and cyan crosses, and circles ) ., Finally , when comparing the mean skewness values of all neural, populations ( Fig . 9D ) ,, neurons and were pulled apart ,, according to their distribution in the PCA space ( Fig ., 9A , gray and black crosses , and black, circles ) ., As a control analysis , we extended the data set used for the PCA by, adding a population of neurons with random Poisson activities ., As shown in, supplementary Figs ., S5 A–B , the population of Poisson neurons ( light green, data points ) was well separated from all model neurons within the space defined, by the first three principal components , suggesting that the variability of, model discharge properties could not be merely explained by a random, Poisson-like process ., We then applied an unsupervised clustering algorithm ( k-means clustering method, with ) to partition the distribution of data points of Fig . 9A , based on the, discharge characteristics of model neurons ., This blind clustering analysis ( i . e ., without any a priori knowledge on neural populations ) allowed us to identify, three main groups ( Fig ., 10A ) ., The first cluster ( blue data points ) corresponded to non-spatial ,, reward-related neuronal activities ( i . e . neurons ) ., The second, cluster ( green points ) represented location-selective activity ( mainly from, neurons , but also including some neurons, ) ., The third cluster ( red data points ) corresponded to, location-selective activity of neurons in the cortical network, ( i . e . mainly ) ., See, supplementary Fig . S6 for details on the composition of the three identified, clusters ., We performed the same series of analyses on a dataset of medial PFC neurons, recorded from navigating rats ( see Materials and, Methods , Sec ., Statistical analysis of neural activities ) ., We, characterized every recorded activity according to the same set of statistical, measures used for model neurons ( i . e . mean firing rate , standard deviation ,, skewness , lifetime kurtosis , spatial information per spike and spatial mutual, information , see Supplementary Text S2 ) ., Then , we applied a PCA on the, resulting high dimensional space containing , per each neuron , the resulting, values of these measures ( see Figs . S4 B , D for details ) ., Finally , we used, the same unsupervised k-mean clustering algorithm to partition the data, distribution in the space defined by the first three principal components ., As, for simulated data , the clustering method identified three main classes ( Fig ., 10B; with red , green , and, blue data points corresponding to three subsets of electrophysiologically, recorded activities in the PFC ) ., We then compared model and experimental, clusters ( Figs . 10C , D , E ) in, order to investigate whether real and simulated data points belonging to the, same clusters shared some discharge characteristics ., In terms of mean spatial, information ( Fig . 10C ) , we, found similar non-homogeneous distributions between model and real clusters ., Both red clusters encoded the largest spatial information content ., Recall that, the model red cluster mainly contained activities from location-selective, neurons ( as quantified in supplementary Fig . S6 B ) ., When looking at mean firing rates averaged over each cluster ( Fig . 10D ) , we found that both, real and simulated activities within the blue clusters had significantly larger, frequencies than others ., The model blue cluster was mainly composed by neurons, propagating reward-related information ., Finally , when, comparing the mean absolute values of the skewness of receptive fields ( Fig . 10E ) , we found both model, and experimental populations with asymmetric fields ( i . e . non-zero skewness ) ., Model-wise , the red and green clusters ( containing neurons, , Fig . S6 B ) had the largest mean skewness ., Similarly , experimental red and green subpopulations had larger skewness values, than the blue population ., As a control analysis , we computed the three mentioned, measures ( i . e . information per spike , mean firing rate and skewness of the, receptive field ) for two populations of neurons with random Uniform and Poisson, activities ., As shown in supplementary Fig . S7 , unlike model data , the two, populations of random neurons could not explain the experimental data in terms, of information content and skewness of the receptive field ., Taken together ,, these results indicated that , within the data set of experimental PFC, recordings , subpopulations of neurons existed with distinct discharge, properties , and that these subpopulations might be related to distinct, functional groups predicted by the model ., Our model is based upon three main assumptions ., First , the model relies on the, columnar organization of the cortex ., Although this concept is supported by many, experimental studies 45 , 48 , no clear general function for columns has emerged to, explain their role in cortical processing 69 ., In addition , Rakic 70 stressed, that the size , cell composition , synaptic organization , expression of signaling, molecules , and function of various types of columns are dramatically different, across the cortex , so that the general concept of column should be employed, carefully ., In our model , we call “column” a local micro-circuit, composed by neurons processing common spatial information , and we propose that, this columnar organization may be a substrate suitable to implement a, topological representation of the environment ., Second , our planning model relies, on an activation diffusion mechanism ., At the neural level , this suggests that, strong propagation of action potentials should occur in the neocortex ., This is, not a strong assumption , since several studies have demonstrated such phenomena, as propagating waves of activity in the brain 71 , 72 ., For example , Rubino et al ., 73, suggested that oscillations propagate as waves across the surface of the motor, cortex , carrying relevant information during movement preparation and execution ., Third , the multiscale representation is based on a putative, signal ., There are several potential candidates for its, implementation in the brain ., One of these candidates is habit learning involving, the striatum 74 , 75 ., Indeed , if for instance the rat always turns left at, a particular location it may acquire a corresponding habit ., The neural activity, corresponding to this stimulus-response association may serve as the, signal ., In this case , the time scale of learning in the, population should correspond to the time scale of habit, acquisition ( potentially many trials , see e . g . 75 ) ., Topological map learning and path planning have been extensively studied in, biomimetic models ( see 6 for a general review; see also 76 for theoretical, discussions on hierarchical cognitive maps ) ., These models aimed at either, providing more efficient path planning algorithms or , like our work ,, establishing relations between anatomical substrates , electrophysiology and, behavior ., In particular , several studies took inspiration from the anatomical, organization of the cortex and used the activation diffusion mechanism to, implement planning ., Burnod 49 proposed one of the first models of the cortical, column architecture , called “cortical automaton” ., He also described, a “call tree” process that can be seen as a neuromimetic, implementation of the activation diffusion principle ., Some subsequent studies, employed the cortical automaton concept 77 , 78 , while others used, either connectionist architectures 16 , 79–84 or Markov, decision processes 85 ., Our approach is similar to that of Hasselmo 44 , who also, addressed goal-directed behavior by modeling the PFC columnar structure ., Both, Hasselmos and our model architectures employ minicolumns as basic, computational units to represent locations and actions , to propagate, reward-dependent signals , and mediate decision making ., Yet , the two models, differ in the encoding principles underlying the learned representations , which, generate , consequently , distinct behavioral responses ., The connectivity layout, of Hasselmos model allows state-response-state chains to be encoded , with, each minicolumn representing either a state or an action ., In our model , a state, and its related actions are jointly encoded by a set of minicolumns within a, column ., Similar to Koene and Hasselmo 44 , 86 , we compared the discharge, of simulated PFC units against experimental recordings exhibiting place- ,, action- and reward-related correlates ., As explained henceforth , we focused, further on the functional relationship between the hippocampus and the PFC in, encoding complementary aspects of spatial memory , with a quantitative approach, based on the analysis of statistical properties and information content of the, neural codes ., We also put the emphasis on the time course analysis of neural, responses mediating place coding vs . decision making ., The successful performance of our model in large environments relies on its, ability to build a multiscale environment representation ., This

Introduction, Materials and Methods, Results, Discussion

The interplay between hippocampus and prefrontal cortex ( PFC ) is fundamental to, spatial cognition ., Complementing hippocampal place coding , prefrontal, representations provide more abstract and hierarchically organized memories, suitable for decision making ., We model a prefrontal network mediating, distributed information processing for spatial learning and action planning ., Specific connectivity and synaptic adaptation principles shape the recurrent, dynamics of the network arranged in cortical minicolumns ., We show how the PFC, columnar organization is suitable for learning sparse topological-metrical, representations from redundant hippocampal inputs ., The recurrent nature of the, network supports multilevel spatial processing , allowing structural features of, the environment to be encoded ., An activation diffusion mechanism spreads the, neural activity through the column population leading to trajectory planning ., The model provides a functional framework for interpreting the activity of PFC, neurons recorded during navigation tasks ., We illustrate the link from single, unit activity to behavioral responses ., The results suggest plausible neural, mechanisms subserving the cognitive “insight” capability originally, attributed to rodents by Tolman & Honzik ., Our time course analysis of neural, responses shows how the interaction between hippocampus and PFC can yield the, encoding of manifold information pertinent to spatial planning , including, prospective coding and distance-to-goal correlates .

We study spatial cognition , a high-level brain function based upon the ability to, elaborate mental representations of the environment supporting goal-oriented, navigation ., Spatial cognition involves parallel information processing across a, distributed network of interrelated brain regions ., Depending on the complexity, of the spatial navigation task , different neural circuits may be primarily, involved , corresponding to different behavioral strategies ., Navigation planning ,, one of the most flexible strategies , is based on the ability to prospectively, evaluate alternative sequences of actions in order to infer optimal trajectories, to a goal ., The hippocampal formation and the prefrontal cortex are two neural, substrates likely involved in navigation planning ., We adopt a computational, modeling approach to show how the interactions between these two brain areas may, lead to learning of topological representations suitable to mediate action, planning ., Our model suggests plausible neural mechanisms subserving the, cognitive spatial capabilities attributed to rodents ., We provide a functional, framework for interpreting the activity of prefrontal and hippocampal neurons, recorded during navigation tasks ., Akin to integrative neuroscience approaches ,, we illustrate the link from single unit activity to behavioral responses while, solving spatial learning tasks .

circuit models, cognitive neuroscience, cognition, computational neuroscience, decision making, neural networks, biology, computational biology, neuroscience, learning and memory

journal.pcbi.1000507

Human miRNA Precursors with Box H/ACA snoRNA Features

Small nucleolar RNAs ( snoRNAs ) and microRNAs ( miRNAs ) are two classes of abundant non-coding regulatory RNAs that carry out fundamental cellular activities but that have only been comprehensively investigated in recent years ., SnoRNAs are small RNA molecules of approximately 60–300 nucleotides in length which generally serve as guides for the catalytic modification of selected ribosomal RNA nucleotides 1 , 2 ., SnoRNAs associate with specific proteins , which are conserved amongst all eukaryotes , to form small nucleolar ribonucleoparticles ( snoRNPs ) ., Two main groups of snoRNAs have been described ., The box C/D snoRNAs , which bind the four conserved core box C/D snoRNP proteins fibrillarin , NOP56 , NOP5/NOP58 and NHP2L1 , are involved in 2′-O-ribose methylation ., The box H/ACA snoRNAs , which bind the four conserved core box H/ACA snoRNP proteins DKC1 ( dyskerin ) , GAR1 , NHP2 and NOP10 , catalyse pseudouridylation ., In vertebrates , most snoRNAs have been shown to reside in introns of protein coding host genes and are processed out of the excised introns 3 ., However , two box C/D snoRNAs have recently been found to be transcribed from independent RNA pol II units 4 ., MiRNAs are ∼18–24 nucleotide-long RNAs that are processed out of ∼70 nucleotide-long hairpin structures ( called pre-miRNAs ) 5 ., In mammals , miRNAs have been shown to be involved mainly in mRNA translation inhibition 6 although recently , they have also been reported to activate translation 7 ., A large class of miRNAs are encoded in introns of protein-coding genes and are co-expressed with these host genes 8–10 ., The remaining miRNAs are encoded in independent transcription units ., Some of these miRNAs have been shown to be under the control of the RNA polymerase II 11 while others are transcribed by the RNA polymerase III 12 ., Many members of the snoRNA and miRNA classes are well conserved throughout evolution 1 , 2 , 13 ., Correspondence between several yeast and human snoRNAs and their target sites have been established and many snoRNAs have a very high sequence identity within mammals as shown in the snoRNAbase database 14 ., In the case of miRNAs , several families have been found to be well conserved in metazoans 13 , 15 ., However , recent reports also suggest the existence of species- and lineage-specific snoRNAs and miRNAs 13 , 16 , 17 ., These and other reports on their origin and evolution are providing clues about the emergence of large groups of these recently evolved molecules ., Through bioinformatic searches , Weber 17 and Luo and Li 16 identified hundreds of human snoRNAs and snoRNA-related molecules that are derived from transposable elements ( TEs ) , thus confirming the widespread nature of this phenomenon , initially described for a small number of snoRNAs 2 , 18 ., These analyses suggest that many snoRNAs result from the retroposition of existing snoRNAs that used long interspersed nuclear element ( LINE ) machinery to transpose themselves to new genomic locations ., Many of these snoRNA-related molecules are surrounded by the presence of sequence features typical of retrogenes such as target site duplications ( TSDs ) and poly ( A ) tails at their 3′ end ., These snoRNA retroposition events generated hundreds of sno-related molecules , termed snoRTs ( snoRNA retroposons ) by Weber 17 , many of which had never been previously identified , but some of which were previously described as functional snoRNAs 16 ., SnoRNA retroposition thus not only permits maintenance of a pool of intact snoRNA copies to safeguard against the effects of deleterious mutations but could possibly also allow for the creation of regulatory RNA molecules that might bind new targets 17 ., Given the stringent thresholds used to search for snoRNA copies in both studies , it is likely that many more such molecules exist in the human genome but might have diverged further from their parental copies and are yet to be discovered ., Recent reports have also described some miRNAs as being derived from TEs , suggesting a possible mechanism for the rapid generation of miRNAs and their corresponding target sites ., In the first such report , Smalheiser and Torvik identified six miRNAs that are derived from TEs 19 ., Two subsequent studies identified a further 95 12 and 55 20 , 21 known miRNAs that might be derived from TEs as well as an additional 85 predicted novel TE-derived miRNA genes 20 ., The TEs that are most frequently found in association with miRNAs are the L2 and MIR families 20 ., As TEs are the most non-conserved sequence elements in eukaryotic genomes 22 , the generation of miRNAs through TEs represents a mechanism that could be a driving force in speciation events and evolution by rapidly creating new regulatory elements in the control of protein production 19 , 20 ., A recent report investigating the small RNAs present in human cells has demonstrated the existence of specific small RNA fragments derived from larger known non-coding RNA molecules 23 ., In particular , distinct small fragments of sizes between 23 and 25 nucleotides were found to map to four box H/ACA snoRNAs 23 ( listed in Table 1 ) ., In addition to this , Ender and colleagues have recently reported eight box H/ACA snoRNA-derived miRNA-like molecules that can be immunoprecipitated with Ago proteins 24 ., While these short H/ACA snoRNA-derived fragments might be discounted merely as non-functional degradation products , several unrelated observations suggest otherwise ., Firstly , only specific fragments derived from one region of each snoRNA were identified , rather than a ladder of fragments consistent with degradation ., Secondly , other snoRNAs encode smaller fragments that are stably produced ., Indeed , three miRNAs present in the miRNA repository miRBase 25 can be shown to be encoded in known H/ACA snoRNAs ( listed in Table 1 ) ., Although at least one pair of these miRNAs and snoRNAs are known to be co-localised in the genome as mentioned in miRBase 25 , it is not known whether the processing of these molecules is independent or dependent and sequential ., Thirdly , as mentioned above , miRNA and snoRNA members have both been found to be TE-derived , suggesting a similar origin and evolution for at least some members of these small non-coding RNA classes ., Here , in light of the accumulation of data suggesting a connection between box H/ACA snoRNAs and miRNA-like molecules , we investigate the possibility of an evolutionary relationship between members of these classes of RNA ., A comparison between the genomic positions of reported miRNA genes from miRbase 25 and box H/ACA snoRNAs reveals three occurrences of overlap between these RNA species ( Table 1 , top section ) ., In all three cases , between 75% ( mir-1291 ) and 97% ( mir-1248 ) of the miRNA hairpin is contained within the snoRNA ( using the coordinates of the UCSC Genome Browser as described in the Methods ) ., Moreover , in all three cases , greater than 90% of the mature miRNA as defined in miRbase release 11 . 0 25 is contained within the snoRNA ., In addition to these known miRNAs encoded in box H/ACA snoRNAs , ten small fragments matching exactly to portions of eleven box H/ACA snoRNAs have been detected 23 , 24 and are listed in Table 1 ( bottom section ) ., One of the fragments is identical to two very similar H/ACA snoRNAs , ACA7 and ACA7B ., Of these ten small fragments , seven have been shown to be bound by Ago proteins and one of these , ACA45 sRNA , has experimentally validated targets 24 ., Apart from HBI-100 , all box H/ACA snoRNAs from Table 1 that contain experimentally detected smaller fragments are either experimentally verified snoRNAs or close paralogues of such experimentally validated snoRNAs ., ACA34 , ACA45 , ACA47 , ACA56 , ACA3 , ACA50 , ACA7 , HBI-61 , U17b , U71a and U92 have been shown experimentally to display characteristics of H/ACA snoRNAs 26–31 ., ACA36B is a close paralogue ( 88% identity ) of the experimentally validated H/ACA snoRNA ACA36 28 ., ACA7B is a close paralogue of ACA7 ( 98% identical ) ., ACA7B and ACA36B share both their predicted rRNA targets with ACA7 and ACA36 respectively , as described in the snoRNAbase 14 ., In addition , as shown in Table 1 , seven of the box H/ACA snoRNAs that encode smaller experimentally detected fragments share their predicted rRNA and snRNA targets with other box H/ACA snoRNAs and one ( U17b ) does not have a known target ., The remaining six box H/ACA snoRNAs , HBI-61 , ACA45 , ACA47 , ACA56 , ACA3 and U92 , are predicted to guide the pseudouridylation of known modified residues 14 and no other snoRNA is known to serve as a guide for these residues ., The UCSC Genome Browser mammalian conservation track shows that for all snoRNAs listed in Table 1 except U71a and ACA56 , the conserved region around these molecules covers the entire snoRNA molecules , not only the miRNA hairpins or small RNA fragments detected ( Figure 1 and Figure S1 ) ., The miRNA hairpins of miR-664 and miR-1291 have short 3′ and 5′ regions respectively that do not overlap with a snoRNA ., These regions correspond to the least well conserved regions of the whole miRNA/snoRNA molecules ., This suggests that these regions originally encoded snoRNAs and not necessarily miRNAs in the most recent common ancestor ., Indeed , to our knowledge , apart from mir-664 and ACA45 sRNA , none of the other miRNAs and smaller fragments have been detected in other mammalian species , suggesting the capability of generating smaller RNA molecules from these snoRNAs might be a recent event ., The box H/ACA snoRNA/miRNA relationship described above was further investigated by studying all known miRNAs to determine whether they might be encoded within genomic regions predicted to harbour H/ACA snoRNAs ., Indeed , if some miRNAs have evolved from H/ACA snoRNA encoding regions , they might still display snoRNA features ., The mammalian version of the snoGPS program predicts pseudouridylation guides in human , mouse and rat genomes by scoring weakly conserved primary and secondary structure motifs using a deterministic search algorithm 32 ., The mammalian version of the snoGPS program also includes a cross-species implementation ( snoGPS-C ) which takes account of conservation between several mammalian genomes to predict box H/ACA snoRNAs 32 ., A locally-installed copy of the mammalian snoGPS program was used to scan with the two-hairpin model for the presence of box H/ACA snoRNAs in 676 distinct sequences consisting of human miRNAs from miRBase ( version 11 . 0 ) 25 and an additional 175 padding nucleotides upstream and downstream ( referred to as the extended miRNA molecules ) ., We did not use the cross-species implementation of snoGPS ( snoGPS-C ) because many of the newly described snoRNAs ( especially the TE-derived snoRNAs ) are lineage- or species-specific 17 ., In order to investigate whether the number of snoGPS predicted hits above a certain threshold was significant , we used snoGPS to scan 100 sets of 676 randomly generated sequences of same length distribution as the miRNAs under study , as described in the Methods section ., The number of hits above a given threshold for both the set of extended miRNA sequences under study and the randomly generated sequences is shown in Figure 2 ., MiRNA precursors , like H/ACA snoRNAs , consist of at least one hairpin ., To control for this , a second set of control sequences consisting of 676 randomly generated hairpins of same length distribution and minimum free-energy distribution ( as calculated by RNAfold 33 ) as the miRNA hairpins under study was generated ., 100 such random hairpin sets were scanned using snoGPS and their average number of hits is shown in Figure 2 ., As expected , the number of hits for the random hairpin groups is significantly higher than the number of hits for the random sequence groups that have not been constrained to form hairpins ., However , for all hit score thresholds investigated , the number of hits predicted by snoGPS for the miRNA group is significantly higher than the number of hits predicted for any of the randomly generated groups ., This suggests that genomic regions around a significant number of miRNAs contain features that very closely resemble box H/ACA snoRNAs ., 148 distinct extended miRNA molecules were predicted to encode at least one hit above a score of 35 . 0 , which is the threshold that is ‘typically’ used when predicting new candidate snoRNAs by snoGPS-C 32 ., Since we chose to use the original snoGPS version , we set the threshold higher , at 40 . 0 , in order to consider only very likely candidates ., Taking this conservative threshold , 29 distinct extended miRNA molecules were predicted to encode at least one hit ., All predicted hits were folded to reveal their predicted secondary structure , using RNAstructure 34 ., When the highest snoGPS hit could not be folded in a secondary structure that was within 10% of the lowest RNAstructure predicted minimum free-energy , snoGPS hits of lower score ( but still above 40 ) were considered ., The best snoGPS hit is defined as the snoGPS hit with highest score that has a predicted secondary structure minimum free-energy within 10% of the lowest predicted minimum free-energy structure for this molecule ., Twenty extended miRNA regions had best snoGPS hits above a score of 40 . 0 and the remaining nine extended miRNA regions with lower best snoGPS hits were not considered further ., Table 2 describes the best hit for each of these twenty extended miRNA molecules ., The position of the predicted H/ACA snoRNA was compared to the position of the miRNA hairpin , taking coordinates downloaded from the UCSC Table Browser as described in the Methods ., Apart from the predicted snoRNAs that contain mir-151 and mir-215 , all predicted snoRNAs in Table 2 contain at least 90% of their encoded miRNA hairpins ., Approximately 80% of the hairpins of both mir-151 and mir-215 are contained in their respective predicted snoRNA ., In addition , for all miRNAs described in Table 2 , at least 90% of the mature miRNA is contained within the predicted snoRNA ., In this respect , all these snoRNA-predicted miRNA pairs are similar to the known H/ACA snoRNAs that encode smaller fragments detected experimentally , described in Table 1 ., SnoGPS predicts guide sequences and corresponding rRNA pseudouridylation sites within snoRNAs ., For all the best snoGPS hits with scores above 40 listed in Table 2 , the predicted pseudouridylation sites are reported ., While most of these predicted pseudouridylation sites are known to be recognised by already reported box H/ACA snoRNAs , four are labelled as having an unknown guide in snoRNAbase 14 ., Indeed , the H/ACA snoRNAs predicted in the extended region around mir-549 , mir-140 , mir-1262 and mir-605 are all predicted to serve as guides for experimentally validated pseudouridylation sites whose guides are unknown , making these interesting candidates for further studies ., Some of these might represent genomic regions with a dual function , serving both to produce miRNAs and snoRNAs ., The miRNAs reported in miRbase have not all been validated to the same extent ., While the mature forms of some of the miRNAs have only been identified with a very small number of sequence reads , others have been identified by larger numbers of reads , display characteristic miRNA signatures ( with detection of a much smaller number of star reads than the mature form reads 24 , 35 ) and have been functionally validated ., For each of the twenty miRNAs described in Table 2 , we include the number of sequence reads and when available , the number of star reads , as reported in the literature ., Ten of the miRNAs in Table 2 have been identified with at least 10 reads and four of these ( miR-151 , miR-885 , miR-140 and miR-520a ) also have corresponding star reads of lower abundance ., On the other hand , three reported miRNAs with snoRNA-like features , miR-549 , miR-548m and miR-605 , have been identified with fewer than 4 reads ., While the best snoGPS hit has been investigated here , it is important to point out that some extended miRNA regions obtain more than one high-scoring hit ., Most notably , mir-548d-1 and mir-548d-2 have high-scoring hits in both their hairpins , in a manner reminiscent of well validated H/ACA snoRNAs such as E2 and U65 ., Box H/ACA snoRNAs have very distinct features ., They usually consist of two hairpins , each of which is followed by short single-stranded regions ( the H and ACA boxes ) ., While the H box is located between the two hairpins , the ACA box is located at the 3′ end of the molecule ., One or both of the hairpins contain bulges , allowing base-pairing with the target RNA , in complex pseudo-knot structures ., In order to better characterise the predicted snoRNAs encoding miRNAs and visualise the position of the mature miRNA within these molecules , all predicted snoRNA sequences were folded using RNAstructure 34 and are shown in Figure 3B and Figure S2 ., In addition , the predicted secondary structure of the four snoRNAs encoding known miRNAs ( from Table, 1 ) are also shown ( Figure 3A ) ., Most of the predicted snoRNAs encoding miRNAs resemble typical snoRNAs with two main hairpins , characteristic boxes and one or two bulges containing the predicted RNA target complementary sites Because numerous snoRNAs and miRNAs have been described as being derived from TEs , all extended miRNA molecules predicted to have box H/ACA snoRNA features surrounding them ( from Table, 2 ) were further investigated for the presence of repeat elements using RepeatMasker ( http://www . repeatmasker . org ) ., Sixteen of the twenty miRNAs originally considered have repeat elements either overlapping the predicted snoRNA encoding the miRNA or within 400 nucleotides ., The position of the repeat elements with respect to the position of the miRNA and predicted snoRNA is shown in Figure 4 and Figure S3 ., In addition , putative L1 consensus recognition sites and flanking target site duplications ( TSDs ) , which are characteristic of retrogenes , were also identified surrounding many of these molecules ( Figure 4 and Figure S4 ) ., Some of these putative snoRNA-encoded miRNA regions have a genomic structure that is very similar to numerous snoRTs 16 , consisting of the snoRNA/miRNA region in close proximity to a downstream SINE member repeat element and flanked by target site duplications ( TSDs ) ., In addition , immediately upstream from the 5′ TSD , an L1 consensus recognition site is often found and a poly ( A ) tail can be identified upstream from the 3′ TSD ., Three such examples resembling the HBI-61c snoRT from 16 are shown in Figure 4A–C ., Shown in Figure 4D is the genomic region surrounding mir-605 , which consists of two pairs of TSDs , one of which flanks a SINE repeat element and the other of which flanks the whole snoRNA/miRNA/SINE region ., This structure resembles the ACA18e snoRT example from 16 ., The examples shown in Figure 4 suggest that it is the predicted snoRNA and not the miRNA hairpin that was captured in the retrogene construct and initially transposed , in a manner similar to snoRT events previously described 16 , 17 ., To explore further potential snoRNA-like features of these miRNA precursors and to investigate whether they have retained some H/ACA snoRNA functionality , we tested whether they can bind dyskerin , a protein component of the functional box H/ACA snoRNPs ., Dyskerin serves as the pseudouridine synthase 36 and is proposed to bind the ACA box 37 ., Five of the twenty snoRNA-like miRNAs , mir-664 , mir-151 , mir-605 , mir-215 and mir-140 were selected for this analysis because they are expressed in HeLa cells ., Purified nuclei from HeLa cells expressing YFP-dyskerin or GFP as a control were immunoprecipitated using an antibody against the fluorescent proteins as described in the Methods ., The RNA isolated from these samples was analysed by RT-PCR for the presence of the molecules of interest ., As shown in Figure 5C , in addition to E2 ( a well-characterised box H/ACA snoRNA ) , five snoRNA-like miRNA precursors ( the extended regions of mir-664 , mir-151 , mir-605 , mir-215 and mir-140 ) are bound by dyskerin ., In contrast , the precursor of hsa-let-7g , a miRNA precursor with no similarity to box H/ACA snoRNAs is not pulled down by dyskerin ., And as expected , other abundant nuclear RNAs including GAPDH pre-mRNA , the small nuclear RNA U1 , the box C/D snoRNA U3 and 5S rRNA are not immunoprecipitated by dyskerin ., These binding experiments confirm the in silico predictions that some miRNA precursors sufficiently resemble box H/ACA snoRNAs to be bound by dyskerin ., Two of the dyskerin-bound miRNA precursors were further characterised by fractionated northern analysis to investigate where the predicted snoRNAs and smaller fragments localise in the cell ., As shown in Figure 6 , bands of the size of the predicted H/ACA snoRNA full-length molecules encoding mir-151 and mir-664 localise to the nucleolus ( bands labelled with ‘a’ in panels 6A and 6B ) ., Bands of the size of the miRNA hairpins are detected in all three fractions although the putative mir-151 hairpin is mainly nucleolar whereas the putative mir-664 hairpin accumulates predominantly in the nucleoplasm and cytoplasm ( bands labelled with ‘b’ in panels 6A and 6B ) ., The mir-151 mature form is also detected and mainly found in the nucleoplasm and cytoplasm ( bands labelled with ‘c’ in panel 6A . For a longer exposure , please see Figure S5 ) ., And a band slightly larger than the mir-664 mature form localises mainly in the nucleoplasm ., Numerous miRNAs have been previously shown to be repeat-derived 12 , 19–21 and many snoRNAs have been described as retrogenes 16 , 17 ., Here , we hypothesize that some reported miRNAs have evolved from box H/ACA snoRNAs or snoRTs ., Several lines of evidence support this possibility ., Fourteen known box H/ACA snoRNAs encode smaller fragments of miRNA size that have been experimentally detected , three of which are reported miRNAs ., Analysis of mammalian conservation patterns suggests that these genomic regions originally encoded the full-length H/ACA snoRNA molecules and not only the miRNAs ., If a subgroup of miRNAs has indeed evolved from box H/ACA snoRNAs , we reasoned that although some of these miRNAs might have sufficiently evolved to no longer bear measurable similarity to H/ACA snoRNAs , others might display detectable H/ACA snoRNA features ., In an effort to further characterise the prevalence of the relationship between these two classes of small RNA molecules , we scanned the regions encoding known miRNAs for the presence of box H/ACA snoRNA features using the snoGPS predictor ., We identified twenty reported miRNAs from miRBase 25 that are encoded in larger regions predicted with high scores to be box H/ACA snoRNAs ., The predicted box H/ACA snoRNAs display usual box H/ACA snoRNA features and resemble the fourteen box H/ACA snoRNAs that encode experimentally detected smaller fragments ., In addition , the genomic sequence surrounding several of the predicted snoRNA-like miRNAs very closely resembles those described for some snoRTs 16 ., These analyses show that some genomic regions previously reported to encode miRNAs resemble regions that encode H/ACA snoRNAs on numerous levels ., This suggests that these miRNAs have evolved from H/ACA snoRNAs or snoRTs ., We applied stringent selection criteria in our analysis , so anticipate that other box H/ACA snoRNA-like miRNA precursors also exist but have not been identified here ., Due to the inherent similarity between miRNAs and snoRNAs , such a relationship is easy to overlook as once a region is categorized as belonging to one molecular class , it is often no longer considered when searching for other types of molecules ., The human genome has been scanned previously for the presence of box H/ACA snoRNAs using mammalian snoGPS 32 ., However , the search space was limited to the 20% most well conserved regions between the human , mouse and rat genomes ., In addition , the dataset was repeat-masked , thus eliminating repeat-derived regions such as those encoding many miRNAs ., Finally , the dataset was restricted to sequences that do not overlap with known features in the UCSC Human Genome Browser database , thus probably eliminating all known miRNAs ., As a consequence , it is not surprising that no miRNA encoding regions were identified as also encoding predicted snoRNAs ., Moreover , at least one recent snoRNA predictor , SnoReport 38 uses miRNAs as negative training examples , thus making it very unlikely to identify any of the snoRNA-like miRNA regions described here ., Although no significant sequence similarity is detected between predicted snoRNA molecules encoding miRNAs and the known snoRNAs that target the same pseudouridylation sites , it is interesting to note that three snoRNAs ( ACA52 , HBI-61 and ACA19 ) share their target pseudouridylation sites with eight of the predicted snoRNAs encoding miRNAs ( Table 2 ) ., This situation also exists amongst known snoRNAs , some of which share the same target site without displaying significant sequence similarity ., In particular , examples exist of a snoRNA harbouring two guide regions , each of which is shared with a different snoRNA ., For example , ACA22 which shares one of its targets with ACA33 and the other with U64 , has no significant sequence similarity with either molecule ., Similarly , ACA50 shares target sites with ACA36 , ACA8 and ACA62 but while it has high sequence identity with ACA62 , it has no significant sequence similarity with ACA8 or ACA36 ., This redundancy in rRNA complementarity may suggest some box H/ACA snoRNAs and snoRTs are not under as much selective pressure to avoid mutations ., We hypothesize that some of these snoRNA encoding regions might be in the process of evolving from functional snoRNAs to miRNA-like precursors ., This process might be facilitated by the fact that box H/ACA snoRNAs have a structure ( 2 hairpins ) that is probably favourable to the formation of miRNAs ., The in silico data presented here support these ideas ., We tested the predictions by experimentally showing that the precursors of five of the predicted snoRNA-like miRNAs , mir-664 , mir-151 , mir-605 , mir-215 and mir-140 , interact with dyskerin , a protein component of functional H/ACA snoRNPs ., While a lack of interaction to dyskerin could not rule out an evolutionary relationship between these miRNAs and snoRNAs as the miRNAs might have evolved sufficiently to no longer interact with functional protein components of snoRNPs , the detection of such an interaction considerably reinforces such claims ., These results show that the snoRNA-like miRNA precursors sufficiently resemble box H/ACA snoRNAs to bind dyskerin , strengthening the possibility of an evolutionary relationship between these molecules ., Further experiments will be necessary to investigate whether these molecules also retain the capability of targeting rRNA in vivo ., It will also be necessary to experimentally test whether the remaining fifteen predicted snoRNA-like miRNA precursors also display aspects of H/ACA snoRNA functionality ., The fact that three of these molecules ( mir-548d-1 , mir-1297 and mir-616 ) display the sequence AGA instead of the canonical ACA box could indicate that they have evolved sufficiently to no longer retain H/ACA snoRNA functionality ., We do note that while identifying molecules that display both miRNA and snoRNA functionality supports our evolutionary hypothesis , we also expect to find a larger number of molecules that display features of both molecules but do not represent completely prototypical examples ., It is interesting to note that Saccharomyces cerevisiae has snoRNAs but no reported miRNAs , consistent with a relationship where primordial snoRNAs may have given rise to certain classes of miRNAs ., This idea is supported by a recent article by Saraiya and Wang reporting that the primitive parasitic protozoan Giardia lamblia , which does not have RNA interference capabilities but has miRNA processing machinery , uses box C/D snoRNAs as miRNA precursors 39 ., In addition to this , Taft and colleagues have recently reported that most snoRNAs in animals , Arabidopsis and Schizosaccharomyces pombe generate small RNAs ( of ∼20–24 nucleotides in length for animal box H/ACA snoRNAs ) , which are associated with argonaute proteins 40 ., Current data such as these dual function molecules with both miRNA and snoRNA capabilities which exist in both human 24 and Giardia 39 and likely many other organisms 40 suggest this process of evolving from a snoRNA encoding genomic region to a miRNA-like encoding region could be ongoing ., In addition to investigating whether some of the H/ACA snoRNA-like miRNA precursors display functional H/ACA snoRNA capability by binding to dyskerin ( Figure 5 ) , we have also characterised the cellular localisation of two of these molecules: the precursors of mir-151 and mir-664 ( Figure 6 ) ., While bands of the size of the predicted full-length H/ACA snoRNA molecules localise to the nucleolus , consistent with their binding to dyskerin , bands of the size of the predicted hairpin form of these miRNAs can be found in all three fractions considered but accumulate mainly in the nucleolus ( in the case of mir-151 ) and in the nucleoplasm and cytoplasm ( in the case of mir-664 ) ., The mature form of mir-151 accumulates mainly in the nucleoplasm which is unusual for a miRNA but might be a consequence of the snoRNA features displayed by its precursor ., These results are consistent with a recent study showing that the precursors and/or mature form of a number of rat miRNAs accumulate in the nucleolus 41 ., Further studies will be required to investigate the exact nature and role of each of these molecules in these cellular compartments as well as how they are processed ., While all the miRNAs characterised in Table 2 are classified as miRNAs in miRBase 25 , they have not all been extensively analysed ., Of the five extended miRNA regions that we experimentally found to be bound by dyskerin , three ( mir-151 , mir-140 and mir-215 ) have been further characterized and functionally validated , either by studies of their processing into their mature form or validation of their targets and effects ., Indeed , mir-151 has been shown to be processed into its mature form by usual miRNA processing machinery 42 while functional targets of mir-140 have been experimentally validated 43 ., And mir-215 , which has been shown to have reduced expression in cancer tissues compared to normal cells , is capable of inducing cell-cycle arrest , colony suppression and cell detachment from a solid support when transfected into cells 44 ., Mir-664 and mir-605 have not , to our knowledge , been further functionally validated and will require additional experimental evidence to confirm they are true miRNAs ., In particular , mir-605 has only been identified previously with one sequence read 45 ., Given that the extended region of mir-605 is predicted to serve as a guide for an experimentally validated pseudouridylation site whose guide is unknown , we postulate that this region encodes an H/ACA snoRNA rather than a miRNA ., This type of analysis can thus be used to filter out unlikely miRNA candidates from the large miRNA repositories which contain many poorly characterized molecules ., A recent large-scale study defining an expression atlas for mammalian miRNAs , by Landgraf and colleagues , classifies known miRNAs into four different groups: prototypical , repeat-derived , repeat-clustered and unclassified 46 ., A lack of repetitiveness , evolutionary conservation and 5′ end processing were considered to classify miRNAs as prototypical ., Only two ( mir-215 and mir-140 ) of our twenty miRNAs encoded in predicted snoRNAs are classified as protypical in this study ., The remaining eighteen miRNAs were either classified as

Introduction, Results, Discussion, Methods

MicroRNAs ( miRNAs ) and small nucleolar RNAs ( snoRNAs ) are two classes of small non-coding regulatory RNAs , which have been much investigated in recent years ., While their respective functions in the cell are distinct , they share interesting genomic similarities , and recent sequencing projects have identified processed forms of snoRNAs that resemble miRNAs ., Here , we investigate a possible evolutionary relationship between miRNAs and box H/ACA snoRNAs ., A comparison of the genomic locations of reported miRNAs and snoRNAs reveals an overlap of specific members of these classes ., To test the hypothesis that some miRNAs might have evolved from snoRNA encoding genomic regions , reported miRNA-encoding regions were scanned for the presence of box H/ACA snoRNA features ., Twenty miRNA precursors show significant similarity to H/ACA snoRNAs as predicted by snoGPS ., These include molecules predicted to target known ribosomal RNA pseudouridylation sites in vivo for which no guide snoRNA has yet been reported ., The predicted folded structures of these twenty H/ACA snoRNA-like miRNA precursors reveal molecules which resemble the structures of known box H/ACA snoRNAs ., The genomic regions surrounding these predicted snoRNA-like miRNAs are often similar to regions around snoRNA retroposons , including the presence of transposable elements , target site duplications and poly ( A ) tails ., We further show that the precursors of five H/ACA snoRNA-like miRNAs ( miR-151 , miR-605 , mir-664 , miR-215 and miR-140 ) bind to dyskerin , a specific protein component of functional box H/ACA small nucleolar ribonucleoprotein complexes suggesting that these molecules have retained some H/ACA snoRNA functionality ., The detection of small RNA molecules that share features of miRNAs and snoRNAs suggest that these classes of RNA may have an evolutionary relationship .

The major functions known for RNA were long believed to be either messenger RNAs , which function as intermediates between genes and proteins , or ribosomal RNAs and transfer RNAs which carry out the translation process ., In recent years , however , newly discovered classes of small RNAs have been shown to play important cellular roles ., These include microRNAs ( miRNAs ) , which can regulate the production of specific proteins , and small nucleolar RNAs ( snoRNAs ) , which recognise and chemically modify specific sequences in ribosomal RNA ., Although miRNAs and snoRNAs are currently believed to be generated by different cellular pathways and to function in different cellular compartments , members of these two types of small RNAs display numerous genomic similarities , and a small number of snoRNAs have been shown to encode miRNAs in several organisms ., Here we systematically investigate a possible evolutionary relationship between snoRNAs and miRNAs ., Using computational analysis , we identify twenty genomic regions encoding miRNAs with highly significant similarity to snoRNAs , both on the level of their surrounding genomic context as well as their predicted folded structure ., A subset of these miRNAs display functional snoRNA characteristics , strengthening the possibility that these miRNA molecules might have evolved from snoRNAs .

molecular biology/rna-protein interactions, evolutionary biology/genomics, evolutionary biology/bioinformatics, computational biology/genomics, genetics and genomics/bioinformatics

journal.pcbi.1005503

Predicting explorative motor learning using decision-making and motor noise

Previously , human motor learning has mainly been examined through motor adaptation tasks in which participants are exposed to a novel perturbation during reaching movements 1–4 ., The error reduction observed during these tasks has been conceptualised as a cerebellar-dependent supervised-learning process in which they learn through a sensory prediction error 3 , 5 , 6 ., However , recent work has shown that motor learning is a far more complex process that can involve multiple mechanisms , including decision-making processes , taking place simultaneously 7–10 ., One example of these motor-learning processes is reinforcement learning ., This learning mechanism requires participants to explore their motor behaviour in order to identify actions that maximise expected future success/reward ( in contrast with minimising the sensory prediction error ) ., Despite being significantly slower and more variable than learning through a sensory prediction error , recent work has shown that participants are able to identify and adjust specific features of a movement , such as the curvature of a trajectory , simply through a reinforcement signal 8 , 11–15 ., Such explorative motor learning has been explained using reinforcement models in which learning is driven by a reward prediction error ., This enables actions to be selected based on the probability of yielding future rewards 11 , 13 , 15 , 16 ., Arguably , it follows that explorative motor learning is simply a sequential decision task where the goal is to optimise reward in the face of task and sensory uncertainty ., If so , participant behaviour on a matched high-level decision-making task should be predictive of performance during an explorative motor learning task ., Previous work has compared high-level ( economic ) decision-making tasks with an equivalent motor lottery task 17 , a review ., Some found that , in contrast to the well-documented sub-optimality in high-level ( economic ) decision-making 18 , participants were able to perform near optimal decisions in a motor lottery task 19 , 20 ., For example , during simple pointing movements , participants hold an internal representation of motor noise uncertainty and compensate for this variability when planning a movement 19 , 20 ., However , others found that participants in a motor lottery task ( where the uncertainty of outcomes were primarily due to motor noise ) exhibited significant suboptimal choice patterns 17 , 21 ., Yet , the patterns of deviation from optimal choice were markedly different from those shown in high-level ( economic ) decision-making ., Previous work highlights that one of the unique features that affect motor performance is a noisy motor system ( motor noise ) ., To our knowledge , most of these previous studies focused on binary or one-shot decision-making and its motor analogue ., In contrast , here we ask if explorative motor learning is a sequential decision task that optimises reward in the face of task uncertainty , sensory uncertainty and motor noise uncertainty 15 , 22 ?, To explore this question , we investigated learning performance in an explorative motor learning task 13 and a decision-making task with a similar underlying structure with the exception that it was not subject to motor noise ., We also took an independent measurement of each participant’s motor noise ., We formulated the learning problem as a Partially Observable Markov Decision Process ( POMDP ) and built a computational model to solve the defined POMDP ., The question we asked was whether we could predict participant explorative motor learning performance by fitting the model to the decision task performance and then adding each participant’s measured level of motor noise ., In addition , we were interested in whether we could predict motor learning performance as a function of gains and losses—one of the key concepts in the decision-making literature ., In Prospect Theory 18 , a theory of human decision-making , gains and losses are defined relative to a reference point that shifts with the decision context ., For example 18 , imagine a situation where a participant has just lost £2000 and is now facing a choice between a 100% chance of winning £1000 and a 50% chance of winning £2000 or nothing ., If the participant’s reference frame had shifted to account for their recent loss , then they are likely to code the decision as choice between a 100% chance of losing £1000 and a 50% chance of losing £2000 or nothing ., Understanding how people interpret gains and losses is important , because , for example , it has been shown that people are more adventurous in the latter representation ( i . e . , loss aversion , 18 ) ., In the motor learning domain , research has shown that reward ( positive feedback ) and punishment ( negative feedback ) have multifaceted effects on motor learning 23 ., Therefore , we were interested in understanding whether the ideas regarding gains and losses in decision-making were relevant to explorative motor learning ., We investigated performance during an explorative motor learning ( reaching ) task adopted from 13 and a novel decision-making ( DM ) task which had a similar underlying structure ., In the reaching ( MO ) task ( Fig 1A ) , participants were seated at a desk , looking down at a horizontal mirror that reflected task-related stimuli from a computer screen ., The mirror blocked direct observation of the index finger , which was instead represented on the mirror via a circular green cursor ., Participants were asked to draw trajectories by sliding their index finger from a central start position across the surface of the desk towards a target line ( thick black line in Fig 1B ) positioned in front of the start position ., Participants made 25 attempts ( green dashed lines in Fig 1B ) to approximate each hidden target trajectory ( red line ) ., Each attempted trajectory resulted in a score that indicated the proximity of the attempted trajectory to the target trajectory ., Both the target and the attempted trajectory were characterised by two parameters: direction and curvature ( Fig 1C; Eq 1 ) ., The score for each attempt was calculated based on the errors between target and attempt in these two dimensions ( Eq 2 ) ., The participants were instructed to adjust their movements’ direction and curvature based on the feedback to produce movements that were as close to the target trajectory as possible ., Each participant attempted to match 24 different , invisible target trajectories that varied in both direction and curvature ( Fig 1C ) ., We also designed a novel decision-making task ., The objective was to capture the structure of the motor learning ( reaching ) task within a decision-making context that was uncontaminated by motor noise ., The effect of motor noise on an aimed movement is that the outcome location is a probability density function centred on the goal 24 ., In the decision-making task , participants interacted with an interface using a computer mouse ., The interface consisted of a two-dimensional grid with cells ( Fig 1D ) ., The horizontal and vertical dimension reflected two parameters: α and β respectively , akin to the direction and curvature parameters in the reaching task ., The parameter values were assigned to the cells in a spatially ordered manner ., Each cell of the grid therefore corresponded to a unique combination of the two parameters ., When one of the cells ( i . e . , one parameter pair ) was chosen as a target cell , the score associated with each of the cells was then calculated using the same score function ( Eq 2 ) as in the reaching task ., Once a cell was chosen ( mouse-clicked ) , an associated score would appear in the feedback window at the top of the screen ., Similar to the reaching task , participants were required to explore different cells ( parameter pairs ) based on the feedback to find the cell that was as close to the target cell as possible ., Participants were asked to search for a series of 24 hidden target cells ., In both tasks , the 24 target trajectories/cells were randomly divided into two feedback conditions ( 12 of each ) : a positive feedback condition and a negative feedback condition ., In the positive feedback condition , points ranged from 0 to 50 ( Eq 2 ) , with greater magnitude indicating greater similarity between the attempted and target trajectory ( 50 for the target ) ., In the negative feedback condition , points ranged from -50 to 0 ( Eq 2 ) , with greater magnitude indicating reduced similarity between the attempted and target trajectory ( 0 for the target ) ., Hence , the goal for the positive feedback condition was to achieve 50 points , whereas for the negative feedback condition it was to achieve 0 points ( i . e . , avoiding losing points ) ., Participants were told which of the two feedback conditions they were in at the beginning of each target search ., Analysis of the points achieved , across both tasks , showed that participants were able to update their behaviour , based on the feedback , and produce actions that were close to the target trajectory/cell ( Fig 2A and 2B ) ., First we examined whether participant performance was different between the positive and negative feedback conditions within both tasks ., To do so , we averaged each participant’s performance across all target trajectories/cells that were experienced with either positive or negative feedback ( Fig 2B ) ., We fitted the exponential function , y = ae−bx + c , to each participant’s average learning curve in each condition ( across 12 targets ) ( Decision-Making: R2 = 0 . 97 ± 0 . 02; reaching: R2 = 0 . 89 ± 0 . 10 ) ., Paired t-tests on the three parameters ( a , b ,, c ) revealed no significant differences between positive and negative feedback conditions in either the decision-making or reaching task ( Table 1 ) ., Further analysis regarding the effect of positive and negative feedback is provided at the end of the results section ., However , for the following analysis , we pooled data from the positive and negative feedback conditions by simply defining a negative score as its positive equivalent ., For example , a score of -40 ( 10 points above the minimum point -50 ) in the negative condition was equivalent to 10 ( 10 points above the minimum point 0 ) in the positive condition ( Fig 2B ) ., Therefore , we then had one average learning curve ( across 24 targets ) for each participant in each of the tasks ., Next we compared the learning performance across tasks ( Fig 2 ) ., In the decision-making task , starting from 12 . 08 ± 6 . 05 , the average points achieved for each target was 49 . 98 ± 0 . 31 ., For the reaching task , starting from 15 . 92 ± 4 . 42 , the average points achieved for each target was 40 . 96 ± 4 . 67 ., Although participants began with a similar score across tasks , they achieved significantly more points in the decision-making task ( t ( 23 ) = 9 . 49 , p < 0 . 001 , d = 2 . 74 ) ., We also noticed that some of the participants failed to explore the curvature dimension in the reaching task ., Specifically , a small subset of participants produced straight movements with little curvature ( Fig 2D ) ., This resulted in significantly greater error remaining in the curvature dimension ( Fig 2F ) , and thus substantially lower points being achieved ., Having quantified the amount of curvature explored during the reaching task , 4 out of the 24 participants ( 10 , 16 , 18 , 22 ) could be considered as outliers ( Fig 2G ) ., For the following analysis , we removed these 4 participants unless stated otherwise ., The aim of both tasks was to find the target by exploring a range of actions ., As the proximity of an action to the target was indicated by the number of points , the exploration process may have been performed by avoiding the actions with bad outcomes ( low reward , high punishment ) and reinforcing the actions with good outcomes ( high reward , low punishment ) ., Thus we expected to see participants make larger action changes after receiving lower points and smaller action changes after receiving higher points ., Using the α and β parameters from each action , we determined action change , ∇A , between two successive actions: at = αt , βt and at+1 = αt+1 , βt+1 as Euclidean distance between two points , i . e . , ∇ A = | ( α t - α t + 1 ) 2 + ( β t - β t + 1 ) 2 | ., As shown in Fig 3A , the action change decreased as a function of score in both tasks ., Interestingly , although the actions were in different forms across the tasks , the amount of action change ( in terms of the Euclidean distance measurement ) given the levels of score was quantitatively similar across the tasks ., Paired t-tests revealed no significant difference in the average action changes between the DM and MO tasks ( t ( 19 ) = 1 . 33 , p = 0 . 20 , d = 0 . 42; Bars in Fig 3A ) ., One pressing question is how the action change looked in terms of α and β within and across tasks ., To examine this , we first fitted the exponential function , y = ae−bx + c , to each participant’s error reduction learning curves ( examples shown in Fig 2E and 2F; DM: R2 = 0 . 96 ± 0 . 02 α , R2 = 0 . 92 ± 0 . 20 β; MO: R2 = 0 . 84 ± 0 . 20 α , R2 = 0 . 80 ± 0 . 26 β ) ., Secondly , three two-way ( IV1:task = DM vs MO; IV2:dimension =α vs β ) repeated measures ANOVA were performed for the parameters a , b and c respectively ., The results showed that the error reduction rate, ( b ) and plateau, ( c ) were not significantly different across α and β within each of the tasks ( S1 Table ) ., In both tasks , the errors in both dimensions were equally weighted to determine the feedback score ( Eq 2 ) ., Hence , the participants learnt to treat these two dimensions equally in order to achieve maximal points ., On the other hand , the error reduction rate, ( b ) and plateau, ( c ) were different across the tasks ( S1 Table ) ., We postulate that this difference was primarily due to the fact that the reaching task required participants to overcome uncertainty involved in the execution of the planned trajectories ( motor noise ) and the lack of visual information of the executed action that was associated with the feedback score ., To examine the role of motor noise in the explorative motor learning task , we obtained a measure of motor noise for each participant ., In the motor noise measurement task , unlike the main motor learning task where the target trajectories were hidden , a series of trajectories was displayed on the screen ( red lines in Fig 4A ) ., For each displayed trajectory , the participants were asked to trace it within a specific time window ( > 700ms and < 1500ms ) ., Five traces were performed for each trajectory ( black lines in Fig 4A ) ., By comparing the direction and curvature parameters of each trace with the target parameters , we obtained one direction error and one curvature error for each trace ., Therefore , we had 5 pairs of errors for each target trajectory ( 5 traces ) ., Each participant was asked to trace 10 target trajectories ., Hence , we collected 50 errors in the direction and 50 errors in the curvature ( Fig 4B ) ., For each participant , we calculated the standard deviation across the errors in the direction and curvature parameters and used these two standard deviations as our measure of their motor noise in the direction and curvature dimensions , respectively ( Fig 4B ) ., As shown in Fig 4D , although participants were encouraged to replicate the trajectories displayed on the screen , the average errors made in both dimensions were significantly larger than zero ( Dir: 0 . 10 ± 0 . 05; Cur: 0 . 12 ± 0 . 04 ) ., Next , we examined how each participant’s level of motor noise correlated with their ability to ‘find’ the hidden target trajectory ., First , the measure of variance from the motor noise task provided an estimate of how accurate a participant could replicate a planned movement trajectory ., During the reaching task , movement variance was initially relatively high as participants explored the space of possible trajectories ( including both the exploration variance and motor noise variance ) ( Fig 5A ) ., However , by the end of each target search movement variance had decreased toward to a level observed in the motor noise task ( although still higher than the variance purely due to motor noise ) ., More importantly , we found that the level of variance observed in the motor noise task was negatively associated with motor learning performance across participants ., Specifically , we fitted an exponential function , y = ae−bx + c , to each participant’s average learning curve across all the targets in the reaching task ( R2 = 0 . 94 ± 0 . 05 ) ., A Pearson correlation indicated that there was a negative correlation between motor noise and the learning rate parameter b ( r = -0 . 47 , n = 20 , p = 0 . 022; Fig 5B ) , and maximal points achieved ( r = -0 . 49 , n = 20 , p = 0 . 015; Fig 5C ) ., The main purpose of this study was to test whether explorative motor learning and decision-making could both be understood as a sequential decision process that optimises reward given task , sensory and/or motor uncertainty ., To this end , we framed the learning problem as a Partially Observable Markov Decision Process ( POMDP ) 25 and built a computational model to solve ( approximately ) the defined POMDP ., The POMDP framework has been proposed to model a variety of real-world sequential decision problems 25–30 , and provides a general mathematical framework that captures the interaction between an agent and a stochastic environment ( Fig 6 ) ., It suggests an interpretation of participant behaviour in terms of maximising total expected future reward ., An informal description of the decision-making task as a POMDP is given in what follows ( for a formal description see the Methods ) ., There is a set of states , each of which corresponds to an event in which the target is one of the cells in the grid ( Fig 1D ) ., As in the experiment , the task is divided into episodes; each episode consists of 25 time steps ( attempts ) to find a hidden target cell ., On each episode , one of the cells is randomly chosen as the hidden target cell ., That is , the environment is in one of the states ( Environment; Fig 6 ) ; and the state is not directly observable ., On each time step within one episode , the model chooses an action ( i . e . , which cell to click ) based on a control strategy so as to maximise the expected future reward ( Action selection; Fig 6 ) ., After taking an action , the model receives two signals from the environment: an observation and a reward ( cost if the value is negative ) ., In our case , the observation and reward are equal , which is the feedback score ( points ) ., Given the defined POMDP , an algorithm can then be used to acquire the optimal control strategy for action selection ., In our model , an approximated optimal control strategy was acquired ( more details in Methods ) ., Framing the model as a POMDP allows for the calculation of the optimal policy given the theoretical constraints 31 ., Constraints include the uncertainty in the sensory input and the uncertain effect of executing an action ., The behaviour predicted by the optimal policy is therefore the rational behaviour given the constraints ., The POMDP framing thereby serves the goal of drawing a causal relationship between the theoretical constraints and the behaviour ( assuming rationality , 32 ) ., For the decision-making task , we assumed that participant performance was constrained by the fact they were naïve to the underlying equation used to generate the score ., In other words , participants were unsure how the current score ( received by selecting a certain cell ) related to the position of the target cell ., This uncertainty was represented in our model by a likelihood uncertainty parameter ( Γ; Eq 3 ) ., Crucially , this was the only free model parameter for the decision-making task ., Initially , we ran the model and examined the effect of increasing likelihood uncertainty on learning rate ., As shown in Fig 7 , a model with a likelihood uncertainty of 1 would find the target after approximately 7 attempts , with increasing uncertainty causing a gradual decline in the speed at which the target was found ., The previous modelling showed that individual performance in the decision-making task ( parameter Γ ) and motor noise task were both critical for predicting individual performance in the reaching task ., Next , we examined whether participant performance in the decision-making task would become similar to their performance in the reaching task if their individual ‘motor noise’ was added to the feedback they received during decision-making ., We recruited a further 6 participants for Experiment 2 ., In this experiment , we asked each participant to complete the same reaching and motor noise task as in the previous experiment ., However , for the decision-making task , the feedback score provided after each attempt ( i . e . , clicking on a cell ) now included noise parameters that were equivalent to the level of noise/uncertainty observed in the motor noise task for each participant ( DM+noise ) ., For example , when a cell α1 , β1 is selected and the target is αT , βT , the feedback score is determined by two errors: |α1 − αT| + noiseα and |β1 − βT| + noiseβ , instead of |α1 − αT| and β1 − βT| as in the previous experiment ., Two motor noise parameters: noiseα and noiseβ were measured in the motor noise task ., Fig 10 shows that participant learning in the decision-making task with ‘motor noise’ ( DM+noise ) and the reaching task ( MO ) was now identical ( R2 = 0 . 88 , rmse = 2 . 64 , Fig 10B ) ., Once again , three two-way repeated measures ANOVAs were conducted on fitted exponential parameters a , b and c ., Unlike Experiment 1 , we found that the error reduction was not significantly different either across α and β or across tasks ( Fig 10C; S2 Table ) As we said in the introduction , we were also interested in whether trial-by-trial motor performance could be predicted as a function of gains and losses ., It has been suggested by a number of authors that the effects of gains and losses maybe be elucidated via trial-by-trial analysis of choice behaviour , as the outcomes of previous choices have been shown to affect subsequent decisions 33 , 34 ., For example , in a sequential tree search task , it has been shown that participants are more likely to curtail any further evaluation of a branch as soon as they encountered a large loss 35 ., Another example of these local influences on choice behaviour is a tendency to repeat the same behaviour following a gain , coupled with a bias to switching behaviour after a loss 36 ., In Experiment 1 , gains and losses are operationalised as positive and negative feedback ., Here , we examine the degree of action change on attempt t + 1 after receiving a certain score on attempt t ., As mentioned ( page 6 ) , the action change was defined as the Euclidean distance between two actions ., First , we compared action changes between positive and negative feedback conditions ., The action change following a score of 10 ( 10 points above the minimum point 0 ) in the positive condition was compared to the action change following -40 ( 10 points above the minimum point -50 ) in the negative condition ., Paired t-tests revealed no significant difference between positive and negative conditions for either the DM task ( t ( 23 ) = -1 . 00 , p = 0 . 32 , d = -0 . 21; Bars in Fig 11 ) or the MO task ( t ( 23 ) = -0 . 26 , p = 0 . 79 , d = -0 . 05; Bars in Fig 11 ) ., Model predictions were given in Fig 9 ., Next , we considered whether gains and losses are better measured relative to a reference point ., Prospect theory suggests that gains and losses are measured relative to a reference point that may shift with recent experience 18 , 38 ., It follows that within the current study , participants may have thought of gains and losses relative to the best score achieved so far while searching for the current target ., For example , a participant who received a score of 22 on their 8th attempt might see this as a loss of 13 given that their best score so far ( on attempt 4 ) was 35 ., Therefore , during the 25 attempts , the current best score could be thought of as the current reference point ., A score that was better than the reference point can be defined as a gain , and a score worse than this reference point a loss ., Participant data was pooled across the positive and negative feedback conditions by transforming a negative score into its positive equivalent ., We investigated action change on attempt t + 1 as a function of the maximum points achieved up to t − 1 ( the reference point ) ., A gain was a score that was better than the reference point on attempt t , and a loss was a score that was worse or equal to the reference point on attempt t ( Fig 12 ) ., Paired t-tests indicated that the action change following a loss was statistically greater than the action change following a gain in both the DM task ( t ( 23 ) = 11 . 39 , p < 0 . 001 , d = 2 . 32; Bars in Fig 12 Left ) and MO task ( t ( 23 ) = 12 . 18 , p < 0 . 001 , d = 2 . 49; Bars in Fig 12 Right ) ., The model predicted this behaviour in both the DM task ( R2 = 0 . 91 , RMSE = 0 . 13; Fig 12 ) and MO task ( R2 = 0 . 96 , RMSE = 0 . 10; Fig 12 ) ., This suggests that participant sensitivity to gains and losses was possibly independent of the positive and negative feedback conditions but in fact related to a shifting reference frame determined by their current best score ., Our goal was to examine whether explorative motor learning 13 , 14 and decision-making could be modelled as the ( approximately ) optimal solution to a Partially Observable Markov Decision Process 25 bounded by noisy neural information processing ., To achieve this , we studied performance during an explorative motor learning task 13 and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor noise ., The solution to the defined POMDP explained 0 . 94 of the variance in the decision-making task , and 0 . 76 of the variance in the explorative motor learning task ., Importantly , we did not fit the model to the motor learning data but predicted it based on parameters derived from the decision-making task and a separate motor noise task ., In addition , the model was also able to explain ( 1 ) varying performance across different target trajectories , ( 2 ) the magnitude of action change after different scores , and ( 3 ) the differences in the magnitude of action change between gains and losses ., A key contribution of the work reported here is to furthering our understanding of the relationship between motor learning and decision making ., In the reported studies , the decision-making task allowed measurement of a participant’s ability to make use of information in previous attempts ., Participants with a high likelihood uncertainty were less able to integrate this information and were slower learners ., Participants with a low likelihood uncertainty were more able to integrate information and were faster learners ., Given an equivalent level of motor noise , participants who were faster learners in the decision-making task were also faster learners in the reaching task ., We can draw this conclusion because of the modelling approach that we used ., We used the likelihood uncertainty parameter estimated from the decision-making task , and the individual motor noise estimated from the motor noise task , to predict motor learning behaviour ., Importantly , we found that the model performed significantly worse when averaged parameters ( across all participants ) were used rather than parameters derived from each individual’s behaviour ., This suggests that taking into account individual performance during both the decision-making and motor noise tasks was important for explaining behaviour during the explorative motor learning task ., Finally , we showed that performance during the decision-making task was similar to performance in the reaching task if motor noise was added to the decision-making task’s feedback ., This provides strong empirical evidence for the predicted relationship between explorative motor learning , decision-making and motor noise within our model ., Although the decision-making task was designed to have a similar underlying structure to the reaching task there were still differences ., For example , unlike the explicit visual cues of orthogonally organised actions in the decision-making task , the relationship between the two parameters was less intuitive in the reaching task ., It is possible that this could have led to these parameters being treated more dependently in the reaching task ., For instance , the errors of these two parameters were correlated during the ‘motor noise’ task ( r = 0 . 52 , p = 0 . 008 ) ., However , Dam et al . , ( 2013 ) 13 , who used a near identical motor learning task , showed that participants were able to isolate direction and curvature so that they only altered the parameter being currently rewarded ., We believe our results suggest that participants treated the parameters in a similar fashion within the reaching task and decision-making task ., For example , the rate of error reduction for the two dimensions was similar within each task , indicating that participants explored both parameters simultaneously , while also implying a comparable strategy across both tasks ., Another potential difference was that there were clearly defined discrete action options ( grid-design ) in the decision-making task ., It has previously been shown that there are limits to the sensory and motor system’s ability to distinguish endless continuous options 42 , 43 ., For example , our ability to distinguish two shades of grey is limited rather than continuous ., This suggests that the motor learning task may also have involved a set of discrete action options ., However , our tasks were not designed to measure participant ability to distinguish between trajectories that varied in direction and curvature ., Therefore , it was not possible to define what these discrete action options could have been during the motor learning task ., Future work could examine whether the ability of the decision-making task to predict explorative motor learning is improved by creating a grid-size which directly reflected participant’s ability to distinguish trajectories with different curvature and directions ., It was also clear that our model did not fully explain motor learning behaviour ., For example , the model predicted faster learning rates and higher plateaus than what was achieved by the participants for a number of trajectories in the reaching task ., These trajectories had large amounts of curvature and also began on one side of the central line and finished on the opposite side ., These elements appeared to make the trajectories more difficult for the participants than the model ., One might argue that these types of trajectories are less likely to be performed in everyday life and therefore are more difficult to find through exploration 44 , 45 ., Alternatively , such ‘two-direction’ movements may be more difficult to execute ., To improve the model’s performance , future work could utilise a more sensitive measure of motor noise by obtaining a curvature and direction noise measurement for each of the trajectories examined ., Variability in movement is a fundamental component in motor behaviour ., It is caused by numerous factors including planning , sensory and neuromuscular noise 46 ., Researchers often categorise variability into two sources: exploration and motor noise ., Exploration represents the variability which results from ‘intentional’ exploration of different actions 15 ., While motor noise represents the variability observed when attempting to repeat a single action 24 ., Previous work has examined the differential role of exploration and motor noise in motor lea

Introduction, Results, Discussion, Methods and models

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world ., Recently , a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems ., If so , participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task ., To investigate this question , we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor ( execution ) noise ., We also collected an independent measurement of each participant’s level of motor noise ., Our analysis showed that explorative motor learning and decision-making could be modelled as the ( approximately ) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing ., The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement ., This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise , and raises interesting questions regarding the neural origin of explorative motor learning .

Until recently , motor learning was viewed as an automatic process that was independent , and even in conflict with higher-level cognitive processes such as decision-making ., However , it is now thought that decision-making forms an integral part of motor learning ., To further examine the relationship between decision-making and motor learning , we asked whether explorative motor learning could be considered a decision-making task that was adjusted for motor noise ., We studied human performance in an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor ( execution ) noise ., In addition , we independently measured each participant’s level of motor noise ., Crucially , with a computational model , we were able to predict participant explorative motor learning by using parameters estimated from the decision-making task and the separate motor noise task ., This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise , and reinforces the view that the mechanisms which control decision-making and motor behaviour are highly integrated .

learning, medicine and health sciences, markov models, decision making, social sciences, neuroscience, learning and memory, cognitive psychology, mathematics, statistics (mathematics), cognition, musculoskeletal system, learning curves, behavior, human learning, probability theory, psychology, anatomy, biology and life sciences, physical sciences, cognitive science, markov processes

journal.pntd.0006337

Are the London Declaration’s 2020 goals sufficient to control Chagas disease?: Modeling scenarios for the Yucatan Peninsula

While the World Health Organization’s ( WHO ) London Declaration on Neglected Tropical Diseases has proposed 2020 goals of “100% of countries certified with no intradomiciliary transmission” , “100% of countries with certification of transfusional transmission interrupted” , and “100% of countries with control of congenital transmission” regarding the three main forms of Chagas disease transmission in Latin America1 , the question remains: what will be the impact of achieving these goals to varying degrees be on Chagas disease ?, Interruption of domestic transmission ( often measured by infections in children under 5 years of age ) is thought to play a key role in controlling Chagas disease ( i . e . , reduction in Chagas disease burden ) , which is caused by the protozoan parasite Trypanosoma cruzi ., 2–5 While previous studies have tried to elucidate the mechanisms of transmission or evaluate particular interventions6–14 , none to our knowledge have specifically tried to evaluate the impact of achieving the 2020 goals ., In fact , many existing studies preceded the formulation and announcement of the goals ., Moreover , not all locations may be able to achieve the 2020 goal , which does not necessarily mean aspiring to them is not worthwhile ., Some regions have yet to implement policies or mandate control programs1 ( e . g . , Mexico has no national control program4 ) , while other regions have programs that are not consistent from year-to-year and region-to-region ( e . g . , geographic variations in control activities in Ecuador15 ) ., Additionally , low attendance to perinatal care can hinder adequate diagnosis and treatment16 of pregnant women and infants , and compliance with universal screening of blood donors is not always 100% . 17, Furthermore , Chagas policies may be thwarted by decentralization ( i . e . , movement of authority from a central to a local government ) . 1 , 18, Therefore , knowing the impact of partially achieving the goals to varying degrees would be helpful ., Really assessing the potential impact of achieving the 2020 goals would need a computational model that incorporates all the complexities ., For example , a model would need to incorporate all the other relevant routes of transmission ( e . g . , vectorial , tranfusional , and congenital2 , 3 , 19 ) to help determine how much disease would persist if vectorial transmission were interrupted ., It also should include various vector habitats ( e . g . , domestic , peridomestic , and sylvatic ) to help determine the impact of reinfestation ., The Yucatan in Mexico can serve a good sample location as Mexico has not received certification , this region has some of the highest levels of Chagas in the country , and its main vector has more than one habitat ( i . e . , domestic , peridomestic , and sylvatic ) which allows us to capture re-infestation dynamics that may thwart the 2020 goals ., Therefore , our team developed a dynamic model of T . cruzi transmission among vectors ( Triatoma dimidiata ) and human and non-human hosts in Yucatan , Mexico and evaluated different levels of achieving the three transmission related 2020 goals on Chagas disease prevalence and number of new acute human cases ., We developed a deterministic compartmental model ( Fig, 1 ) using Python ( Python Software Foundation , Wilmington , DE ) to represent vector and host populations involved in T . cruzi transmission and included triatomines , human hosts , non-human hosts ( i . e . , dogs ) , and dead-end hosts ( i . e . , chickens ) to simulate vector-borne transmission between these populations in both domestic and peridomestic habitats , as well as congenital and transfusion/organ transplantation transmission ., The S1 Text provides additional model details ( including equations representing transitions between compartments ) ., The model ran in monthly time steps ( i . e . , t = 1 month or 30 days ) and was simulated across a 50-year period ., During each time step , probabilities and rates determined the number of individuals in each compartment ., Triatomine bugs could be susceptible ( not infected with T . cruzi and able to become infected ) or infectious ( infected with T . cruzi and able to transmit to vertebrate hosts upon biting ) ., Upon feeding on an infectious host ( human and viable non-human ) , a susceptible bug had a probability of becoming infected with T . cruzi , conditional on the disease state of the host ., The number of triatomine bugs ( NV ) in the model was determined from the carrying capacity , or the number of bugs sustainable in each habitat ., The number of susceptible triatomines entering the domestic or peridomestic population was dependent on the vector birth rate , carrying capacity , and number of triatomines in each habitat ( S1 Text ) ., Each member of the human population ( NH ) could be in any of the following mutually exclusive disease states ( Fig 1 ) : susceptible ( not infected with T . cruzi and able to become infected ) , acute Chagas disease ( infected with T . cruzi and able to transmit , exhibiting mild and nonspecific symptoms , but in some cases can show specific symptoms such as Romaña’s sign or can be serious and life-threatening , and having microscopically detectable parasitemia for 6 to 8 weeks19 ) , indeterminate Chagas disease ( infected with T . cruzi , able to transmit , but showing no symptoms , i . e . , asymptomatic ) , and symptomatic chronic Chagas disease ( infected with T . cruzi , able to transmit , and showing symptoms of chronic disease such as cardiomyopathy and/or megaviscera ) ., Upon a feeding contact by an infectious triatomine , a susceptible human had a probability of becoming infected with T . cruzi via contamination with bug feces during or immediately after the feeding ., This is represented in the vector-borne force of infection ( S1 Text ) ., Based on the clinical progression of disease in humans2 , 19 , all new infections start in the acute state ., Pregnant women had a probability of transmitting Chagas to their infants upon birth , with newborns becoming infected based on the congenital force of infection ( S1 Text ) ., Additionally , a proportion of humans receiving a blood transfusion or organ transplant had a probability of becoming infected with T . cruzi , based on the transfusion force of infection ( S1 Text ) ., We assumed that once infectious , persons were considered always infectious in the absence of treatment ., Those in the acute and symptomatic chronic states of disease had probabilities of Chagas-related mortality ., Dogs ( ND ) served as reservoir hosts for T . cruzi and could be either susceptible or infected , with a susceptible dog becoming infected upon the bite of an infected vector at a rate depending on the force of infection ( S1 Text ) ., Dogs were considered competent transmitters of T . cruzi ( i . e . , susceptible triatomines could become infected upon biting an infected dog ) ., Chickens ( NC ) served as dead end hosts and could not transmit T . cruzi back to vectors , as they are unable to become infected with T . cruzi . 20, Our model included transmission in both domestic and peridomestic habitats , which vary by vector-host contact rates , and allowed for the movement of triatomines between them ( e . g . , re-infestation ) ., Vectorial transmission in our model was governed by the vectorial force of infection ( S1 Text ) ., Consistent with other models of vector-borne diseases21 , this is a function of: ( 1 ) the triatomine biting rate , ( 2 ) the triatomine feeding proportion for each host type in each habitat , ( 3 ) the probability of transmission from vector to susceptible host , ( 4 ) the probability of transmission from infected host to susceptible bug , ( 5 ) the proportion of hosts in each habitat , and ( 6 ) the number of hosts in each habitat ., Transmission probabilities from vector to host varied with host species , while triatomine biting rates were assumed to be constant ., Despite having one of the greatest burdens of Chagas disease worldwide , Mexico has not yet undertaken a national vector control program4 and only started mandatory serological screening in 201217 ., In 2010 , approximately an estimated 876 , 458 people were infected and 23 . 5 million were at risk for infection22 , with 88% of the population potentially exposed to at least one competent vector species23 ., These cases result in an estimated $32 . 3 billion in societal costs over their lifetime . 24, Yucatan State has one of the highest Chagas burdens in Mexico ., Chagas is endemic throughout the peninsula , with 12–25 cases reported per 100 , 000 population over the last several years . 25 , 26, Additionally , the Yucatan is home to only one main vector species , Triatoma dimidiata , which can be found in the domestic , peridomestic , and sylvatic environments , and typically infests houses on a seasonal basis with limited ability to colonize . 27 , 28, Thus , the domestic and peridomestic transmission cycle are fueled by the sylvatic transmission of T . cruzi and house invasion by infected bugs ., Currently , there are no vector intervention or control strategies in place in the Yucatan ., Thus , this endemic setting , with no programs currently in place and home to a vector that can reinfest homes , is an ideal location to fully estimate the impact of the 2020 goal ., Our model was populated and calibrated to simulate T . cruzi transmission in a rural village ( NH = 2 , 000 ) in Yucatan , Mexico ., Table 1 shows our key input parameter values and sources ., The number of dogs ( ND = 617 ) was based on the ratio of dogs to humans29 , 30 , while the number of chickens ( NC = 250 ) was based on the proportion of households with chickens and the number of persons per household28 ., The carrying capacity was set at 50 bugs per person ( consistent with previous work9 ) , yielding a T . dimidiata population size of 99 , 885 ., Our model was calibrated to assume a median T . cruzi prevalence value of 32 . 5% in T . dimidiata27 , 31–38 , and seroprevalence estimates of 1 . 85% in humans4 , 38–47 , and 14 . 58% in dogs31 , 41 , 42 , 48–51 ., As transmission probabilities ( i . e . , from vectors to humans and dogs , and from dogs and humans to triatomines ) and T . dimidiata feeding proportions across host species are highly variable and/or not well defined in the literature , these parameters were calibrated to available empirical data for the Yucatan ( Table 1 and S1 Text ) ., We evaluated the impact of interrupting vector-borne transmission in the domestic setting , and congenital and transfusional transmission to varying degrees to achieve the 2020 goals ., We modeled each as an attenuation of the force of infection ( S1 Text ) ., For vector-borne transmission , we modeled this as a reduction in the contact rate between humans and triatomines only in the domestic setting; that is , we attenuated the force of infection by a specified amount and account for the proportion of transmission due to domestic vectors ( S1 Text ) ., As the goal is to evaluate the 2020 goals and not the way in which these are achieved , these reductions served as a proxy to represent a variety of ways transmission could be interrupted in the domestic settings ( e . g . , housing improvements , indoor residual spraying , bed nets ) and for congenital transmission ( e . g . , screening and treatment ) ., Sensitivity analyses evaluated the degree to which transmission was interrupted for the three types ( 0% to 100% ) ., Additional sensitivity analyses further evaluated each calibrated parameter at their low and high calibrated values ( Table 1 ) ., We also varied the movement of triatomines to and from the peridomestic and domestic settings ( ±50% ) , as this can vary with many factors . 13 , 52, Model outcomes are the number of new/acute human cases ( which reflects transmission ) and the overall prevalence of human cases ( which reflects the general disease burden ) ., With no interruption in any form of transmission , T . cruzi prevalence in humans remained stable at 1 . 8% , with 1 . 0 new acute case each year ( i . e . , transmission event ) , so that at any given point in time there were 1 . 5 acute cases , 30 . 6 indeterminate cases , and 4 . 6 chronic Chagas disease cases in the population of 2 , 000 persons ( Table 2 ) ., T . cruzi prevalence in triatomines remained stable at 23 . 5% and 48 . 4% for those in the domestic and peridomestic habitat , respectively ( Fig 2B and 2C ) , while the prevalence of T . cruzi in dogs remained stable at 8 . 8% ( Fig 2D ) ., Fig 3A shows the maginitude of impact of each of various parameters on the resulting number of new monthly acute infections ., Movement of triatomines between habitats had the largest impact , resulting in 0 . 064 to 0 . 089 transmission events per month ( for -50% to +50% of the baseline value ) ; followed by transmission from vector to dog and vector to humans ., Varying the three most impactful parameters ( the transmission from vector to dog , transmission from vector to human , and the proportion at which triatomine feed on humans ) to their extreme values resulted 0 . 72 to 1 . 18 new acute cases each year and a prevalence of 17 . 7% to 26 . 0% in domestic triatomines and of 35 . 9% to 52 . 6% in periodomestic triatomines ., Fig 2 and Table 2 show the impact of only domestic vectorial interruption to varying degrees over time on T . cruzi transmission events , prevalence , and the number of acute , indeterminate , and chronic Chagas disease cases ., The largest reductions in prevalence were seen within the first year of reducing vector-host contact with the impact becoming stable by year five , regardless of the degree of reduction ., Fig 2A shows the reduction in total T . cruzi transmission events in humans ., Over the course of one year , a 50% to 100% reduction in domestic vector-host contact resulted in a 42 . 8% to 82 . 5% relative reduction in the number of new acute Chagas cases; this increased to a relative reduction of 46 . 1% to 83 . 0% over five years ( Fig 2A and Table 2 ) ., Even with a sharp reduction in the total number of new transmission events , the number of Chagas disease cases remained relatively stable over time , with a decrease in the number of indeterminate and chronic disease cases taking approximately 12 years to manifest ( Table 2 ) ., Fig 3B shows the impact of varying parameters on new acute cases with a 100% reduction in domestic vector-host ., As shown , the rank order of parameters change from the no interruption scenario so that transmission from vector to dog , congenital transmission , and transmission from vector to humans had the largest impact ., Varying transmission from vector to dog , transmission from vector to human , and the proportion at which triatomines feed on humans to their extremes resulted 0 . 08 to 0 . 13 new acute cases each year ., Among triatomines ( Fig 2B and 2C ) , T . cruzi prevalence among domestic triatomines experienced relative decreases of 16 . 2% , 21 . 3% , 25 . 8% , and 27 . 7% compared to no reduction for vector-host reductions of 50% , 70% , 90% , and 100% respectively , after five years , while peridomestic triatomines garnered relative reductions up to 3 . 9% ., After 50 years , the prevalence among triatomines ranged from 10 . 9% to 17 . 4% in the domestic ( 15 . 8% for base assumptions , Fig 2B ) and from 30 . 4% to 48 . 6% in the peridomestic ( 44 . 2% for base assumptions , Fig 2C ) settings under all conditions tested with a 100% vector-host reduction ., The prevalence of T . cruzi among dogs decreased to 8 . 7% at 5 years when attenuating domestic transmission by 50% to 100% ( Fig 2D ) ., The differences between scenarios in Fig 2 and Table 2 show that gains can be achieved by increasing the degree of vector-host interruption at different points in time ., For example , increasing from 50% to 100% in year 3 would result in 2 . 0 total transmission events by year 5 compared to 2 . 7 events per 2 , 000 persons ., After five years , only controlling congenital transmission led to a 2 . 4% ( 30% reduction ) to 8 . 1% ( 100% reduction ) relative reduction in the total number of new acute cases ., This resulted in 0 . 1 to 0 . 4 fewer total transmission events , respectively ., However , controlling only congenital transmission had very little impact on T . cruzi prevalence in triatomines and dogs , with maximum relative reductions of 0 . 9% , 0 . 2% , and 0 . 1% in domestic triatomine , peridomestic triatomine , and dog seroprevalences , respectively , after five years ., Reducing only transfusional transmission had minimal impact on the number of new acute cases and no impact on T . cruzi prevalence in any population ., The relative reduction in the total number of new acute cases ranged from 0 . 1% to 0 . 3% ( 30% to 100% reduction ) over five years ., Fig 4 and Table 3 show the impact of reducing all three transmission routes to varying degrees ., After five years , there are two to five fewer Chagas cases per 2 , 000 persons , varying with the degree of interruption ( Table 3 ) ; however , differences increase over time , with 25 fewer cases given 100% interruption of all three transmission routes ., Stopping all three forms of transmission resulted in 0 . 2 transmission events over the first year and 0 . 5 over five years ( compared to 1 . 0 and 5 . 0 with no interruption over one and five years , respectively ) ; interrupting all forms by 30% resulted in 3 . 4 total events over five years ., This corresponds to a 32% to 90% relative reduction ( 30% to 100% interruption in all forms ) in new acute cases over five years ., Interrupting all three transmission routes by 100% resulted in a human prevalence of 0 . 6% after 50 years ., Transmission from vector to dog ( ranging from 0 . 006 to 0 . 008 transmission events per month ) , followed by triatomine movements between habitats , and transmission from vector to human had the largest impact on the number of new transmission events ( Fig 3C ) ., Again , varying the three parameters most impactful with no transmission resulted in a range of 0 . 06 to 0 . 10 new acute cases per year ( compared to 0 . 08 per year when held at middle values ) ., Relative reductions in domestic triatomine prevalence over five years ranged from 10 . 5% to 27 . 8% ( 30% reduction in all types to 100% reduction in all types ) ., The differences between scenarios show the gains that can be achieved by increasing the degree of vector-host interruption at different points in time ., Greater achievements could be made by increasing vector-host interruption alone than by increasing control of congenital and transfusional together ( i . e . , greater gains increasing vectorial from 30% to 70% than increasing congenital and transfusional from 30% to 90% ) ., For example , if congenital and transfusional transmission were interrupted by 90% , further reducing vector-host contact from 70% to 90% in year two for two years would result in 1 . 0 total transmission event vs . 1 . 3 per 2 , 000 persons ., All models are simplifications of real life and as such cannot represent every possible event or outcome ., Our current model is deterministic in nature and does not include the full heterogeneity possible for Chagas disease transitions between states ., Our model inputs were fit to disparate data of varying quality yet can be refined as new data become available ., As Chagas disease is underdiagnosed and underreported , our estimates for T . cruzi seroprevalence in the absence of control measures are subject to limitations; however , we used the best available data for these parameters ., We assumed a robust interruption in transmission that did not wane over time and assumed a constant reduction ., While our model allows for differential infectiousness of humans in the three disease states , we assumed the same value for both indeterminate and chronic patients , as evidence suggests these patients have comparable levels of low parasitemia . 61, We also did not consider oral Chagas disease transmission nor account for seasonal effects ., For simplicity , our model also does not include other biological states , transmission types , and outcomes for dogs nor other synanthropic wildlife ( however , dogs and chickens serve as reservoir and dead-end populations , respectively ) ., Given lack of data on transmission rates for parameterization , we did not further stratify the infection state in dogs to include acute and chronic disease , nor did we consider oral transmission ., Likewise , we did not include the impact of predation rate on vectors by dogs , given we modeled a stable bug population ., While there is a possibility that excluding these factors may affect results , most likely they would not as we calibrate the simulation to a certain prevalence in dogs and this would be maintained regardless of the number of dog disease states and transmission types ., This prevalence is maintained by the vector to dog transmission rate , thus highlighting its importance to our model; however , we note that our resulting calibrated value for the infectiousness of dogs was lower than values reported in the literature . 62, Our future work can further incorporate these factors ., Our results suggest that achieving the 2020 Sustainable Development Goals of 100% interruption and control of domestic sectorial , congenital , and transfusional transmission in the Yucatan and other regions with similar epidemiological conditions fall short of completely interrupting T . cruzi transmission , despite considerably reducing the number of new Chagas cases ., Thus , complementary approaches and other prevention and control measures ( e . g . , peridomestic vector control , vaccines and increased healthcare utilization ) are needed to fully interrupt Chagas disease transmission ., Even if these goals are missed , most gains are achieved within the first year of implementation , thus the goals should be actively pursued .

Introduction, Methods, Results, Discussion

The 2020 Sustainable Development goals call for 100% certified interruption or control of the three main forms of Chagas disease transmission in Latin America ., However , how much will achieving these goals to varying degrees control Chagas disease; what is the potential impact of missing these goals and if they are achieved , what may be left ?, We developed a compartmental simulation model that represents the triatomine , human host , and non-human host populations and vector-borne , congenital , and transfusional T . cruzi transmission between them in the domestic and peridomestic settings to evaluate the impact of limiting transmission in a 2 , 000 person virtual village in Yucatan , Mexico ., Interruption of domestic vectorial transmission had the largest impact on T . cruzi transmission and prevalence in all populations ., Most of the gains were achieved within the first few years ., Controlling vectorial transmission resulted in a 46 . 1–83 . 0% relative reduction in the number of new acute Chagas cases for a 50–100% interruption in domestic vector-host contact ., Only controlling congenital transmission led to a 2 . 4–8 . 1% ( 30–100% interruption ) relative reduction in the total number of new acute cases and reducing only transfusional transmission led to a 0 . 1–0 . 3% ( 30–100% reduction ) ., Stopping all three forms of transmission resulted in 0 . 5 total transmission events over five years ( compared to 5 . 0 with no interruption ) ; interrupting all forms by 30% resulted in 3 . 4 events over five years per 2 , 000 persons ., While reducing domestic vectorial , congenital , and transfusional transmission can successfully reduce transmission to humans ( up to 82% in one year ) , achieving the 2020 goals would still result in 0 . 5 new acute cases per 2 , 000 over five years ., Even if the goals are missed , major gains can be achieved within the first few years ., Interrupting transmission should be combined with other efforts such as a vaccine or improved access to care , especially for the population of already infected individuals .

While World Health Organization’s ( WHO ) London Declaration on Neglected Tropical Diseases has proposed 2020 goals of 100% certified interruption or control of the three main forms of Chagas disease transmission ( vectorial , congenital , and transfusional ) in Latin America , the impact of achieving and/or missing these goals is not known ., Policy makers need to know the potential impact of missing these goals on disease incidence and prevalence ., If they are achieved , decision makers need to know what may be left to adequately inform policies and the future for controlling Chagas disease ., Our compartmental simulation model suggests that achieving the 2020 goals would still result in 25 new acute cases per 100 , 000 over five years ., However , substantial gains could still be garnered within one year by interrupting transmission to varying degrees , so the goals should still be pursued .

animal types, medicine and health sciences, domestic animals, tropical diseases, vector-borne diseases, vertebrates, parasitic diseases, parasitic protozoans, animals, mammals, dogs, pets and companion animals, protozoans, neglected tropical diseases, infectious disease control, zoology, infectious diseases, protozoan infections, disease vectors, trypanosoma cruzi, trypanosoma, chagas disease, eukaryota, biology and life sciences, species interactions, amniotes, organisms

journal.pntd.0003077

Structural and Functional Analysis of a Platelet-Activating Lysophosphatidylcholine of Trypanosoma cruzi

Trypanosoma cruzi is the etiological agent of Chagas disease , which is associated with myocarditis and vasculitis , accompanied by an increase in inflammatory mediators , such as cytokines , chemokines , phospholipids , and glycolipids 1–3 ., There is also an increase in platelet aggregation , focal ischemia , and myonecrosis in both acute and chronic stages of the disease 2 , 4 , 5 ., Most of the chronic cases are linked with debilitating cardiomyopathy , which is responsible for more deaths than any other parasitic disease in Latin America 1 , 6 ., Endemic Chagas disease affects eight to ten million people in 21 countries in Latin America 7 ., Chagas disease is also becoming a global health problem because of the migration of unconsciously T . cruzi-infected individuals from Latin American countries to other regions of the world ., Several thousands of people in the United States , Canada , various European countries , Australia , and Japan are chronically infected with T . cruzi 7 ., T . cruzi has a complex life cycle , with two morphophysiological stages within a triatomine bug and two in a mammalian host ., Infectious metacyclic trypomastigotes can be expelled with the insects excreta during a bloodmeal , reaching the host bloodstream through the bite wound or exposed ocular or oral mucosa ., In addition , the insect can acquire the parasite during blood feeding from an infected individual and continue the cycle 8 ., Blood transfusion , organ transplantation , congenital transmission and food and fluid contamination are other significant ways of transmitting this disease 9 , 10 ., In general , lipid mediators 11–16 and specifically , lysophosphatidylcholine ( LPC ) 17 , 18 , have been implicated in experimental models of Chagas disease ., LPC is present in the saliva of at least one of the insect vectors of Chagas disease , the hemipteran Rhodnius prolixus , where it acts as an anti-hemostatic molecule and immunomodulator of T . cruzi infection in a mammalian model 17–19 ., LPC ( 1-acyl-2-hydroxy-sn-glycero-3-phosphorylcholine ) is a major plasma phospholipid of oxidized low-density lipoproteins ( Ox-LDL ) , albumin and other carrier proteins , being a critical factor in the inflammatory processes and the atherogenic activity of Ox-LDL 20–22 ., LPC is an intracellular modulator that activates several second messengers , controlling important biological activities , such as cellular proliferation and differentiation , transcription of adhesion molecules and growth factors in endothelial cells , as well as the transportation of fatty acids , choline , and phosphatidylglycerol between tissues 20–24 ., The biological activities of LPC are usually mediated by G protein-coupled receptors ( GPCRs ) , such as G2A , GPR4 , and the receptors for prostacyclin ( IP ) , thromboxane A2 ( TXA2 ) ( TP ) , and platelet-activating factor ( PAF ) ( PAFR ) 24–33 ., Specifically , LPC species are capable of eliciting different cellular activities depending on the length and degree of unsaturation of its sole acyl-chain 30 , 34 , 35 ., Trypanosomatid parasites ( e . g . , T . brucei , Leishmania spp . , and T . cruzi ) are known to synthesize phosphatidylcholine ( PC ) and LPC ., Over 50% of the total lipids shed to the culture medium by T . cruzi were identified as PC and LPC 36 ., These molecules were also found in Leishmania 37 , 38 , African trypanosomes 39 , and in the malaria parasite , Plasmodium falciparum 40 ., To the best of our knowledge , however , the chemical structures of LPC species synthesized by T . cruzi have not been defined to date ., Platelet-activating factor ( 1-O-alkyl-2-acetyl-sn-glycero-3-phosphocholine; PAF ) is structurally very similar to LPC 41 ., PAF exhibits potent biological activity and is synthesized by a wide variety of cells , including neutrophils , platelets , macrophages , and lymphocytes 42 ., PAF induces numerous physiological and pathophysiological effects , such as cellular differentiation , inflammation , and allergy , through the activation of specific GPCRs with seven transmembrane helices 25 , 43 ., We have previously shown that T . cruzi synthesizes a lipid with platelet-aggregating properties similar to PAF 14 ., Preliminary structural analysis by chemical and enzymatic treatment indicated that the T . cruzi PAF-like lipid , metabolically labeled with 14C-acetate , was labile to mild-alkaline or hydrofluoric acid hydrolysis , suggesting a molecule containing a glycerolipid moiety with at least one acyl chain and a phosphate group 14 ., However , the detailed structure of the T . cruzi PAF-like lipid remains elusive ., Here , we describe the identification and bioactivity of the as-yet elusive T . cruzi PAF-like molecule ., We use a novel approach for the enrichment of this and other closely related lysophospholipids , followed by tandem mass spectrometry ( MSn ) to provide ample structural information ., Moreover , we constructed a 3-D molecular model of PAFR and used molecular docking to predict the interactions of the T . cruzi PAF-like molecule and other lysophospholipids with the receptor ., Rabbit platelets used in this study were obtained following the guidelines of the Committee for Evaluation of Animal Use for Research of the Federal University of Rio de Janeiro ( CAUAP-UFRJ ) and the NIH Guide for the Care and Use of Laboratory Animals ., The vertebrate animal protocol was approved by CAUAP-UFRJ under registry number IBQM011 ., Synthetic C16:0- , C18:0- , C18:1 ( Δ9 ) - , and C22:6-LPC , and C16:0-PAF were purchased from Avanti Polar Lipids ( Alabaster , AL ) ., The competitive PAF antagonist WEB 2086 ( 4-3-4- ( 2-chlorophenyl ) -9-methyl-6h-thieno3 , 2-f1 , 2 , 4triazolo4 , 3-adiazepin-2-yl-1-oxopropylmorpholine ) was kindly provided by Dr . H . Heurer from Boehringer Ingelheim ( Ingelheim , Germany ) ., Otherwise indicated , all other reagents and solvents used here were of analytical , HPLC , or mass spectrometric grade from Sigma-Aldrich ( St . Louis , MO ) ., All T . cruzi life cycle stages or forms were obtained from the Y strain 44 ., Epimastigote forms ( Epis ) were maintained by weekly transfers using liver infusion tryptose ( LIT ) medium 45 , supplemented with 0 . 002% hemin and 10% heat-inactivated fetal calf serum ( FCS; Hyclone , heat-inactivated at 56°C for 30 min ) at 28°C ., Metacyclic trypomastigote forms ( Metas ) were obtained by spontaneous axenic differentiation of Epis at 28°C , followed by their purification using ion-exchange chromatography 46 , 47 ., Mammalian tissue culture-derived trypomastigotes ( TCTs ) were obtained from the supernatants of 5 to 6 days old T . cruzi-infected LLC-MK2 cells ( American Type Culture Collection , Rockville , MD ) , maintained in RPMI-1640 medium supplemented with 2% FCS at 37°C in a 5% humidified CO2 atmosphere 48 ., Intracellular amastigotes ( ICAs ) were obtained as described 49 ., Briefly , infected monolayers of LLC-MK2 cells were gently detached by scraping ( BD Falcon cell scraper , BD Biosciences ) and resuspended in PBS supplemented with 10% FCS ., Mammalian cells were disrupted by passage through a 27-gauge needle ( BD , Becton and Dickinson & Co . ) ., ICA forms were separated from the cell debris by centrifugation ( 800× g for 5 min at 4°C ) ., The supernatant was then harvested and passed through a DE-52 column and parasites were incubated for 2 h at 37°C in a humidified 5% CO2 atmosphere , after which the parasites were again passed through a DE-52 column , from which they were harvested and stored ., For the viability testing of all parasite forms , cells were resuspended in a Trypan Blue solution and counted in a Neubauer chamber 50 ., In this study , all experiments were performed using parasites that were harvested by centrifugation and washed three times with PBS before use , unless otherwise specified ., All parasite forms were counted and then frozen in liquid nitrogen prior to use ., Frozen pellets derived from Epis , Metas , ICAs , and TCTs ( 2×109 cells each ) , were suspended in 1 . 6-ml ice-cold HPLC-grade water and transferred to 13×100-mm Pyrex culture tubes with polytetrafluoroethylene ( PTFE ) -lined screw caps ., HPLC-grade chloroform and methanol were added to each vial , giving a final ratio of chloroform/methanol/water ( C/M/W ) of 1∶2∶0 . 8 ( v/v/v ) ., The samples were mixed vigorously using a vortex for 2 min and then centrifuged for 15 min at 1 , 800× g at room temperature ., After centrifugation , the supernatants were transferred to PTFE-lined Pyrex glass test tubes and the pellets were dried under a constant flow of N2 stream ., The dry pellets were then extracted three times with C/M ( 2∶1 , v/v ) and twice with C/M/W ( 1∶2∶0 . 8 , v/v/v ) ., After extraction , the supernatants were pooled together and dried before being subjected to Folchs partition 51 ., To this end , samples were first dissolved in C/M/W ( 4∶2∶1 . 5 , v/v/v ) and then mixed vigorously for 5 min using a vortex and finally centrifuged for 15 min at 1 , 800× g at room temperature ., After centrifugation , the lower ( organic ) and upper ( aqueous ) phases were separated in PTFE-lined Pyrex glass test tubes ., The Folch lower phase was then washed two times with a freshly prepared upper phase , dried under N2 steam , and stored at −70°C until use ., Phospholipids derived from the lower phase of the Folchs partition , as described above , were purified from other classes of lipids using a three-step SPE protocol 52 ., Briefly , 100 mg silica gel ( Merck , grade 7754 , high purity , 70–230 mesh , 60 Å ) were packed into borosilicate glass Pasteur pipettes ( 5 ¾″ , Fisher Scientific ) using Pyrex glass fiber wool ( 8-µm pore size , Sigma-Aldrich ) as a sieve ., The column was sequentially conditioned with 4 ml each methanol , acetone , and chloroform ., Dried Folch lower phase samples from all T . cruzi life stages were redisolved in 3 ml chloroform and a third of each sample was added to the column ., Lipids were sequentially eluted with 4 ml chloroform ( neutral lipids ) , acetone ( glycolipids ) , and methanol ( phospholipids and free fatty acids ) ., Each fraction was collected into a 7-ml amber glass vial with PTFE-lined screw top ( SUPELCO , Sigma-Aldrich ) ., All samples were immediately dried under a constant flow of N2 stream and stored at −70°C until use ., With the purpose of enriching the putative PAF-like molecule from a complex T . cruzi phospholipid mixture , a novel method was developed using perfusion chromatography 53 ., Briefly , fifty microliters of a suspension of 40 mg/ml POROS R1 50 ( polystyrene/divinylbenzene , with similar binding strength as C4 supports ) beads ( Applied Biosystems , 50-µm diameter ) in HPLC-grade n-propanol ( Honeywell , Burdick & Jackson , Radnor , PA ) were packed into a 200-µl sterile micropipette tip ( Axygen , Corning Life Sciences , Union City , CA ) ., Pyrex glass fiber wool ( 8-µm pore size , Sigma-Aldrich ) was used as sieve ., The POROS R1 mini-column was washed twice with 100 µl HPLC-grade methanol and then conditioned with an n-propanol/water gradient ( from 50% to 0% , in 5% increments ) ., Each step of the gradient was performed with 100 µl solvent , with the exception of the last step ( 0% n-propanol ) , which was performed twice with 100 µl HPLC-grade water ., Then , either the mixture of lipid standards ( 100 pmol C16:0-lyso-PC ( C16:0-LPC ) , C16:0-lyso-PAF ( C16:0-LPAF ) , C16:0-PAF , and C18:0/C18:1-diacyl-PC ) or T . cruzi phospholipids derived from the SPE procedure were suspended in HPLC-grade water , sonicated for 10 min in a bath sonicator , and added to the column ., Both the standards and the phospholipid extracts were eluted using a 0%–50% n-propanol gradient in 5% n-propanol increments ., Each fraction was stored in Axygen 2-ml microcentrifuge tube at −70°C until further use ., All fractions derived from the POROS R1 mini-column purification , as well as the fractions obtained prior to this last procedure ( namely , the lower Folch and methanol phase ) , were dissolved in MS-grade methanol containing 5 mM LiOH , as indicated ., Samples were directly injected ( at 300 nl/min ) by chip-based infusion using a TriVersa NanoMate nanoelectrospray source ( Advion , Ithaca , NY ) , into an LTQXL ESI-linear ion trap-MS ( ESI-LIT-MS ) ( Thermo Fisher Scientific ) , in positive-ion mode ., The source voltage was set at 0 . 01 kV and current at 0 . 03 µA; capillary voltage and temperature were 36 V and 150°C , respectively; and tube lens voltage was set at 145 V . Select ions were subjected to sequential tandem fragmentation ( MSn ) by collision-induced dissociation ( CID ) ., Full-scan ( MS ) spectra were collected at the 400–1000 m/z range ., Tandem mass fragmentation was carried out using normalized collision energies of 35 , 40 , and 45 for MS2 , MS3 , and MS4 , respectively ., The resulting spectra were compared to the aforementioned phospholipid standards , as well as to previously described results ., The location of the acyl chain on T . cruzi LPC species was determined by MS2 and M3 analysis , essentially as described by Hsu et al . 54 ., Briefly , sn-1 and sn-2 C18:1-LPC regioisomer standards were generated by treatment of 18:1 ( Δ9-cis ) -PC ( 1 , 2-dioleoyl-sn-glycero-3-phosphocholine , catalog # 850375 , Avanti Polar Lipids ) with either PLA2 ( from porcine pancreas , catalog # P6534 , Sigma-Aldrich ) or PLA1 ( from Thermomyces lanuginosus , catalog # L3295 , Sigma-Aldrich ) ., For PLA1 treatment , one mg of diacyl-PC standards were dried under N2 stream , redisolved in 200 µL of reaction buffer ( 50 mM Tris-HCl , 2 mM CaCl2 , 140 mM NaCl , pH 8 . 0 ) and sonicated for 30 min ., Afterwards , the samples were incubated at 37°C for 2 h in the presence of 12 units PLA1 ., The reaction was interrupted by the addition of 1 . 5 mL chloroform , followed by vortexing for 1 min ., The resulting LPC species present in the organic phase of the mixtures were then purified by SPE , following the protocol described above ., Finally , LPCs were recovered in the methanolic phase of the SPE column ., PLA2 treatment was performed following the same procedure steps used for PLA1 treatment but using a different reaction buffer ( 100 mM Tris-HCl , 5 mM CaCl2 , 100 mM NaCl , pH 8 . 0 ) and 5 units PLA2 55 ., The purified lipids ( methanolic phase of the SPE ) were analyzed by ESI-LIT-MS in 100% methanol containing 5 mM LiOH or 5 mM NaCl ., The MS analyses were performed as described above ., The assignment of the fatty acid position on the LPC ( sn-1 or sn-2 ) was performed by comparing the fragmentation pattern of standard sn-1 and sn-2 C18:1-LPCs with T . cruzi-derived 18:1-LPC samples ., The position of the double bond on fatty acid substituents was determined by MS4 , essentially as described by Hsu et al . 56 ., Briefly , sn-1 C18:1 ( Δ6-cis ) -LPC and sn-1 C18:1 ( Δ9-cis ) -LPC standards were generated by treatment of both 18:1 ( Δ6-cis ) -PC ( 1 , 2-dipetroselenoyl-sn-glycero-3-phosphocholine , catalog # 850374 , Avanti Polar Lipids ) and 18:1 ( Δ9-cis ) -PC ( 1 , 2-dioleoyl-sn-glycero-3-phosphocholine , catalog # 850375 , Avanti Polar Lipids ) with PLA2 ( from porcine pancreas , catalog # P6534 , Sigma-Aldrich ) , as described above ., LPC species were recovered from incubation mixtures and analyzed by ESI-LIT-MS in 100% methanol containing 5 mM LiOH , as described above ., Commercial C10:0-LPC ( 1-decanoyl-2-hydroxy-sn-glycero-3-phosphocholine , catalog # 855375 , Avanti Polar Lipids ) was used as an internal standard to quantify the most abundant LPC species found in T . cruzi samples ., Briefly , 18 nmoles of standard C10:0-LPC were added to parasite pellets ( 7×108 cells ) shortly before lipid extraction ., Lipid extraction was conducted on freshly prepared parasite pellets following the protocol described above ., Then , the Folch lower-phase fractions were analyzed by ESI-MS ( at the 400–1000 m/z range ) under the identical MS conditions used for the characterization of T . cruzi LPCs ., The amount of each LPC species was calculated using the formula: T . cruzi LPC peak intensity/C10:0-LPC peak intensity×concentration of C10:0-LPC ( 18 pmol/µl ) /MRRF , where , MRRF stands for the molar relative response factor of each LPC species to the C10:0-LPC standard ., The MRRF was calculated by dividing the intensity of the peak corresponding to a standard LPC ( C16:0-LPC , C18:0-LPC , or C18:1-LPC ) by the intensity of the peak corresponding to C10:0-LPC ( m/z 418 . 5 ) , when all molecules were in equimolar concentrations ., T . cruzi LPC species were also quantified in extracellular vesicles ( EV ) and EV-free supernatant or conditioned medium ( VF ) of Epis ( eV2 , eV16 , and eVF ) and Metas ( mV2 , mV16 , and mVF ) , obtained from Epi and Meta pellets ( 9×109 parasites each ) , as previously described 57 ., C10:0-LPC ( m/z 418 ) ( 18 nmoles per sample ) was used as an internal standard ., LPC species were extracted from Epi- and Meta-derived EVs , and respective total parasite pellets ( ePellet and mPellet ) as described above ., The Folch lower phase fractions were analyzed by ESI-LIT-MS as above ., C16:0-PAF and various synthetic ( C16:0- , C18:0- , C18:1- , and C22:6-LPC ) or purified ( C18:2-LPC ) LPC species were tested in a platelet aggregation assay 58 ., Rabbit blood platelets were prepared from blood collected with 5 mM EDTA as anticoagulant , isolated by centrifugation , washed and resuspended in a modified Tyrodes buffer , pH 7 . 4 , containing 2 mM CaCl2 , at a final concentration of 3–4×105 cells/µl in Tyrodes buffer ., Platelet aggregation experiments were performed with a Chronolog Aggregometer ( Havertown , PA , USA ) , with monitoring time of 5 min ., Rabbit platelets used in this investigation were obtained following the guidelines for animal experimentation of the USA National Institutes of Health and the experimental protocol received official approval of the Institutional Animal Care and Use Committee , Universidade Federal do Rio de Janeiro ., The amino acid sequence of the human PAF receptor ( PAFR , UniProtKB ID: P25105 ) was obtained from ExPASy server 59 ., The region between Asp10-Ser310 , part of PAFR sequence that includes all seven-transmembrane domains , was submitted to I-TASSER server , which combines threading and ab initio algorithms 60 , 61 ., The I-TASSER server , ranked as the best server in recent CASP7 and CASP8 experiments , builds protein models based on multiple-threading alignments by LOMETS program and iterative TASSER program simulations 61 , 62 ., In addition , MODELLER v9 . 10 program 63 , 64 ( http://salilab . org/modeller/ ) was used to add a disulfide bridge between Cys90-Cys173 and , subsequently , to refine the best I-TASSER model ., Thus , the final model was validated using three programs: PROCHECK 65 and ERRAT 66 , both at SAVES server ( http://nihserver . mbi . ucla . edu/SAVES_3/ ) and PROQM 67 ( available as a server at http://www . bioinfo . ifm . liu . se/services/ProQM/index . php ? about=proqm ) ., PROCHECK analyzes the stereochemical quality and ERRAT evaluates the non-bonded atomic interactions in the model structure , while PROQM uses a specific-scoring function for membrane protein , including GPCR , to assess local and global structural quality of the model ., The ligand structures ( C16:0-PAF , C16:0-LPC , C18:0-LPC , C18:1-LPC , and C18:2-LPC ) were built in the Spartan10 software ( Wavefunction , Inc . , Irvine , CA ) ., The docking of the ligands to the PAFR model binding site was performed using Molegro Virtual Docker ( MVD ) program ( CLC bio , Aarhus , Denmark ) , which uses a heuristic search algorithm that combines differential evolution with a cavity prediction algorithm ., The MolDock scoring function used is based on a modified piecewise linear potential ( PLP ) with new hydrogen bonding and electrostatic terms included ., Full description of the algorithm and its reliability compared to other common docking algorithm have been described 68 ., As no satisfactory cavities were found by cavity prediction algorithm using MVD , His248 ( a constituent residue of the binding pocket ) was set as center of searching space ., The search algorithm MolDock optimizer was used with a minimum of 50 runs and the parameter settings were: population size\u200a=\u200a200; maximum iteration\u200a=\u200a2000; scaling factor\u200a=\u200a0 . 50; offspring scheme\u200a=\u200ascheme 1; termination scheme\u200a=\u200avariance-based; crossover rate\u200a=\u200a0 . 90 ., Due to the stochastic nature of algorithm search , ten independent simulations per ligand were performed to predict the binding mode ., Consequently , the complexes with the lowest interaction energy were evaluated ., The interactions between PAFR and each ligand were analyzed using the ligand map algorithm , a standard algorithm in MVD program ., The usual threshold values for hydrogen bonds and steric interactions were used ., All figures of PAFR modeling and docking were edited using Visual Molecular Dynamics ( VMD ) program ( available for download at http://www . ks . uiuc . edu/Research/vmd/vmd-1 . 9 . 1/ ) ., Previous results from our group strongly indicated that T . cruzi synthesizes a phospholipid with platelet-aggregating activity similar to PAF 14 ., Thus far , however , the precise structure of this bioactive parasite-derived molecule remains unknown ., Aiming at the enrichment and characterization of the putative T . cruzi PAF-like phospholipid from a complex phospholipid mixture , we developed a fractionation protocol , which included solvent extraction and Folchs partition , followed by SPE and perfusion chromatography ( Fig . 1 ) ., Lipid fractions obtained after each step of purification were analyzed by ESI-LIT-MS in positive-ion mode ., Total-ion mapping ( TIM ) for the neutral loss of the trimethylamine group ( =\u200a59 a . m . u . ) was performed to promptly localize phosphocholine-containing phospholipids of all life-cycle stages of T . cruzi ( data not shown ) ., ESI-LIT-MS analysis of the Folch lower phase of the four parasite forms ( Epi , Meta , ICA , and TCT ) showed major phospholipid species at the 700–900 m/z range , except for the ICA form ( Fig . 2A ) ., Tandem MS ( MSn ) analysis of these lipid species revealed that , as expected , they were mostly diacyl-PC and sphingomyelin ( SM ) species ( data not shown , to be published elsewhere ) ., In contrast to other parasite forms , ICA is much richer in lipid species at the 500–600 m/z range , particularly m/z 526 , 528 , 530 , and 574 ( Fig . 2A ) ., Noteworthy , lithiated singly-charged ion species ( M+Li+ ) of synthetic LPAF , LPC , and PAF standards were found at this range of the spectrum ( Fig . S1 , top spectrum ) ., ESI-LIT-MS analysis of SPE-derived fractions of all T . cruzi forms revealed a clear enrichment of phosphocholine-containing lipids at the low m/z range ( 400–600 ) , which would indicate an enrichment of potential LPC , LPAF , or PAF molecules ., Nevertheless , most samples still contained high amounts of diacyl-PCs and , possibly , SMs ( data not shown ) ., Therefore , a novel protocol using POROS R1 beads was designed to enrich the putative PAF-like molecule , which as we predicted could have a structure similar to PAF , LPAF , or LPC ., First , we tested the POROS R1 mini-column with a complex mixture of phospholipid standards containing LPAF , LPC , PAF , and diacyl-PCs ., We were able to obtain highly enriched LPAF , LPC , and PAF species in the 20% and 25% n-propanol fractions ( Fig . S1 ) ., Identical conditions were applied for further fractionation of the phospholipids present in the SPE methanolic fraction of all T . cruzi forms ., The 25%-n-propanol fractions from these parasite stages were then compared by positive-ion mode ESI-LIT-MS , using the same concentration of cells ( 4×105/µl ) and flow rate ( 300 nl/min ) ( Fig . 2B ) ., The ion species at the 700–900 m/z range , corresponding to diacyl-PCs and SMs , were noticeably much less abundant in the 25%-n-propanol POROS R1 fraction ( Fig . 2B ) than in the Folch lower phase and SPE methanolic fractions ( Fig . 2A and data not shown ) , which is in agreement with the observed phospholipid standard results ( Fig . S1 ) ., In contrast , the ion species at the 400–600 m/z range were prominently more abundant in the 25%-n-propanol POROS R1 fraction than in lower Folch and SPE methanolic fractions ( Fig . 2A , B and data not shown ) ., In particular , the ion species at m/z 526 and 528 were remarkably abundant in the infective TCT form and in the noninfective ICA form ., Tandem MS was then performed for the elucidation of the molecular structures of all phosphocholine-containing lysophospholipids at the 400–600 m/z range ., The fragmentation pattern of the synthetic C16:0-PAF standard ( m/z 530 ) was compared to those of synthetic C18:0- and C18:1-LPC standards ( m/z 530 and 528 , respectively ) , because certain PAF and LPC species may be isobaric ., The assignment of the lysophospholipid species found in the 25%-n-propanol POROS R1 fraction of all T . cruzi forms was based on the fragmentation pattern of these standards , as well as on previously reported results 41 , 54 , 56 ., Tandem MS ( MS2 ) analysis of singly-charged , lithiated C16:0-PAF , C18:0-LPC , and C18:1-LPC ion species gave rise to fragment ions at m/z 471 , 471 , and 469 , respectively , corresponding to the neutral loss of 59 a . m . u . ( =\u200atrimethylamine group ) ., The fragmentation of 16:0-PAF standard , however , also gave rise to a fragment ion at m/z 341 , consistent with the neutral loss of the whole phosphocholine headgroup along with the lithium adduct ( −189 a . m . u . ) ., This fragment could not be detected on either LPC standards ( Fig . S2A ) ., MS3 Fragmentation of the major ions obtained by MS2 of C16:0-PAF , C18:0-LPC , and C18:1-LPC ( i . e . , m/z 471 , 471 , and 469 ) , gave rise to the major non-lithiated ion fragments at m/z 341 , 341 , and 339 , respectively , corresponding to the loss of 130 a . m . u . ( =\u200aethyl phosphate group+Li ) ( Fig . S2B ) ., In addition , we observed lithiated ion fragments at m/z 347 , 347 , and 345 , corresponding to the loss of 124 a . m . u . ( =\u200aethyl phosphate group ) , for C16:0-PAF , C18:0-LPC , and C18:1-LPC , respectively ., Interestingly , C18:0-LPC and C18:1-LPC also gave rise to two fragment ions ( m/z 291 and 289 , respectively ) that could not be detected in the C16:0-PAF standard ., These fragment ions corresponded to the lithiated ( R1CO2H+Li+ ) stearoyl ( m/z 291 ) and oleyl chains ( m/z 289 ) , after the loss of choline ( N+ ( CH3 ) 3 ( CH2 ) 2OH ) from the precursor ions m/z 427 and 425 , respectively ( Fig . S2B ) 54 ., Finally , we carried out MS4 analysis of the major fragment ion species obtained by MS3 of C16:0-PAF , C18:0-LPC , and C18:1-LPC standards ( m/z 341 , 341 , and 339 , respectively ) ( Fig . 3 ) ., A complex fragmentation pattern that provided ample structural information for all three standards was observed ., In these spectra , it was possible to identify fragment ions generated by the loss of the acetyl group at the sn-2 position of C16:0-PAF ( m/z 281 and 263 ) , and a fragment ion corresponding to the protonated C16:0-alkyl chain ( R1+ ) ( m/z 225 ) ., Moreover , a series of fragments resulting from the loss of methylene groups ( =\u200a14 a . m . u . ) from the PAF C16:0-alkyl chain could also be seen below m/z 225 ., Similar information could be obtained from the C18:0-LPC and C18:1-LPC , where fragment ions derived from the protonated stearoyl ( m/z 285 , 267 , and 249 ) and oleyl ( m/z 265 and 247 ) acyl chains at the sn-1 position could be identified 54 ., In both cases , a series of fragments corresponding to the loss of methylene units could also be seen below m/z 240 ( Fig . 3 ) ., Based on the fragmentation pattern of the standards in MS2 , MS3 , and MS4 , we could assign the different lysophospholipid species of T . cruzi enriched in the POROS R1 25%-n-propanol fraction ., As observed in Figs ., 3 and S2A , B , the parent ions at m/z 526 , 528 , and 530 corresponded to C18:2-LPC , C18:1-LPC , and C18:0-LPC , respectively ., The lithiated ( R1CO2H+Li+ ) , non-lithiated ( R1CO++ , and dehydrated R1CO+ - H2O+ ) fragment ions of linoleyl ( m/z 287 , 263 , and 245 ) , oleyl ( m/z 289 , 265 , and 247 ) , and stearoyl ( m/z 291 , 285 , 267 , and 249 ) chains , respectively , corroborated our assignments of T . cruzi ( Tc ) m/z 526 , 528 , and 530 as C18:2- , C18:1 , and C18:0-LPC ( Figs . 3 and S2A , B ) ., No detectable traces of C16:0-PAF ( isobaric to C18:0 LPC ) or any other PAF-like species could be found ., The structure and the fragmentation pattern of C16:0-PAF and the major T . cruzi LPC species are represented in Fig . 4 ., The MSn experiments carried out above , however , could not provide sufficient structural information to determine the position of the fatty acid ( sn-1 or sn-2 ) and the location of the double bonds on three major LPCs of T . cruzi ., To address this point , we first generated LPC standards with fatty acids on either the sn-1 or sn-2 position by treating diacyl-PCs with commercial PLA2 and PLA1 , respectively ., Following protocols by Hsu et al . 54 , we were able to determine that the acyl chain in the three major T . cruzi LPCs was localized at the sn-1 position ., In Figs ., S3A , B , the fragmentation spectra ( MS2 and MS3 ) of sodiated and lithiated sn-1 C18:1-LPC , sn-2 C18:1-LPC , and T . cruzi C18:1-LPC ( from ICA form ) are shown ., In agreement with Hsu et al . 54 , the relative abundance of the sodiated parent ion ( m/z 544 ) to the fragment ion ( m/z 485 ) , corresponding to the loss of trimethylamine ( −59 a . m . u ) , could be used to differentiate between the two possible regioisomers ( Fig . S3A ) ., Clearly , T . cruzi C18:1-LPC showed a fragmentation pattern consistent with an acyl chain located at sn-1 ., This result was corroborated by the fragmentation spectrum of the lithiated T . cruzi C18:1-LPC ( Fig . S3B ) ., In this case , the relative abundance of the fragment ion at m/z 425 M – N+ ( CH3 ) 3 ( CH2 ) 2OH+Li++ to the ions at m/z 339 ( M - 189 ) and m/z 345 ( M - 183 ) was used to corroborate the sn-1 position of the acyl chain on T . cruzi C18:1-LPC ., We carried out identical experiments with T . cruzi C18:0- and C18:2-LPC and found that both contained the acyl chain at the sn-1 position ( data not shown ) ., To address the location of the double bonds in T . cruzi C18:1- and C18:2-LPC , we followed the protocols described by Hsu and Turk 69 ., By comparing the MS4 spectra of Δ9- and Δ6-C18:1-LPC standards , we observed noticeably different fragmentation patterns of the acyl chain , especially in the relative abundance of fragment ions C16H31 ( m/z 223 ) , C14H29 ( m/z 197 ) , C14H27 ( m/z 195 ) , C13H27 ( m/z 183 ) , C13H25 ( m/z 181 ) , C8H15 ( m/z 111 ) , and C8H13 ( m/z 109 ) ( Fig . S3C ) ., When T . cruzi C18:1-LPC ( from ICA forms ) was analyzed under the same MS conditions , the fragmentation pattern observed was consistent with a Δ9 double bond ( Fig . S3C , bottom spectrum ) ., The same type of experiment was conducted with T . cruzi C18:2-LPC ( from ICA forms ) and the resulting fragmentation was consistent with Δ9 , 12 double bonds ( data not shown ) ., The POROS R1 protocol we have described here also enriched other LPC species , which included C22:6-LPC ( m/z 574 ) , C22:4-LPC ( m/z 578 ) , C16:0-LPC ( m/z 502 ) , and C16:1-LPC ( m/z 500 ) that were also characterized by MSn ( Fig . S4 ) ., Most of these species , except for C22:6-LPC , had very low abundance and , in the case of Epi and Meta forms , could only be seen in the enriched 25%-n-propanol POROS R1 fraction ., Even for TCT and ICA forms , MS3 and MS4 of the C16:1- , C22:4- and C18:0-LPC species could only be conducted with samples derived from the POROS R1 chromatography ., This confirms that indeed this last fractionation step is necessary for the full characterization of low-abundance LPC species from complex phospholipid mixtures of T .

Introduction, Materials and Methods, Results, Discussion

Trypanosoma cruzi is the causative agent of the life-threatening Chagas disease , in which increased platelet aggregation related to myocarditis is observed ., Platelet-activating factor ( PAF ) is a potent intercellular lipid mediator and second messenger that exerts its activity through a PAF-specific receptor ( PAFR ) ., Previous data from our group suggested that T . cruzi synthesizes a phospholipid with PAF-like activity ., The structure of T . cruzi PAF-like molecule , however , remains elusive ., Here , we have purified and structurally characterized the putative T . cruzi PAF-like molecule by electrospray ionization-tandem mass spectrometry ( ESI-MS/MS ) ., Our ESI-MS/MS data demonstrated that the T . cruzi PAF-like molecule is actually a lysophosphatidylcholine ( LPC ) , namely sn-1 C18:1 ( delta 9 ) -LPC ., Similar to PAF , the platelet-aggregating activity of C18:1-LPC was abrogated by the PAFR antagonist , WEB 2086 ., Other major LPC species , i . e . , C16:0- , C18:0- , and C18:2-LPC , were also characterized in all T . cruzi stages ., These LPC species , however , failed to induce platelet aggregation ., Quantification of T . cruzi LPC species by ESI-MS revealed that intracellular amastigote and trypomastigote forms have much higher levels of C18:1-LPC than epimastigote and metacyclic trypomastigote forms ., C18:1-LPC was also found to be secreted by the parasite in extracellular vesicles ( EV ) and an EV-free fraction ., A three-dimensional model of PAFR was constructed and a molecular docking study was performed to predict the interactions between the PAFR model and PAF , and each LPC species ., Molecular docking data suggested that , contrary to other LPC species analyzed , C18:1-LPC is predicted to interact with the PAFR model in a fashion similar to PAF ., Taken together , our data indicate that T . cruzi synthesizes a bioactive C18:1-LPC , which aggregates platelets via PAFR ., We propose that C18:1-LPC might be an important lipid mediator in the progression of Chagas disease and its biosynthesis could eventually be exploited as a potential target for new therapeutic interventions .

Chagas disease , caused by the parasite Trypanosoma cruzi , was exclusively confined to Latin America but it has recently spread to other regions of the world ., Chagas disease affects 8–10 million people and kills thousands of them every year ., Lysophosphatidylcholine ( LPC ) is a major bioactive phospholipid of human plasma low-density lipoproteins ( LDL ) ., Platelet-activating factor ( PAF ) is a phospholipid similar to LPC and a potent intercellular mediator ., Both PAF and LPC have been reported to act on mammalian cells through PAF receptor ( PAFR ) ., Previous data from our group suggested that T . cruzi produces a phospholipid with PAF activity ., Here , we describe the structural and functional analysis of different species of LPC from T . cruzi , including a LPC with a fatty acid chain of 18 carbon atoms and one double bond ( C18:1-LPC ) ., We also show that C18:1-LPC is able to induce rabbit platelet aggregation , which is abrogated by a PAFR antagonist ., In addition , a three-dimensional model of human PAFR was constructed ., Contrary to other T . cruzi LPC molecules , C18:1-LPC is predicted to interact with the PAFR model in a fashion similar to PAF ., Further studies are needed to validate the biosynthesis of T . cruzi C18:1-LPC as a potential drug target in Chagas disease .

biochemistry, lipids, lipid mediators, biology and life sciences, microbiology, parasitology

journal.pcbi.1004634

MIiSR: Molecular Interactions in Super-Resolution Imaging Enables the Analysis of Protein Interactions, Dynamics and Formation of Multi-protein Structures

The regulation of many biological processes requires exquisite control over the formation of multimolecular complexes in specific regions of the cell ., For example , the endocytosis , transport and exocytosis phases of vesicular trafficking require the coordination of multiple regulatory proteins , signaling lipids and second messengers on the plasma membrane , endoplasmic reticulum , Golgi and multiple other vesicular structures ( reviewed in 1–9 ) ., Importantly , in these systems a single protein can evoke different responses depending on its subcellular localization and the interacting partners present in a particular cellular niche ., This complexity is well illustrated by the Phosphofurin Acidic Cluster Sorting Protein 2 ( PACS-2 ) which regulates ER-mitochondria traffic through interactions with BAP31 10 , cytosol-to-mitochondria and cytosol-to-lysosome translocation of apoptotic effectors through interactions with Bid , Bim and Bax 10 , 11 , induces cell cycle arrest through interacting with nuclear-localized SIRT1 12 , and is even targeted by pathogens such as HIV to misdirect MHC I endocytosed from the cell surface 13 ., Adding further complexity is the requirement that these proteins be scaffolded into high-order structures such as receptor complexes , coated pits and membrane microdomains to mediate their function 14–17 ., While this building of molecularly unique complexes enables a small number of regulatory proteins to coordinate a vast and heterogeneous vesicular trafficking system , the commonly employed molecular and biochemical approaches used to identify and characterize these complexes obscures the heterogeneity that provides specificity to these cellular systems ., For example , the en bulk nature of biochemical assays such as immunoprecipitation do not preserve the heterogeneity and subcellular localization of these systems , while conventional microscopy-based assays suffer from limited spatial resolution ( typically 250–350 nm 18 ) ., Indeed , many tools have been developed to overcome these limitations of optical microscopy , including Forster Resonance Energy Transfer ( FRET ) and Bimolecular Fluorescence Complementation ( BiFC ) 19 , 20 ., While these technologies have provided an increased understanding of the unique molecular complexes that regulate cellular processes such as vesicular trafficking , they typically involve energy transfer between two fluorophores , or reconstitution of fluorophore halves , and thus are usually limited to assessing interactions between two protein species 21 , can suffer from low signal-to-noise ratios ( FRET , 22 ) and may alter protein interaction dynamics through irreversible cross-linking ( BiFC , 23 ) ., Super-resolution microscopy avoids these issues by directly imaging fluorophores with high precision , with the number of molecular interactions that can be measured limited only by the number of available fluorescent channels ., The advent of super-resolution microscopy systems has enabled the direct visualization of individual molecular complexes with high precision 24 , 25 , with improvements in resolution achieved by one of two strategies ., The first strategy uses patterned excitation light that confines fluorophore excitation to sub-resolution regions ( e . g . STimulated Emission Depletion Microscopy ( STED , 26 , 27 ) and Saturated Structured Illumination Microscopy ( SSIM 28 ) ) ., The second strategy relies on the stochastic switching of fluorophores between dark and fluoresce states at low densities , followed by localization of each individual fluorophore by mapping a Gaussian function to each fluorophore’s point-spread function ( e . g . Photoactivation Localization Microscopy ( PALM , 29–31 ) and Ground State Depletion Microscopy ( GSDM , 32 ) ., Although the stochastic techniques generally provide higher lateral ( xy ) resolution ( 20–30 nm , 31–33 ) than do structured illumination methods ( 50–80 nm , 26–28 ) , both methods provide sufficient resolution to quantify intermolecular interactions ., Recent advances such as the use of astigmatic lenses have provided similar improvements in axial ( z ) resolution ( 50–60 nm ) , allowing quantification to be extended into three dimensions ., Several of these methods can be deployed using widely available laser-based Total Internal Reflection Fluorescence ( TIRF ) microscopes and free software 34 , allowing super-resolution microscopy to be easily and inexpensively implemented in many existing microscopy facilities ., Although a powerful tool , super-resolution microscopy lacks a standardized set of validated quantitative tools for assessing intermolecular interactions equivalent to the commonly employed colocalization and morphological methods available to conventional microscopy 18 , 35 ., Indeed , many analytical techniques developed for conventional microscopy produce erroneous results if applied to super-resolution images , due both to super-resolution images with pixels smaller than the size of many of the complexes being imaged–leading to interacting molecules being resolved in neighboring ( non-colocalizing ) pixels–and due to the stochastic nature of super-resolution imaging which can lead simultaneously to both missed fluorophore detections ( undersampling ) and repeat detection of fluorophores ( oversampling ) within the same sample 36–38 ., To address these limitations we have developed Molecular Interactions in Super-Resolution ( MIiSR ) , a validated set of software tools for quantifying intermolecular interactions and the formation of higher-order molecular complexes in super-resolution images ., MIiSR overcomes the issues associated with applying conventional colocalization and morphological analyses to super-resolution images through the use of spatial statistical approaches which elucidate the presence of intermolecular interactions and the presence of large molecular assemblies through quantification of the spatial relationship between labeled molecules ., By taking a statistical approach , MIiSR provides precise quantitative data from super-resolution images , even given the issues of oversampling , undersampling , and differential sampling between channels , and allows these analyses to be applied to the molecular position files produced by most super-resolution imaging systems ., MIiSR is comprised of a set of analytical functions and two graphical user interfaces ( GUI’s ) written in the Matlab mathematical language ( S1 –MIiSR Program ) ., The input required by MIiSR are molecular position files produced by many super-resolution microscopy systems ., MIiSR natively supports positions files from Leica GSD-SR Ground-State Depletion microscopes , Zeiss ELYRA PS1 dSTORM microscopes , and any microscope running QuickPALM software 34 ., In addition , the import of multiple formats of tab-delineated positions files is also supported , allowing for import from many other super-resolution microscopy systems ., Best analysis practices require data files containing the X/Y/Z coordinates of the detected molecules , as well as the number of photons collected or precision of detection , for each detected molecule–although data lacking Z coordinates and/or photon/precision data can be analyzed ., To install , decompress the MIiSR . zip file and add the resulting “MIiSR” folder to the path file in Matlab ., The first GUI , run using the command ‘MIiSRconvert’ , enables the batch conversion of microscope position files to Matlab-compatible . mat files in a standardized format ( X coordinate , Y coordinate , Z coordinate , # Photons , Precision of detection , Fig 1A ) ., Files can be added to the conversion queue individually , by folder , or by folder and all sub-folders ., Options are available to scale molecular positions from pixels to nm , to scale intensity to number of detected photons , to filter out poorly resolved fluorophores based on fluorophore intensity or the precision of detection , and to optimize processing speed ., The integrity of the molecule position data is maintained through applying these scaling factors in a linear fashion to all data points in the position file ., Once processing has been started all files in the queue will be converted , with converted files saved in the same folder as the original position file ., MIiSRconvert acts as a GUI for the function fileConv . m , which can operate independently of the GUI , enabling its use in user-written Matlab functions and scripts ., The second GUI , run using the command ‘MIiSR’ , is the primary interface for analysis of super-resolution images ( Fig 1B ) ., This GUI acts as a wrapper for functions which combine color ( image ) channels and provide image cropping ( LoadCrop . m ) , filter out molecules located in low-density regions in order to enhance analysis of clustered molecules ( densityFilter . m ) , quantify intermolecular interactions ( SAA2col . m and SAA3col . m ) , quantify molecular clustering ( spatialStats . m ) , and segment molecular clusters within super-resolution images ( DBSCAN . m , OPTICS . m and hierOPTICS . m ) ., These functions can operate independently of the GUI , allowing for their incorporation into user-written functions and scripts ., The general workflow for MIiSR ( Fig 1C ) is to load one to three color channels , which will generate a preview image , and using this preview , to select a Region Of Interest ( ROI ) to analyze ., Once an ROI is set , the user configures the analyses they wish to perform , and then adds the ROI and its attendant analysis options to the queue ., The user then has four options–the analysis conditions for the current ROI can be changed and the new analysis scheme saved as a new entry in the queue , a new ROI and analysis scheme can be selected from the existing image and added to the queue , a new image can be loaded and ROIs and analysis schemes from the new image added to the queue , and lastly , the queue can be processed ( Fig 1C ) ., During processing a folder will be generated for each entry in the queue , located in the same folder as the position file for the first ( red ) color channel ., The data , graphs and images generated for each ROI will be saved in this folder ., While the analysis routines provided in the MIiSR GUI represent a series of well-validated and powerful analytical tools , alternative methods to perform similar analyses have been published previously ., To provide a comprehensive set of tools , we have included functions for Owen et al’s ., density-based method of cluster identification ( Hsegment . m ) and Sengupta et al’s pair correlation based analysis of cluster composition ( RDFquant . m ) 15 , 39 ., These command-line functions perform these analysis on the molecular position files produced by the MIiSR GUI , and can be run as either stand-alone functions or can be incorporated into user-written scripts and functions ., All functions provided in the MIiSR package are extensively documented through the use of in-function comments , enabling users to modify each function as needed ., Detecting intermolecular interactions , in the context of their subcellular localization , is the aim of many biochemical fractionation and imaging methods ., Super-resolution imaging offers an unprecedented opportunity to investigate intermolecular interactions , as many super resolution microscopy methods can detect fluorophores with a lateral resolution of ~20 nm , which is sufficiently accurate to enable a statistical assessment of intermolecular distances 31–33 ., By comparing distances between the molecule of interest and its nearest neighbor ( s ) in the other imaging channels , statistical evidence of inter-molecular interactions can be established ., Indeed , we have previously used this nearest-neighbor approach to establish that tetraspanin’s scaffold the formation of molecularly distinct CD36-integrin complexes , an observation that was obscured by conventional imaging and biochemical assays 40 ., This toolbox includes a more powerful form of this Spatial Association Assay ( SAA ) , which begins with a conventional nearest-neighbor approach wherein the Euclidian distance from each molecule in one color channel of the image to its nearest neighbor ( s ) in the other color channel ( s ) ( S1A Fig ) , but rather than defining intermolecular interactions as those occurring below an arbitrarily selected threshold , SAA instead introduces a statistically robust detection of intermolecular interactions by defining potentially interacting molecules as those separated by distances below the Colocalization Distance Criterion ( CDC ) ., The fraction of interactions is then compared to a randomized image to determine if the observed degree of interaction is above those predicted of non-interacting samples , thus determining if the observed interactions are real or due to chance juxtaposition of molecules in the image ( S1A Fig ) ., The CDC is defined by two factors , the precision of fluorophore detection , and by any chromatic registration defects in the microscope’s optical path ., The precision of localization of each detected molecule in an image is usually provided in the position files produced by super-resolution microscopes , while any registration errors must be measured directly 41 , but are generally negligible in most commercial super-resolution microscopy systems ., The CDC is defined as the root-mean-squared error of the sum of mean precision across color channels ( σRMS ) , multiplied by a 90% or 95% probability cutoff ( 1 . 65 or 2 standard deviations ) , with any image registration ( Ireg ) error added to this product: σRMS= ( ∑i=1nσci ) 12 , where σci is the mean precision of color channel i ., Thus the CDC represents a statistically defined maximum separation distance between interacting molecules in a super-resolution image after sampling precision and chromatic aberration are taken into account ( S1 Text , 42 ) ., For 2-color images most microscopes will have a CDC of 23–28 nm , the exact value of which is determined by the numerical aperture and magnification of the objective lens , and the brightness and number of fluorophores detected in each color channel; for 3-color images the CDC is larger , due to the increased degree of error incurred by measuring across an additional color channel , with typical values ranging from 28 to 35 nm ., It is important to note that SAA analysis is highly sensitive to the area selected for analysis; ROIs which extend past the boarders of the cell , or which include unlabeled structures that have displaced the labeled molecules within the ROI , can lead to over-estimation of the degree of molecular interactions during the randomization process–specifically , leading to the randomization of molecular positions over a larger area/volume than that truly occupied by the imaged molecules ., To limit this error , the user should ensure that all ROIs used for SAA analyses fall within the boundaries of the cell and lack any obvious voids ., In addition , MIiSR includes the option to estimate the true area occupied by the labeled molecules , through refining the user-selected ROI to a minimum bounding polygon encompassing all points in the original ROI ., Proper selection of ROIs , along with this automated refinement of the randomization area , will ensure accurate quantification of molecular interactions in super-resolution images ., To demonstrate the efficacy of SAA analysis we generated 30-mer complementary and non-complementary DNA oligos 5’ tagged with either Cy3 or Cy5 ( Fig 2 ) ., Annealed complementary oligos create Cy3/Cy5 pairs separated by 10 . 2 nm 43 , while the non-complementary oligos distribute randomly ., As expected , MIiSR analysis of 1:1 mixtures of non-complementary oligos found no significant association , and displayed a distribution of intermolecular distances identical to that measured in randomized images ( Fig 2A ) ., In contrast , there was a marked increase in intermolecular separations below the CDC in a 1:1 mixture of complementary oligos , with the mode of the distribution curve falling close to the predicted 10 . 2 nm separation of the Cy3 and Cy5 fluorophores ( Fig 2B ) ., Indeed , measurement of intermolecular distances of Cy3/Cy5 labeled complementary DNA oligos ranging from 20 bp to 60 bp ( estimated lengths of 6 . 8 to 20 . 4 nm 43 ) , determined that the mode of the SAA plot accurately measured the predicted intermolecular distances , and could provide an accurate measure of intermolecular distances as small as one-third the precision of the super-resolution microscope ( Fig 2C ) ., It is important to note that the calculation of interactions by SAA is non-symmetrical , meaning that the degree of association observed between molecule A and molecule B will be different from that measured between B and A . This is illustrated when complementary Cy3 and Cy5 labeled oligos are mixed such that there is excess Cy5-labeled oligo , leading to a mixture containing annealed Cy3-Cy5 oligos and non-annealed Cy5 oligos ( Fig 2D ) ., As all Cy3 oligos are bound to a Cy5 oligo , the SAA interaction observed between Cy3 and Cy5 remains constant , whereas increasing the portion of unannealed Cy5 oligo decreased the measured interaction of the Cy5 oligo with the Cy3 oligo ( Fig 2D ) ., MIiSR automatically calculates SAA along all lines of symmetry , providing a full assessment of molecular interactions within an image ., In theory , the statistical approach taken in SAA analysis should render it insensitive to modest over- and under-sampling ., To test this we imaged complementary Cy3 and Cy5 oligos and then modified the data sets to mimic over- and under-sampling ., Modest oversampling had a minimal impact on the observed degree of colocalization ( S1B Fig ) , whereas under-sampling reduced the measured degree of colocalization ( S1C Fig ) ., Importantly , however , undersampling did not change the measured colocalization when normalized to the colocalization observed in randomized images ( S1D Fig ) , indicating that image randomization can be used to normalize for differing degrees of sampling ., It is important to note that because of oversampling it is not practical to use SAA analysis to quantify homeotypic interactions as it is extremely difficult to differentiate between the repeated detection of the same fluorophore versus detection of multiple fluorophores within an area less than the CDC ( S1E Fig ) ., Moreover , extreme oversampling can lead to randomized images where the mean intermolecular separation is less than the CDC ., For highly oversampled images , MIiSR includes the option to analyze a randomly selected subset of the molecules in the image , a method demonstrated previously to provide accurate quantification of intermolecular interactions 40 ., To demonstrate the utility of SAA analysis in a biologically relevant system we used MIiSR to analyze interactions with the HIV protein Nef , which modulates the trafficking regulators PACS-1 and AP-1 in order to alter membrane trafficking in a manner advantageous to HIV immune evasion 13 , 44 ., Using cells expressing GFP-Nef , mCherry-PACS-1 and Alexa-647 immunolabled Golgin 97 , we used GSDM to image the interaction of Nef with PACS-1 and the Golgi , and then analyzed these images using 3-color SAA analysis ( Fig 3A and 3B ) ., This analysis revealed that the majority of Nef is complexed with PACS-1 , with a third of the Nef-PACS-1 complexes localized to the Golgi ( Fig 3B and 3C ) ., The small portion of non-PACS-1 associated Nef was split between Golgi and non-Golgi compartments , and importantly , all of these interactions were significantly different from the degree of association predicted for non-interacting molecules ( Fig 3C , SRP ) ., By fixing and imaging cells at different time points , SAA analysis can be applied to the study of temporal changes in protein-protein interactions ., Indeed , we found that HIV Nef decreases its interaction with the trafficking regulator PACS-1 as time increases after HIV protein expression ( Fig 3D and 3E ) , consistent with the shift of Nef’s function from altering endocytosis to altering Golgi export over this time period 45 ., Clearly , MIiSR can provide insight into intermolecular interactions not otherwise detectable by conventional biochemical and microscopy techniques ., While SAA quantification provides insights into intermolecular interactions , it does not provide information on the formation of higher-order molecular structures such as protein islands , lipid microdomains , vesicles , or other large molecular assemblies ., These higher-order structures are critical in many biological systems , for example , comprising the 90 nm diameter endocytic clathrin-coated pits 17 ., Indeed , GSDM imaging of the β2-adrenergic receptor after stimulation with 10 μM isoproterenol shows a visually apparent increase in co-clustering with β-arrestin-1 and clathrin ( Fig 4A ) , consistent with our previous studies showing this receptor to be internalized via a β-arrestin-1/clathrin-dependent pathway 46 , 47 ., While formation of de novo interactions of the β2-adrenergic receptor with β-arrestin-1 and clathrin can be identified by SAA , the delivery of β2-adrenergic receptor to clathrin-coated pits by β-arrestin-1 is best assessed using clustering assays ., Quantification of molecular clustering is implemented in MIiSR using spatial statistics , specifically the Radial Distribution Function ( RDF ) and Ripley’s K statistic 48–50 ., These analyses provide quantifiable , numerical measures of clustering that can be compared across multiple images ., RDF , also known as the pair-correlation function and G-function , was developed nearly 50 years ago for the assessment of atomic and molecular distributions in physical and chemical systems 51 , and more recently has been applied to super-resolution images 15 , 38 , 48 ., RDF quantifies the density of molecules as a function of distance, ( r ) to other molecules in the same color channel ( self-clustering ) , or in a second ( co-clustering ) , color channel ( S2A Fig and S1 Text ) ., RDF analysis will accurately illustrate the presence of multiple cluster sizes and inter-cluster distance ( S2A Fig ) , and the relative degree of clustering is indicated by the height of the peaks corresponding to the molecular clusters ., An alternative approach proposed by Ripley in 1977 , termed the K function , quantifies the number of molecules as a function of distance ( S3A Fig and S1 Text , 52 ) ., The K function is typically normalized to distance and density , producing the H function ., Ripley’s H function , plotted against intermolecular distance, ( r ) , produces an inverted parabola-like curve with a single peak ., The value of r corresponding to this peak ( rmax ) correlates roughly with mean cluster size , while the height of this peak correlates to the degree of clustering in the sample ., It is important to note that the value of rmax is impacted by the distance between neighboring clusters , and thus is not a direct indicator of mean cluster size 50 , 53 ., Several approaches are available to calculate an accurate mean cluster radius from H-plots; Lagache et al , determined that average cluster radius could be approximated by rmax/1 . 3 in biological systems 53 , whereas Kiskowski et al , determined that the mean cluster radius was equal to one-half the radius where the derivative of the H, ( r ) plot H’, ( r ) crosses -1 50 ., MIiSR calculates both H, ( r ) and H’, ( r ) , allowing the user to use either method to determine mean cluster radius ., The difference between Ripley’s H-function and RDF analysis is illustrated in S1–S3 Videos , which respectively compare: S1 Video ) the co-clustering of two randomly distributed molecular species , S2 Video ) co-clustering of a randomly distributed population of molecules with a pre-clustered population of molecules , and S3 Video ) the impact of increasing ratios of unclustered to clustered molecules on these analyses ., Of note , RDF analysis is better able to identify weak co-clustering ( S1 and S2 Videos ) , and retains its ability to identify clusters in the presence of a large number of unclustered molecules ( S3 Video ) ., In contrast , Ripley’s analysis provides a single clear peak indicative of mean cluster size , whereas RDF produces a more complex , multipeaked curve , indicative of sub-populations of differently sized clusters ( S1–S3 Videos ) ., The utility of these tools in MIiSR is demonstrated by analyzing the sequential formation of a macromolecular endocytic complex formed by the β2-adrenergic receptor , its endocytic regulator β-arrestin-1 , and clathrin-coated pits ( Fig 4A ) ., Following stimulation with isoproterenol the β2-adrenergic receptor associates with β-arrestin-1 , which subsequently delivers the β-adrenergic receptor to clathrin-coated pits for endocytosis 46 , 54 , 55 ., RDF analysis revealed that unstimulated β2-adrenergic receptor is minimally clustered with β-arrestin-1 ( Fig 4B ) , with this weak clustering indicated in Ripley’s analysis as an upward curve ( Fig 4C ) ., As expected , stimulation with isoproterenol results in a rapid co-clustering of the β-adrenergic receptor with β-arrestin-1 that increases over time ( Fig 4A–4C ) ., In contrast , no significant clustering of the β-adrenergic receptor is observed with clathrin prior to , or in the first minute following , isoproterenol stimulation , with significant co-clustering between the β-adrenergic receptor and clathrin observed only at 5 minutes post-stimulation ( Fig 4A , 4D and 4E ) ., Interestingly , the β2-adrenergic receptor clusters start off as small clusters ( ~40 nm , Fig 4D , 1 minute arrow ) and then coalesced into larger structures at 5 minutes ( ~90 nm , Fig 4D , 5 minute arrow ) ., These larger clusters overlap , and are of the same size ( 90 nm ) as , the β2-adrenergic receptor/clathrin clusters observed at the same time point ( 5 minute arrows , Fig 4D and 4E ) , and closely match the reported size of clathrin coated pits 56 ., While these are powerful tools for quantifying clustering , careful sample preparation and imaging acquisition are required to minimize artifacts in both RDF and Ripley’s analysis ., It is critical to employ labeling methods which minimize labeling-induced clustering ., Indeed , significant antibody-induced oligomerization of CD81 was observed when samples were labeled using full-length primary and secondary antibodies , whereas replacing either the primary or secondary antibody with Fab fragments reduced CD81 oligomerization to that observed when a monomeric primary labeled Fab was used ( Fig 4F ) ., In addition , while MIiSR allows for quantification of self-clustering using both Ripley’s and RDF analyses , great caution needs to be taken when interpreting these results as oversampling caused by the repeat detection of the same fluorophores can create an artefact of intense self-clustering , in which case the apparent cluster size will equal the precision of the Gaussian mapping algorithm ( S2B and S3B Figs ) ., As a result , analysis of self-clustering remains difficult 38 , although alternate methods such as Sengupta et al . ’s method for quantifying cluster composition in RDF plots can provide some indication of self-clustering 15 ., This function is provided as ‘RDFquant . m’ in the MIiSR package ., A final consideration is image acquisition , specifically the degree of under- or over-sampling in the analyzed images ., In contrast to measurements of self-clustering , neither over- or under-sampling have a significant impact on cross-RDF analysis ( S2D Fig ) ., In contrast , cross-Ripley’s analysis is sensitive to both oversampling and undersampling , with undersampling leading to underestimation of clustering , and oversampling leading to smaller measured cluster size ( S3C–S3F Fig ) ., As such , it is important that Ripley’s analysis be performed on a sample containing the smallest possible degree of over- and under-sampling ., At this time there are no widely accepted methods for determining the ideal degree of sampling for quantitative super-resolution imaging , and as such we would recommend that both RDF and Ripley’s analysis be performed in parallel ., Alternatively , we have determined that it is possible to define a reasonable stop-point in the image reconstruction process at which reconstructed image achieves the best balance between undersampling and oversampling ( S3D–S3F Fig ) ., By performing a Pearson’s autocorrelation as the super-resolution image is reconstructed it is possible to determine the point where the adding additional frames to the image reconstruction process ceases to add additional information to the resulting super-resolution image ( S3D and S3E Fig ) ., Autocorrelation increases rapidly early during image reconstruction as new fluorophores are added to the image , but will asymptotically approach 1 later in the reconstruction process as new fluorophore detections decrease and repeat detections increase ( S3E Fig ) ., Ripley’s analysis of a modeled super-resolution acquisitions incorporating both over- and under-sampling demonstrates that the measured H, ( r ) values most closely match the true H, ( r ) values when super-resolution image reconstruction is restricted to the portion of the acquisition where the autocorrelation R value is ≤ 0 . 990 ( S3F Fig ) ., Unfortunately , not all super-resolution systems provide positions files compatible with this form of image reconstruction , and as such this reconstruction method could not be included in MIiSR ., While the RDF and Ripley’s functions provide a quantitative measure of the degree of molecule clustering in an image , they do not allow for easy segmentation of individual clusters within the image ., A previous approach , which calculates Ripley’s H function at a static value of r followed by thresholding of the resulting image , has been successful 39 , and is provided in MIiSR as Hsegment . m ., However , the resulting cluster map is highly dependent on the value of r and the threshold value selected by the user ., As such , MIiSR also includes the cluster-identification algorithms Density-Based Spatial Clustering of Applications with Noise ( DBSCAN , 57 ) and Ordering Points To Identify the Clustering Structure ( OPTICS , 58 , 59 ) , which are unbiased image segmentation tools for identifying molecular clusters ., To demonstrate the utility and limitations of these tools they were applied to the analysis of Alzheimer’s-disease associated Amyloid Precursor Protein ( APP ) in lysosomes ., APP may be processed into pathological forms of β-amyloid in lysosomes , with subsequent exocytosis depositing APP in the brain , thus forming the pathological β-amyloid plaques typical of Alzheimer’s disease 60–65 ., The mechanism of APP release into the extracellular environment is not clear , but our earlier studies suggest that multi-vesicular body ( MVB ) exocytosis may be a key route of APP secretion 63 , 65 , 66 ., GSDM imaging was used to identify immunolabeled APP and the lysosomal network through identifying clusters of the late endosome/lysosomal marker LAMP1 ( Fig 5A ) ., DBSCAN and OPTICS analysis in MIiSR were then applied to assess the localization of APP within the late endosome/lysosome network ., DBSCAN is an algorithmic approach to identifying clustered molecules in which a molecule is considered part of a cluster if it has a sufficient number of neighboring molecules within a defined area ( S1 Text and S4A Fig , 57 , 67 ) ., Unlike many clustering algorithms , DBSCAN can identify core versus edge molecules within a cluster , but is limited in that the user must choose appropriate values for neighborhood size ( ε ) and minimum number of molecules per cluster ( k ) ., Indeed , the values selected for these variables are critical , as inappropriate values can lead to over- or under-estimation of cluster composition ( Fig 5B and S4B Fig ) ., Applied properly , DBSCAN allowed for the identification of APP clusters with only minimal errors , but was unable to identify LAMP1 clusters due to the more heterogeneous nature of LAMP1 staining ( Fig 5B ) ., Regions of interest were defined

Introduction, Design & Implementation, Results, Materials and Methods

Our current understanding of the molecular mechanisms which regulate cellular processes such as vesicular trafficking has been enabled by conventional biochemical and microscopy techniques ., However , these methods often obscure the heterogeneity of the cellular environment , thus precluding a quantitative assessment of the molecular interactions regulating these processes ., Herein , we present Molecular Interactions in Super Resolution ( MIiSR ) software which provides quantitative analysis tools for use with super-resolution images ., MIiSR combines multiple tools for analyzing intermolecular interactions , molecular clustering and image segmentation ., These tools enable quantification , in the native environment of the cell , of molecular interactions and the formation of higher-order molecular complexes ., The capabilities and limitations of these analytical tools are demonstrated using both modeled data and examples derived from the vesicular trafficking system , thereby providing an established and validated experimental workflow capable of quantitatively assessing molecular interactions and molecular complex formation within the heterogeneous environment of the cell .

In this paper we present the software package Molecular Interactions in Super Resolution ( MIiSR ) , which provides a series of quantitative analytical tools for measuring molecular interactions and the formation of higher-order molecular complexes in super-resolution microscopy images .

journal.pcbi.1002197

B Cell Activation Triggered by the Formation of the Small Receptor Cluster: A Computational Study

B lymphocytes activation is initiated by B cell receptor ( BCR ) aggregation following antigen engagement ., Activated B cells can differentiate to form extrafollicular plasma-blasts responsible for the rapid antibody production and early protective immune responses ., Alternatively , they can differentiate into plasma cells , which can secrete high-affinity antibody , or memory B cells , which provide long-lasting protection 1 ., BCR is composed of the highly varied membrane-bound immunoglobulin ( mIg ) molecule and a heterodimer of the Igα and Igβ chains containing the tyrosine-based motifs ( ITAM ) which can be phosphorylated by members of the Src family kinases ( SFKs ) 2 ., In turn , phosphorylation of ITAMs enables the stable binding of SFKs via SH2 domains , preferentially to Igα chains , which are then activated by transphosphorylation , see 3 for review ., When B cell receptors are aggregated , the weakly-bound Src kinases initiate phosphorylation of the ITAMs of neighboring receptors ., The phosphorylated tyrosines of Igα chain ITAM then bind more stably to the SH2 domains of the Src kinases , thus allowing the kinases to mediate phosphorylation of both Igα and Igβ chains more efficiently 4 ., The higher affinity SH2 domain binding enables Src kinase transphosphorylation in the activation loops thereby increasing their catalytic activity 5 ., Thus , although BCR does not directly activate Src kinase , binding of Src kinases to phosphorylated ITAM motifs enables Src kinase phosphorylation ., The phosphorylated tyrosines of the Igβ ITAM recruit a cytosolic protein , tyrosine kinase Syk , which mediates phosphorylation of proteins acting further downstream in the signalling pathway 6 ., We recently demonstrated by a computational analysis of a reaction-diffusion model that the strength of positive feedback controlling cell ability to be activated , is regulated by the kinase diffusion coefficient 7 and the spatial distribution of the membrane receptors 8 ., Rapid kinase diffusion , although enhances transmission of activity towards cell nucleus , causes that the activated kinase quickly leaves the vicinity of the cell membrane , and cannot activate receptors ., As a result , for a broad range of parameters the cell can be activated only if the kinase diffusion coefficient is sufficiently small 7 ., Moreover , aggregation of receptors increases the chance that the receptor activated kinase will target the other ( neighboring ) receptors , before it will be dephosphorylated by phosphatases ., We showed that aggregation of membrane receptors alone can trigger cell activation 8 ., In this paper we explore a modified reaction-diffusion model of a mutual kinase-receptors interaction in the context of B cells ., Below , we review the established facts of B cell receptor signaling , which led us to the considered model ., Upon the contact with antigen-presenting cell , the B cell membrane is reorganized , leading to the formation of an immunological synapse , which induces B cell spreading over the antigen-containing surface , and then its contraction 1 , 9 ., During the contraction phase the antigens are gathered into the central cluster , with an area of less than 10% of the total cell surface 10 ., Formation of BCR clusters triggers rapid phosphorylation of BCRs and the associated SFK 11 ., B cell activation follows the recognition of membrane-bound specific antigens by BCR 12 ., Antigen binding drives the formation of BCR clusters that initiate the formation of signaling complexes consisting of BCRs and SFKs 13 , 14 ., These clusters can be formed by receptor cross-linking due to binding of polyvalent ligands recruited from the solution 14 ., Alternatively , the receptor clusters can be formed by a B cell contact with an antigen-presenting cell ( APC ) loaded with antigens ., It was showed by Batista et al . 15 and then by Tolar et al . 16 that monovalent ligands are also capable to initiate BCR signaling if presented on APC ., Formation of the immunological synapse is a way to select the strongly binding antigens from the surface of the antigen presenting cell and to collect them into a smaller area ., Large antigens , including viruses and immune complexes , are captured from the lymph by macrophages and are presented by these macrophages to B cells , see 17 ., Dintzis and Vogelstein 18 , 19 proposed a theory in which about 10–20 BCR rich aggregates ( immunons ) which are formed upon binding of the highly multivalent antigens , like viruses , are capable of triggering B cell responses ., This idea was then theoretically investigated by Sultzer and Perelson 20 , 21 who were able to explain the experimental dose-response curves , obtained with highly multivalent antigens mixed with ligands of lower valence ., Summing up , in both modes of activation – by monovalent and polyvalent large antigens–the limiting step in B cell activation is the formation of BCR cluster ., As discussed , these aggregates can be formed by direct cross-linking of receptors , or by diffusion trapping after kinetic segregation on the B cell membrane ., Aggregates form independently of signaling through BCRs , because they were observed to form on membranes of Lyn deficient B cells following antigen stimulation 11 ., Formation of microclusters leads to rearrangement of the corticalactin network and formation of corrals around BCR microclusters ., These fibroactin corrals are responsible for restricting BCR diffusion and maintaining integrity of BCR microclusters 11 ., Src family includes at least eight highly homologous proteins 22; the three SFKs most abundantly expressed in B cells are Lyn , Blk and Fyn 23 ., As shown by Sato et al . 24 , Src kinases trafficking is specified by the palmitoylation state ., Non-palmitoylated SFK like Blk rapidly move between the plasma membrane and late endosomes or lysosomes , mono-palmitoylated Src kinases , such as Lyn are transported to the cell membrane via the Golgi apparatus , whereas dually palmitoylated Fyn , is directly targeted to the membrane 24 ., Sato et al . examine the localization of SFKs by transfecting monkey kidney epithelial cells ( COS-1 ) with Lyn and Fyn ., They found that in the early phase of expression ( 8 hours ) , Lyn is predominantly localized in the perinuclear region and gradually moved to the plasma membrane in the later phase ( 24 hours ) , while the majority of Fyn was found at the plasma membrane from the early phase ., In some cell lines the transient nuclear localization of SFKs was observed: Fyn in zebrafish embryo development 25 , Lyn in HeLa cells with Lyn inhibitor Csk overexpression 26 ., Recently , Takahashi et al . reported nuclear localization of SFK in COS-1 cells 27 ., Nevertheless , SFKs lack nuclear localization signal ( based on http://pprowler . itee . uq . edu . au/NucImport , 28 ) and majority of studies indicated that in immune cells SFKs predominatly localize either on the cytoplasmic face of plasma membrane or transiently in the perinuclear region 29 , 24 ., The cytoplasmic kinase Syk binds preferentially to doubly phosphorylated Igβ chains , where it is transphosphorylated ., Rolli et al . 30 found that Syk also mediates positive feedback phosphorylating ITAM motifs ., In particular , they demonstrated that Lyn phosphorylates ITAMs only at the first tyrosine residue , whereas Syk phosphorylates both tyrosines of the ITAM ., Furthermore , Syk is a positive allosteric enzyme which is strongly activated by binding to the phosphorylated ITAM tyrosine residues ., Thus , the recruitment of Syk to the phosphorylated ITAM allows Syk to catalyze the phosphorylation of more ITAMs allowing the recruitment of additional Syk molecules to the clustered BCR complexes 31 ., This leads to further amplification of the signaling 32 ., Since in the proposed model all receptor interacting kinases are represented by a single kinase species , with respect to the above properties of Src kinases and Syk , we will consider two cases: B cell activation leads to irreversible cell fate decisions and differentiation into cells capable of secretion of protective antibodies or memory cells that provide long-lived protection against secondary infection ( see 11 for review ) ., One can thus expect that this regulation is associated with bistability 33 ., In particular , Bhattacharya et al . proposed that B cell differentiation relies on bistable switch 34 ., Bistable systems are capable to convert the graded signals into well defined all-or-nothing responses 35 , 36 ., They are typically associated with positive feedback loops and nonlinearities 37 ., The considered system of Src kinases interacting with BCRs has several positive regulatory couplings ., In particular As discussed above , the formation of a receptor cluster is the limiting step in initiation of the signaling ., In this study we propose a reaction-diffusion model which can explain how signal initiated by a small BCR cluster can propagate and trigger activation of the remaining receptors ., In the considered model we omit the step of ligand binding and the process of receptor aggregation ., Instead we assume , according to the above discussion , that as a result of the ligand binding , some fraction of receptors is aggregated and immobilized in a portion of the cell membrane ., The model exploits the assumption that the BCR-SFK regulatory system is bistable ., Fuss et al . proposed that the SFK activity is controlled through a bistable switch resulting from SFK kinase autophosphorylation 38 , 39 ., Next , Kaimachnikov and Kholodenko 40 showed that SFK can display oscillatory , bistable and excitable behaviors ., They demonstrate that the saturability of phosphatase activity suffices for bistability ., In the model proposed in this study , bistability arises due to Michaelis-Menten kinetics of SFK dephosphorylation , and in addition due to distributive phosphorylation of ITAM tyrosines ., As discussed in 27 , the distributive phosphorylation requires sufficiently large diffusion ., For small diffusion and short reactivation time ( the time needed for the kinase to release ADP and bind the next ATP molecule ) , the rapid rebindings of the enzyme can turn the distributive phosphorylation into the processive one ., Each of these two nonlinearities suffices for bistability , but their combination causes that the bistability range is broader ., Bistable , spatially extended systems may be triggered by a relatively small but localized signal which induces local transition from inactive to active state ., Thereafter , the surge of kinase and receptors activity propagates as a traveling wave ., Relevant to our model , Wang et al . observed mechanically initiated Src activity wave propagating over plasma membrane 41 ., We will show that the minimal size of the activatory cluster decreases with decreasing the thickness of the cytoplasm-which is very scanty for B cells ., The matured B cells ( that range in size from 7–20 ) frequently retain the original 4∶1 nuclear-cytoplasmic volume ratio of premature cells which gives the ratio of the nuclear to cell radii of about 0 . 93 42 ., We will show also that cell can be activated in response to nuclear displacement without receptor clustering ., In this case , the activation starts in the place where the cytoplasm is the thinnest , and then propagates over the rest of the cytoplasm ., Nuclear movements during synapse formation are well documented experimentally in T lymphocytes 43 ., In this variant of the model we assume that the kinase is tethered to the cell membrane ., In such a case the 3D model may be replaced by a 2D model on the cell membrane ., Moreover , if the axial symmetry is assumed the problem becomes essentially 1D , in which and are the functions of t and only , where ., In the non-dimensional form the system of equations for membrane model reads: ( 6 ) ( 7 ) ( 8 ) with initial conditions ( 9 ) As in the cytosolic model , we will put ., In the case of thin cytoplasm , i . e . when for the solutions of the system ( 6 ) – ( 9 ) approximate the solutions of the original model ( 1 ) – ( 5 ) ., In the original model the last condition implies that the kinase activity is almost constant on the depth of the cytoplasm , thus only the dependence of K on θ is important ., In the limit of infinite diffusion ( ) for the system is perfectly mixed , thus the concentrations , K and R , are constant in space ., In this case systems ( 1 ) – ( 4 ) and ( 6 ) – ( 7 ) converge to the system of ordinary differential equations ( 10 ) ( 11 ) One can obtain the above system by integrating Eq ., ( 1 ) over and using the Gauss theorem ., The system ( 10 ) – ( 11 ) can be mono- or bistable , i . e . it has one or two stable stationary solutions depending on parameters c0 , H and B , where ( 12 ) In the case of bistability , the two stable solutions correspond to the states with low and high levels of the active kinase and receptors ., These states will be referred to as inactive and active ., In Figure S1 , we show the bistability regions in ( B , H ) plane for several values of the spontaneous activation constant ., The bistability regions do not depend explicitly on , because this parameter is already incorporated in B defined in ( 12 ) ., The bistability range is the largest for and shrinks with increasing , see Text S1 ., In further analysis , in order to reduce the number of free parameters , we fix the spontaneous activation constant and the Michaelis constant H\u200a=\u200a0 . 1 ., As may be deduced from Figure S1 , such a choice of and H implies a robust bistability ., As shown in Figure S1 , the bistability region is even broader for , but since this is non-generic case and can lead to results valid only for the zero basal activity of kinases and receptors , we focus on the case of small but positive ., The numerical simulations leading to the results presented in Figures 2–7 and Figures S3–S6 were performed using the COMSOL multiphysics simulation software environment ., As shown in Figure S2 in a certain range of parameters the system is bistable also for finite diffusion ., In the further considerations we focus on the particular bistable case ., That is to say , for a given α and rn we choose such , for which the system is bistable ( for ) and loses its bistability ( becomes monostable active ) , precisely when the receptor level increases to with all other parameters fixed ., We set q\u200a=\u200a1 . 5 ., Such a choice of q is arbitrary , but it does not qualitatively influence the system behavior ., It assures that the system ( for the reference receptor level ) cannot be activated by a modest increase of the total receptor level ., For example , for α\u200a=\u200a0 and the system is bistable for with , for α\u200a=\u200a3 and the system is bistable for , whereas ., For and the system is bistable for and ., As shown in Figure 2 , for α\u200a=\u200a10 , , and the system has two stable stationary solutions , inactive and active ., The solutions starting from zero initial condition converge to the inactive state , whereas starting from initial condition , converge to the active state ., As discussed above , our choice of bq guarantees that the cell , when inactive , cannot be activated by a modest increase of the total receptor level , assuming that the receptors are uniformly distributed ., We will show however , that the cell activation may follow an aggregation of a small fraction of the total amount of membrane receptors , with the total receptor amount unchanged ., We will model the receptor aggregation by introducing the following modification to the spherically symmetric distribution of the receptors: ( 13 ) where F describes the fraction of the aggregated receptors ., For large i the aggregated receptors occupy approximately fraction of the total cell membrane ., As shown in Figure 2B , for F\u200a=\u200a0 . 01 and , the trajectory of system ( 1 ) – ( 5 ) starting from the zero initial condition converges in time to the active steady state shown in Figure 2A ., First , the cell activates itself in the vicinity of the receptor cluster; then the activation wave propagates around the whole cytoplasm ., In this case the aggregation of 1% of the membrane receptors on the 1% fraction of the cell membrane causes that the local receptors density increases by a factor of ., As a result , the system becomes locally monostable since , as discussed above , the q-fold increase of the receptor concentration leads to monostability ., As in the rest of the membrane , the system remains bistable , the activated region may spread until the entire cytoplasm becomes active ., In this scheme , there are two conditions necessary for cell activation: Ad ( I ) The necessity of the global bistability for the full activation is demonstrated in Figure S3 ., The simulation is performed for the same parameters as chosen for Figure 2 , but with the aggregated fraction of receptors F\u200a=\u200a0 . 5 ., The system is locally active in the vicinity of the cluster , but since the large fraction of receptors is translocated from the rest of the membrane , the system is no longer globally bistable ., Thus , the traveling wave cannot propagate ., This shows that the local monostability of the system is not sufficient for the whole cell activation ., The global bistability does not imply that the wave front propagates in the “right” direction-i . e . that the active region grows ., If the “energy” of the inactive state is lower than the “energy” of the active state , the active region will rather shrink than expand , see Figure S5 ., As a result , even if the system activates locally , the wave of kinase activity will not leave the region of the higher receptor concentration ., The traveling wave propagates from inactive to active state when is close to ., For ( defined in the previous section ) the “energy” of the active state appears to be always smaller than the “energy” of inactive state and the wave propagates in the right direction ., In addition , the difference between the active and inactive state energies should be large enough to overcome the effect of the wave front curvature ., Expansion of the curved wave front implies its elongation and growth of its energy-the effect is proportional to the value of the front curvature and thus it is important if the receptor cluster is small ., In such a case the receptors may be activated locally , but the activity wave cannot propagate , see Text S1 for detailed analysis of the curvature effect ., In Figure S4 we compare the conditions of straight and curved wave front propagation ., In Figure S6 we show that aggregation of F\u200a=\u200a0 . 1 fraction of receptors at fraction of the cell surface leads to cell activation , but when the same fraction of receptors is clustered on of cell surface , the cell activates only locally in the vicinity of the receptor cluster ., Ad ( II ) The system is locally monostable if the value of the local receptor concentration exceeds q , and the fraction of cell membrane occupied by the receptor cluster is large enough ., For given α and rn there exists the minimal fraction of the cell surface on which the receptor concentration must exceed q in order to achieve the local activation ., In Figure 3 , we show the critical fraction of the aggregated receptors for α\u200a=\u200a1 , α\u200a=\u200a3 , α\u200a=\u200a10 and for different values of nuclear radius rn\u200a=\u200a0 , 0 . 8 , 0 . 9 , 0 . 95 and 0 . 98 ., The critical fraction of the aggregated receptors decreases nearly linearly with the size of the cluster 1/i for sufficiently large 1/i ., This is in accordance with the observation that for the sufficiently large cluster , cell activation is controlled by the local density of receptors in the cluster ., This condition is well visible for small diffusion α\u200a=\u200a10 and thin cytoplasm rn\u200a=\u200a0 . 98 , when the critical fraction of aggregated receptors F as a function of 1/i lies above but follows the line ., This implies that the local concentration of receptors must increase at least by q to trigger cell activation ., However , when the size of receptor cluster 1/i becomes too small , this trend breaks ., As shown in Figure S6 , even if the cell activates locally , the front curvature is so large that its expansion is not possible ., The further decrease of receptor cluster size causes that the cloud of the activated kinases is washed out by the diffusion and even the local activation is not possible ., Thus , for any fixed F and 1/i sufficiently small the system does not activate even locally ., Summing up; for a given value of 1/i there exists the critical fraction of the aggregated receptors such that for the cell activates ., decreases with decreasing 1/i until 1/i achieves the limit value , which corresponds to the ( absolute ) minimal fraction of receptors , , that must be aggregated in order to activate the cell ., The smaller is the kinase diffusion ( the larger value of α ) the smaller is the wash-out effect and thus the fraction of aggregated receptors needed for activation is smaller , compare Figure 3 panels ., Moreover , for fixed α , the fraction of aggregated receptors necessary for cell activation decreases monotonically with the thickness of cytoplasmic layer ( ) , Figure 4 ., This is due to the fact that for thin cytoplasm the diffusion is rather 2D than 3D ., As it is seen from Figure 4 , for a given α such that , can be approximated by the formula ., It is tempting to speculate that the thickness of cytoplasm provides a measure that distinguishes between activatory and nonactivatory clusters ., For receptor clusters larger than the thickness of cytoplasm the diffusion of activated kinases is essentially 2D , which facilitates the wave front formation and propagation ., As we will see in the next section the activatory cluster is much smaller under the assumption that kinases are confined to the cell membrane ., Remark: For a scalar reaction-diffusion equation ( 14 ) the potential energy may be defined as a ., In bistable scalar reaction-diffusion equation the direction of wave front velocity is such , that the region in which , ( where is the global potential minimum ) expands , see e . g . 47 ., As a result the total energy of the system decreases ., For two-component system , the potential energy function exists only when the source terms f1 and f2 satisfy , the consistency condition ., In this non-generic case , source terms can be expressed as potential derivatives i . e . In our case the consistency condition is not satisfied , and thus our systems does not have a true potential ., Therefore the term “energy” is used , only in the intuitive sense ., Thinning of cytoplasm increases boundary to volume ratio and thus facilitates cell activation ., As a result , the cell can be also activated by a displacement of the nucleus , which locally increases membrane to cytoplasm volume ratio ., As shown in a related model by Meyers et al . 48 , the increase of surface to volume ratio increases phosphorylation level ., However , as opposed to Meyers et al . consideration , in our case the nucleus is not penetrated by the kinases ., As a result only the cytoplasmic volume counts and thus the local cell activation may be induced without deformation of the plasma membrane ., As shown in Figure 5 , for rn\u200a=\u200a0 . 9 , α\u200a=\u200a10 , the cell activates in response to the displacement of the nucleus by 0 . 08 , which causes that locally the thickness of the cytoplasm drops to 0 . 02 ., As a result , the cell locally activates , but interestingly , although the activity on the opposite pole is much lower , the activatory wave spreads also on the thicker parts of the cytoplasm ., The displacement of the nucleus may accompany formation of the immunological synapse , when B cell scans the antigen presenting cell ., It is well documented that in cytotoxic T lymphocytes , microtubule organizing center translocates towards target cell during synapse formation 43 ., T cell polarization enables unidirectional killing ., Although not reported , it is intuitive that the nuclear displacement takes place also in the early stage of B cell synapse formation when B cell spreads over antigen presenting cell , which requires cytoskeleton reorganization 11 ., In the membrane model the range of bistability in parameter b , bmin , bmax , is the same as for the system in the infinite diffusion limit ( 10 ) – ( 11 ) and from the analytical examination of bistability of this system ( see Figure S1 ) , we have for p\u200a=\u200a1 , and ., The value of bq , as defined previously , is ., In particular , we consider the membrane model corresponding to the cytosol model with , which gives the value of ., For this value of a we obtain , , ., The typical protein diffusion coefficients on membranes are about ten times smaller than in cytosol , accordingly the values of α expected in the membrane model are higher ., For large α , as discussed in the Text S1 ( see Figure S4 ) , the effect of wave front curvature on wave propagation becomes negligible ., In this limit , the front speed c can be approximated as , where u0 is the nondimensional coefficient characterizing the system ( here ) ., In dimensional units front speed is thus , which gives the total cell activation time , where , recall , we set ., Wang et al . 41 found that mechanically induced wave of Src kinase activity propagates with velocity of nm/s ., Such propagation speed can be obtained in our model by taking α\u200a=\u200a0 . 5×6 µm× ( 1/s ) / ( 18 nm/s ) ≈167 ., For such α we obtained the cell activation time equal to TA\u200a=\u200a1000 ( see Figure 6 ) ., Obviously , the experiments of Wang et al . 41 performed on human umbilical vein endothelial cells may serve only as order of magnitude reference ., The visual analysis of the data showed by Depoil et al . ( 49 , Figure 3 ) suggests that the wave of Syk kinase activity ( which requires BCR activation ) spreads outside the central cluster in time of order of 10 minutes ., Collectively , these two experiments suggest that time scale of the activation process is of order of 10 minutes , which gives α within the range the experimental estimates , see Table 1 ., In Figure 7 we compare 2D and 3D models for rn\u200a=\u200a0 . 95 and α\u200a=\u200a3 with respect to the dependence of the critical fraction of the aggregated receptors on the fraction 1/i of the cell surface occupied by the cluster ., As in the 3D case , for any fixed F and 1/i sufficiently small the activation of the cell by the local receptor aggregation is not possible , even locally ., As it is seen in Figure 7 , the minimal fraction of the aggregated receptors needed for activation in 2D case is much smaller than the minimal fraction of the aggregated receptors in the corresponding 3D case ., For clusters occupying a large portion of cell surface , the critical fraction of aggregated receptors is almost the same for 3D and 2D models ., When the diameter of the cluster becomes comparable with the thickness of the cytoplasmic layer , the activated kinase diffusion becomes 3D and the minimal clusters defined by the two models are different ., The minimum of activatory fraction in 2D case decreases asymptotically as α−2 and for parameters , used for Figure 6 , we numerically found that for α\u200a=\u200a1; for α\u200a=\u200a3; for α\u200a=\u200a10 ., For the minimal activatory fractions of receptors predicted by the membrane model is less than 10−3 ., Since the estimates based on dephosphorylation and diffusion constants suggest that α>10 , one can expect that the minimal size of the activatory aggregate is determined by the magnitude of the stochastic fluctuations ., As found by Dintzis and Vogelstein , BCRs aggregates of ten or more receptors are signaling competent 18 , 19 ., Aggregates smaller than ten BCRs possibly switch ON and OFF too fast to trigger traveling waves ., However , when OFF rate is small as in the example of Ras activation considered in 50 even the local stochastic fluctuation of kinase activity can initiate travelling wave propagation ., The spatiotemporal kinetics of proteins and other substrates regulate cell fate and signaling 51–52 ., The temporal dynamics is coupled with spatial gradients of concentrations or activity ., The gradients of kinase activity come about when phosphorylation and dephosphorylation proceed at different cellular locations ., Here , we proposed a spatially-extended B cell activation model exploiting the intrinsic chemical and geometrical properties of the system: B cells can be activated by the formation of a relatively small receptor cluster that consists of a small fraction of the total number of receptors ., This suggests that such a cluster , or clusters , can serve as a switch triggering the response which is not proportional to the signal ., These features of B cell activation bear some similarities to the process of calcium wave initiation and propagation ., The traveling wave of free calcium concentration in the cytosol can be induced by a sufficient number of strongly localized Ca2+ ions released from the internal stores ( endo- or sarcoplasmic reticulum ) to the cytosol 53 ., The released calcium interacts with the ryanodine receptors located on the surface of the internal stores leading to the further calcium release , which provides positive feedback to the process ., As found by Dintzis and Vogelstein , the interconnection of ten or more BCRs in one cluster ( immunon ) due to binding of highly polyvalent antigens is sufficient for emitting an immunogenic signal 18 , 19 ., This characterizes the ‘immunon’ as a quantum of aggregated receptors necessary for delivering an immunogenic signal ., The immunon hypothesis was then developed into a mathematical theory by Perelson and Sulzer 21 ., Though this theory takes into account only some aspects of very complicated phenomena 54 , it proved to be successful in explaining the dose-response curves obtained by Dintzis et al . 55 ., The alternative way of formation of BCR clusters is based on spreading and contraction of B cell on antigen presenting cells , which selectively sequesters high affinity antigens to a small fraction of B cell membrane ( reviewed in 1 ) ., In this mode of activation the receptor aggregate can be formed even if the antigens are monovalent ., This finding has demonstrated that although local receptor aggregation is necessary for triggering B cell activation , the physical cross-linking of receptors by antigens is dispensable ., In our study we focus on the spatial aspects of B cell receptor signaling , keeping the chemical reaction part simplified ., We expect , however , that inclusion of the omitted details of the chemical interactions would not qualitatively influence the overall dynamics , provided that the full system retains bistability ., Within the proposed model we demonstrated that displacement of the nucleus , and the resulting local thinning of the cytoplasmic layer can trigger local BCR and SFK activation ., The activity wave can then propagate throughout the rest of membrane and cytoplasmic layer ., The activation is induced by the locally increased ratio of the membrane surface to the cytoplasm volume , and the fact that nucleus is not penetrated by the Src kinases ., The nuclear movements during synapse formation are well documented experimentally in T lymphocytes 43 ., Due to the fact that the B c , ell cytoplasm layer is uniformly thin , this effect was , to the authors knowledge , not reported for B lymphocytes ., However , it is intuitive that the local narrowing of the cytoplasmic layer does take place in the early stage of immunological synapse formation when B cell spreads over APC , possibly by means of cytoskeleton reorganization as reported by 11 ., Such local narrowing of the cytoplasmic layer would facilitate B cell activation ., A similar activation effect due to the change of geometry was discussed by Meyers and colleagues 48 , who s

Introduction, Models, Results, Discussion

We proposed a spatially extended model of early events of B cell receptors ( BCR ) activation , which is based on mutual kinase-receptor interactions that are characteristic for the immune receptors and the Src family kinases ., These interactions lead to the positive feedback which , together with two nonlinearities resulting from the double phosphorylation of receptors and Michaelis-Menten dephosphorylation kinetics , are responsible for the system bistability ., We demonstrated that B cell can be activated by a formation of a tiny cluster of receptors or displacement of the nucleus ., The receptors and Src kinases are activated , first locally , in the locus of the receptor cluster or the region where the cytoplasm is the thinnest ., Then the traveling wave of activation propagates until activity spreads over the whole cell membrane ., In the models in which we assume that the kinases are free to diffuse in the cytoplasm , we found that the fraction of aggregated receptors , capable to initiate B cell activation decreases with the decreasing thickness of cytoplasm and decreasing kinase diffusion ., When kinases are restricted to the cell membrane - which is the case for most of the Src family kinases - even a cluster consisting of a tiny fraction of total receptors becomes activatory ., Interestingly , the system remains insensitive to the modest changes of total receptor level ., The model provides a plausible mechanism of B cells activation due to the formation of small receptors clusters collocalized by binding of polyvalent antigens or arising during the immune synapse formation .

B cells are activated in response to binding of appropriate ligands , which induces the aggregation of B cell receptors ., The formation of even small clusters containing less than 1% of all the receptors is sufficient for activation ., This observation led us to a model in which the receptor cluster serves only as a switch that turns on the activation process involving also the remaining receptors ., The idea of the model exploits the fact the Src kinase - BCR system is bistable , and thus its local activation may start the propagation of a traveling wave , which spreads activation over the entire membrane ., We found that the minimal size of the activatory cluster decreases with the thickness of the cytoplasm and kinase diffusion coefficient ., It is particularly small when kinases are restricted to the membrane ., These findings are consistent with the properties of B cells , which prior to activation have extremely thin cytoplasmic layer and in which Src family kinases ( interacting with the receptors ) are tethered to the membrane .

mathematics, biology

journal.pntd.0000458

When Can Antibiotic Treatments for Trachoma Be Discontinued? Graduating Communities in Three African Countries

Over 40 million doses of oral azithromycin have already been distributed to control the ocular strains of chlamydia that cause trachoma 1 ., The World Health Organization ( WHO ) advocates three annual community-wide distributions and continued treatment until clinical evidence of infection falls below a threshold where resulting blindness should not be a major public health care concern ., These distributions have proven effective in reducing infection in longitudinal studies , individual-randomized trials , and community-randomized trials 2–14 ., Studies have also suggested that in some circumstances distributions may be safely discontinued , either because infection has been eliminated , or the few remaining infections may disappear on their own 7 , 8 , 10 , 12–14 ., Laboratory testing offers the possibility of graduating communities that no longer need to receive mass antibiotics , reducing expense , side-effects , and the potential for developing drug resistance in other organisms such as Streptococcus pneumonia 13 , 15 ., Here we explore the effect of graduating communities from a treatment program , with graduation being defined as a point in time when a community will no longer receive mass antibiotic distributions because evidence of infection is below a prescribed threshold ., Less expensive diagnostic laboratory testing for infection may become available for trachoma programs in the near future ., Estimates of the prevalence of infection using PCR-based tests can be made more cost-effective by sampling individuals within communities and by pooling several samples into one test 6 , 10 , 14 , 16 , 17 ., There is hope that a low cost , point-of-care ( POC ) test will become available which will allow on-the-spot identification of infected communities 18 ., Strategies to graduate communities when the prevalence of infection has decreased below a threshold are now being tested ., However , the results will not be available for years , and only one strategy can be tested at a time ., Here we use data from three African countries to fit a stochastic mathematical model , and then predict the results of two different treatment programs , three annual mass treatments consistent with WHO recommendations versus three annual mass treatments using strategies to graduate communities ., Data were collected in three countries at baseline , and 2 and 6 months after treatment as previously described 6 , 8 , 19 ., Informed consent along with institutional review board approval was given in each of the studies as described 6 , 8 , 19 ., Although there were differences in the underlying study designs in the three countries , the similarities allow comparable data sets to be abstracted from each ., In all three sites , there was a baseline mass oral azithromycin distribution , and then no subsequent azithromycin treatment until after the 6 month collection ., In all three sites , all individuals aged one year and older were offered a single dose of oral azithromycin during mass treatments ., Pregnant women and those allergic to macrolides were offered alternative treatment ( topical tetracycline ) ., The right upper conjunctiva of each child was swabbed , and the swab was then tested for the presence of chlamydial DNA using Amplicor PCR ( Roche Diagnostics , Branchburg , NJ ) ., In Ethiopia , sixteen villages in the Gurage region of southern Ethiopia were randomly selected and 1–5 year old children , the ages most likely to harbor infection , were monitored 6 ., In Tanzania and in The Gambia , swabs were taken from all individuals of all ages , although for the purposes of this study , we use only swabs taken from children aged 1–10 years ., In Tanzania and The Gambia , 15 balozis ( a household administrative unit consisting of about 10 households ) , and 14 villages , respectively , were followed 7 , 8 ., Previously , we constructed a simple stochastic SIS model ( Susceptible , Infected , Susceptible ) of ocular chlamydial infection in a core group of children 20 , 21 ., In this report , the previous model was modified to include infection from outside of the community , and a transmission term that can vary between communities in the same region as a normally distributed random effect ( to account for the known variability of communities ) ., Treatment strategies that allowed graduation of communities when observed infection fell below a certain threshold , as detected by a POC test , were incorporated ., Specifically , we constructed a Markov model by letting denote the probability that there are i infected individuals in the population at time t ( where i varies from 0 to N ) ., At scheduled treatments , we assumed that each infected individual had an 80% chance of being treated ( the WHO-recommended coverage rate ) , and if treated would revert to being susceptible ., Between periodic mass treatments , the model is a standard continuous time Markov process ., We assumed equilibrium at baseline , that infected individuals recover naturally at rate γ , that uninfected individuals become infected at rate β I/N from sources within the community ( with β\u200a=\u200aR0·γ ) , and at a rate of ν from outside the community ( with ν decreasing to zero once wide-spread programs have begun ) , leading to the following set of N+1 Kolmogorov-forward equations: For clarity , we expressed β in terms of the basic reproductive number , R0 ( where β\u200a=\u200aR0·γ ) ., Note that R0 is defined as the mean number of secondary infectious cases caused by a single infectious case in an otherwise completely susceptible community 22 ., At the time of the scheduled periodic mass treatments , we assume that each infected individual has a probability c of being treated ( the effective coverage ) , with the number of infections post treatment being drawn from the corresponding binomial distribution ., Parameters for this stochastic model were fitted to baseline and 6-month data for each country using maximum likelihood estimation ., We initiated simulations at the average prevalence for that region , and simulated the Kolmogorov-forward equations for 40 years to allow the distribution of prevalence to approximate the pre-treatment distribution at time point zero ., We also initiated the model at the observed 2-month prevalence and simulated the equations for 4 months to estimate the expected distribution of prevalence at 6-months ., The total log-likelihood was the sum of the baseline and the 6 month log-likelihoods for each of the communities in the area ., Note that any event that occurred between baseline and 2-months ( such as treatment , or mass re-infection from travel ) would not bias these results 8 ., Based on these Kolmogorov equations , the values of the parameters R0 , standard deviation of R0 ( thus treating R0 as a random effect ) , γ , and ν that maximized the probability of obtaining the observed baseline and 6-month data for that country ( i . e . the likelihood ) were determined using an iterative , hill-climbing algorithm ., Numerical optimizations were repeated a minimum of 4 times from random starting points ( because of the possibility any single run could converge to a local , rather than the global , maximum ) ; each iteration converged to the same value ., Furthermore , a grid search did not reveal any greater maxima ., The variance of these estimates was assessed by inverting the Hessian of the log-likelihood evaluated at the maximum likelihood estimate ( although note that the 95% confidence interval could not include ν\u200a=\u200a0 , because in each country , a community went from 0 infections at 2 months to >0 infections at 6 months ) ., Coverage was assumed to be 80% , and the average population size was set at the mean of empirical results from the surveyed communities in that region ( Table 1 ) ., For sensitivity analyses , we ran 1000 simulations with the fitted parameters under different scenarios ., We varied the sensitivity and specificity of the POC test , as well as the threshold for declaring graduation , keeping R0 , standard deviation of R0 , γ , and ν at the optima found for that region ., If not being varied , the sensitivity and specificity of a POC diagnostic test were set at 70% and 99% respectively , and the prevalence threshold for graduating communities of ≤5% ., All analyses were carried out in Mathematica 5 . 2 ., Characteristics of the observed data from the three countries are shown in Table, 1 . Tanzania had a higher mean prevalence than The Gambia , but there was a larger variation in prevalence in Gambian communities , with some having as high as 40% and nearly two-thirds not having any infection identified ., Ethiopia had high prevalences in all communities ., These areas of Tanzania and The Gambia would be considered meso-endemic or hypo-endemic areas , and the area of Ethiopia hyper-endemic ., Predicted model parameters are displayed in Table, 2 . The mean basic reproduction number , R0 , and the estimate of the standard deviation of R0 ( treating the particular R0 in a community as a random effect ) , the recovery rate , γ , and the exogenous infection rate , ν , are displayed for the data sets from each country , along with the 95% confidence intervals for these parameter estimates ., The Ethiopian data resulted in the highest estimated mean R0 , while the largest coefficient of variation for R0 was found in The Gambia , consistent with the observed heterogeneity between villages found there ., The exogenous infection rate is dependent not only on the overall prevalence of infection in an area , but the proximity of the defined communities in each country ., The WHO-recommended strategy of three annual mass treatments ( before subsequent re-evaluation for further treatment ) resulted in an estimated mean prevalence of 0 . 0% in Tanzanian , 2 . 4% in Gambian , and 12 . 9% in Ethiopian communities at three years ( Figure 1A , 1B , and 1C ) ., In addition , results showed complete elimination of infection in 99 . 8% of Tanzanian communities , 93 . 3% of Gambian communities , and 39 . 8% of Ethiopian communities ( Figure 2A , 2B , and 2C ) ., With a strategy of graduating communities from further mass antibiotics when the observed prevalence of infection falls below 5% , the estimated mean prevalence at 3 years was 0 . 3% in Tanzania , 3 . 9% in The Gambia , and 14 . 4% in Ethiopia ( Figure 1A , 1B , and 1C ) ., The proportion of communities where elimination would be expected was 97 . 6% in Tanzania , 88 . 8% in The Gambia , and 30 . 0% in Ethiopia ( Figure 2A , 2B , and 2C ) ., Elimination in each of the three countries is affected by the threshold for graduation , sensitivity and specificity of the diagnostic test , R0 , recovery rate , and the exogenous rate ( Figures 3 and 4 ) ., Increasing the threshold for graduation marginally increased the median prevalence of infection at 36 months ( Figure 3 ) and the proportion of communities in which infection could still be identified ( Figure 4 ) ., As one might expect , increasing either R0 or the exogenous rate increases prevalence and decreases the proportion of villages which attain infection elimination ( Figures 3 and 4 ) ., However , these sensitivity analyses suggest that the simulation results are not dependent on the exact choice of the sensitivity and specificity of the diagnostic test ., Increasing the recovery rate ( γ ) allows the prevalence of infection to return to the equilibrium of the region more rapidly ., Figure 5 shows the amount of antibiotics used during three years of the graduation strategy , compared to three years of annual treatment ., The graduation strategy , over the three year period , would reduce treatments by 63% in the Tanzanian communities , 56% in Gambian communities , and 11% in the Ethiopian communities , compared to annual treatments ., These reductions in treatments are also affected by alterations in the threshold for graduation , test sensitivity , and test specificity ( Figure 6 ) ., Increasing the specificity has a marked reduction in antibiotic use ( Figure 6C ) ., Note that if the specificity falls low enough , then more than 5% infections will be “observed” due to false positives ., In this case , there will be no graduations , regardless of the true prevalence of infection ., The WHOs recommendation of a minimum of three annual antibiotic distributions with at least 80% coverage should work well in most areas ., In the models representing Gambian and Tanzanian communities , infection was eliminated in more than 95% of communities during the first three years of a WHO-recommended treatment program , consistent with later observed results in the Tanzanian communities 13 ., Note that the WHO recommends reassessment at this point , and further antibiotic distributions if the clinical signs of infection are still present ., Most communities in these areas will have eliminated infection in children after 3 annual treatments ., In hyper-endemic areas like the studied in Ethiopia , we expect 3 years of treatment to eliminate infection in 40% of communities ., This is consistent with a recent community-randomized clinical trial in a nearby area of Ethiopia 14 ., We have previously suggested that biannual treatment in hyper-endemic areas may be a more successful strategy to eliminate infection , at least in a 2–5 year time frame 14 , 23 ., A strategy that graduates communities when the observed prevalence of infection in children falls below 5% appears to be reasonable ., The models presented here predict that treatment could be discontinued in the vast majority of communities similar to those studied in Tanzania and The Gambia ., Graduating communities had only a minimal effect on the mean prevalence of infection found in the area at the end of a 3-year trachoma program ., This is because trachoma transmission is low in many of the Tanzanian and Gambian communities , and because the graduation strategy favors re-treating the communities which have higher transmission ., We predict that antibiotic use could be reduced 2 to 3-fold by introducing such measures , and even more if the graduation threshold were applied before the first treatment—here , we treated all communities at least once ., The goal of trachoma programs is to efficiently use resources to eliminate trachoma as a public health concern ., The cost of providing treatment for a community in Africa , estimated at $0 . 25–1 . 00 per individual , includes the costs of antibiotics , as well as wages for health workers and administrators 24 ., These costs can be expected to result in a 75–90% reduction in ocular chlamydial prevalence at one year after treatment , per mass distribution 7 , 9 , 10 , 14 ., However , the cost-effectiveness of a program is determined by more than treatment costs and reduction of infection ., Trachoma programs must decide which communities to treat , and when to stop treatment ., As shown in this study , this decision will require knowledge of the prevalence of chlamydial infection , since areas with highly prevalent chlamydial infection will likely require longer periods of treatment ., The costs of diagnosing ocular chlamydial infection vary ., Currently , chlamydial infection is usually assessed with the clinical conjunctival exam 25 ., Although the clinical exam is relatively inexpensive ( costs include only the examiners time ) , it is not particularly accurate in identifying chlamydial infection , particularly after treatment 26 ., Chlamydial PCR is an alternative , though it is expensive when performed for an individual ( $12–20 per test , personal communication , V . Cevallos , F . I . Proctor Foundation ) ., Because trachoma programs base treatment decisions not on individuals , but on the entire community , samples could be pooled for PCR testing , which would result in considerable savings ( $2–3 per individual tested , assuming 5 samples per pool ) 16 , 17 ., A point of care test for chlamydia may also provide a less expensive method for diagnosing ocular chlamydia ( $0 . 70 per individual tested ) 18 ., Besides the inclusion of these additional costs , there are additional outcomes that play a role in the cost-effectiveness of trachoma control ., Specifically , while the outcomes of antibiotic treatment programs can easily be measured as the reduction in ocular chlamydial infection , the true outcome of interest is the reduction of blindness ., However , the progression from infection to blindness in individuals can take decades , and rates are difficult to assess ., Of note , treatment efforts besides antibiotics , such as promotion of face-washing and construction of latrines , may also be administered by trachoma programs , though the effectiveness of these measures has not been proven , making their role in cost-effectiveness difficult to study at present ., Mathematical models of trachoma control can be useful in determining optimal distribution strategies , and predicting what is expected from specific programs ., Stochastic models have an advantage over deterministic models in this setting , because they include the effect of chance seen with the low levels of infection in communities near elimination ., Models must also include variation between regions , since strategies that have proven effective in hypo-endemic areas may not translate to hyper-endemic areas ., Models should also include variation between communities in the same region ., To incorporate this community variation into a realistic model , we included a community-level random effect in transmission ( specifically , variation in the underlying R0 for each community ) ., The results confirm that there is likely some variability between neighboring communities even beyond that which would be expected by chance ., Incorporating further mixing structure , for example preferential mixing within a household , might create more variance between simulated communities , reducing the need to introduce a random effect at the community-level as we have done here ., The graduation strategy appears to be effective over a wide range of parameter choices in the Tanzanian and the Gambian communities studied here ., More endemic areas such as those found in Ethiopia , appear more sensitive to parameter choices , and are far more vulnerable to reinfection ., There is a long history of the use of mathematical models of the transmission of infectious diseases ., These are often used to make theoretical points , which do not require precise parameter estimates ., Even when parameters are fitted to data , they are rarely fitted to data from more than one community ., Community trials in trachoma control have provided longitudinal prevalence data in 14–16 communities in each of three sub-Saharan African countries ., There are still many limitations of this simple model which can be explored in the future ., We have included only transmission in children , the age group known to harbor most of the infection , but adults could be included with available data ., We have assumed that individuals do not gain or lose immunity over the 3 year program , and that there is no antibiotic resistance; the importance of these factors has been debated 11 , 27 , 28 ., Our model defines the rate of contact to be independent of the population size ( since the mass action term is divided by the effective population size N ) —other assumptions such as density dependent transmission are also possible ., Finally , we have assumed that treatment of children on a given visit is independent of past treatment history ., Were treatments to miss the same 20% of children each time , then infection might linger longer in this subgroup ., Repeated mass oral azithromycin distribution will be effective in reducing the prevalence of ocular chlamydia ., But distribution costs are high , and side effects and the potential for resistance are important issues ., Graduating communities when diagnostic testing reveals a prevalence of ocular chlamydia of 5% or less appears to be an appropriate strategy in most areas ., More frequent treatment and different stopping rules may be required for more hyper-endemic areas similar to the region of Ethiopia studied here .

Introduction, Methods, Results, Discussion

Repeated mass azithromycin distributions are effective in controlling the ocular strains of chlamydia that cause trachoma ., However , it is unclear when treatments can be discontinued ., Investigators have proposed graduating communities when the prevalence of infection identified in children decreases below a threshold ., While this can be tested empirically , results will not be available for years ., Here we use a mathematical model to predict results with different graduation strategies in three African countries ., A stochastic model of trachoma transmission was constructed , using the parameters with the maximum likelihood of obtaining results observed from studies in Tanzania ( with 16% infection in children pre-treatment ) , The Gambia ( 9% ) , and Ethiopia ( 64% ) ., The expected prevalence of infection at 3 years was obtained , given different thresholds for graduation and varying the characteristics of the diagnostic test ., The model projects that three annual treatments at 80% coverage would reduce the mean prevalence of infection to 0 . 03% in Tanzanian , 2 . 4% in Gambian , and 12 . 9% in the Ethiopian communities ., If communities graduate when the prevalence of infection falls below 5% , then the mean prevalence at 3 years with the new strategy would be 0 . 3% , 3 . 9% , and 14 . 4% , respectively ., Graduations reduced antibiotic usage by 63% in Tanzania , 56% in The Gambia , and 11% in Ethiopia ., Models suggest that graduating communities from a program when the infection is reduced to 5% is a reasonable strategy and could reduce the amount of antibiotic distributed in some areas by more than 2-fold .

Trachoma , the major cause of infectious blindness in the world , occurs when repeated infections of the ocular strains of Chlamydia trachomatis lead to a cascade of conjunctival scarring , in-turned eyelids and eyelashes , and eventually blindness due to corneal opacity ., To reduce the prevalence of infection , the World Health Organization ( WHO ) advocates at least three annual community-wide distributions of oral antibiotics in affected areas ., This approach has proven effective , but there is room to explore other treatment strategies which reduce the use of antibiotics ., Here , we used mathematical models and data from three trachoma-endemic countries ( Tanzania , The Gambia , and Ethiopia ) to analyze different treatment strategies ., In the simulations , we show that a graduation strategy can reduce antibiotic distributions more than 2-fold in moderately affected areas ., Both treatment strategies provide favorable results in reducing the prevalence of ocular chlamydia , but high costs and the potential for resistance are important issues to consider when administering mass doses of antibiotics .

infectious diseases, infectious diseases/neglected tropical diseases, public health and epidemiology/epidemiology, public health and epidemiology/infectious diseases, mathematics/mathematical computing, mathematics/statistics, infectious diseases/epidemiology and control of infectious diseases

journal.pntd.0006161

Unreported cases in the 2014-2016 Ebola epidemic: Spatiotemporal variation, and implications for estimating transmission

Despite the unprecedented scale of the 2014–2016 Ebola epidemic in West Africa 1 , significant uncertainty remains about the precise number of cases involved , and about the spatiotemporal dynamics of transmission 2–8 ., The numbers of non-hospitalized , community-based cases over time and among locations , are particularly uncertain ., Ebola cases who become sick and die in the community are at increased risk of onward transmission , with caregiving and fluid contact as especially important transmission routes 9–11 ., Accurate case counts over space and time , which include non-hospitalized cases , are important for estimating disease transmission rates , and identifying response strategies 6 , 12–14 ., A central problem is that non-hospitalized cases are less likely to be reported , compared to hospitalized cases: the WHO 2014–2016 Ebola case line-list predominantly includes cases who have been hospitalized 15 ., As a result , non-hospitalized cases are underrepresented in Ebola surveillance data , and the observed pattern of cases over time reflects the dynamics of hospital bed capacity and access to formal care as well as the underlying trajectory of the epidemic 16 ., A community case may be detected outside of clinical care through interaction with other epidemic response measures , for instance by inclusion in contact tracing , or by laboratory testing of a specimen collected before or after death ., However , during the recent epidemic these measures were implemented heterogeneously , due to constraints on public health systems in the affected countries ., Estimates of the reporting rate—the proportion of the total number of infected individuals over a specified time period that are reported as cases—range from 0 . 33 17 to 0 . 83 18 , which bound the initial estimate by the United States Centers for Disease Control ( CDC ) of 0 . 40 19 ., The differences among these estimates may stem in part from the different analytical methods used , including capture recapture methods 17 , inference from viral sequence data 18 and comparison of hospitalized cases with the projections of a compartmental epidemic model 19 ., Reporting rates may vary significantly over time and location ., For instance , temporal fluctuations in the number of available hospital beds may cause corresponding fluctuations in case ascertainment rates 11 ., Increased case ascertainment through increased access to care may itself lower transmission rates , because hospitalized cases are less likely to transmit 10 ., The number of non-hospitalized cases , and the proportion of non-hospitalized cases relative to the total case burden , are also important indicators of the impact of interventions 20 , as fewer community based cases reflects better access to hospital care , or a downturn in transmission rates , or both ., Estimates of reporting rates for non-hospitalized cases may not be a priority at the beginning of an outbreak ., During an outbreak , such infrastructure is difficult to establish at an appropriate scale , beyond informal reports , until resources are allocated according to case data from patient hospitalizations ., As a result , the non-hospitalized cases that occur in the initial weeks of an outbreak , in a new area , are often not extensively captured ., Thus , transmission and reporting may be intertwined , making it difficult to tell whether a change in incidence is driven by contagion or surveillance thereof , although the relative contributions of the two processes can have important implications for public health responses ., Estimates of disease transmission dynamics during the 2014–2016 Ebola epidemic drove recommendations for a variety of containment strategies—including contact tracing , quarantine , and safe burial—based on estimates of the reproductive number of the disease in different contexts 21 ., However , it is unclear how robust these analyses are to temporal and spatial fluctuations in ascertainment bias in case time series , especially in situations where many cases are unreported ., Here we disentangle reporting and epidemic dynamics for the recent Ebola epidemic in Sierra Leone using individual-level records of burials performed in the Safe and Dignified Burial ( SDB ) program that was coordinated by the International Federation of Red Cross and Red Crescent Societies ( IFRC ) in Sierra Leone ., Safe and dignified burial was required by law for every community death in Sierra Leone , during the timespan of the data analyzed here ( Oct 20 , 2014 –March 30 , 2015 ) ., Safe burials were to be conducted in the same way regardless of the suspected cause of death , including collecting a skin swab sample for Ebola testing 22 ., However , in practice safe burial was not conducted for every community death , and not all community deaths were tested for Ebola ., Non-hospitalized Ebola deaths that did not interface with the SDB program , or where a swab sample was not tested , represent a primary source of underreporting in the epidemic , as well as a significant potential source of onward transmission ., Below we describe how the prevalence of Ebola in burial swab samples can be used to estimate the total number of non-hospitalized cases , which can in turn be used to estimate location- and time-specific reporting rates ., Using these data , we examine how reporting rates varied over time and across districts , and reconstruct the epidemic curves in each district , accounting for unreported cases ., Finally , we address the question of how spatiotemporal dynamics in reporting might affect estimates of disease transmission rates ., Our results agree with previous studies 17–19 showing that a substantial fraction of Ebola cases in the 2014–2015 epidemic were unreported ., In addition , we find substantial systematic variation across geographic regions and over time in the level of underreporting ., In some cases , this variation was sufficient to systematically alter estimates of the reproduction number of the epidemic ., This study relies solely on retrospective analysis of de-identified data , which was collected as part of a humanitarian response and not for research purposes , and is exempt from ethics committee approval ., Ebola prevalence in the SDB data was strongly correlated with reported prevalence in the WHO situation reports , however , the relationship between burial prevalence and reported prevalence varied across districts ( Fig 2 , Table 1 ) ., In the capital district of Western Area Urban and the adjacent district of Western Area Rural , burial prevalence accrued more slowly under increasing reported prevalence , while in the districts of Bo and Bombali , burial prevalence rose more steeply under increases in reported prevalence ., This is consistent with the hypothesis that reported Ebola prevalence outside of the capital was indicative of a proportionally larger number of unreported , non-hospitalized cases ( Fig 3 , top row ) ., Fitting the time-varying hierarchical binomial model to the data quantified variation in reporting rate over time and among districts ( Fig 3 , bottom row ) ., Midline estimated reporting rate in Western Area Urban on Oct 20 , 2014 was 0 . 68 ( credible interval: {0 . 45 , 0 . 73} ) ., However , estimates for the same date in other districts were significantly lower , at 0 . 55 , 0 . 27 and 0 . 33 , for Western Area Rural , Bombali , and Bo respectively ., Reporting rates increased over the course of the epidemic in all districts in the data , so that by March 30 , 2015 , reporting rates in all districts were estimated to be at or above Scarpino et al . ’s estimate of 0 . 83 18 ., However , reporting rates outside the capital district stayed lower for longer before beginning to increase ., For instance , while the midline estimated reporting rate in Western Area Urban on the week of January 5 , 2015 had risen to 0 . 96 ( credible interval: {0 . 93 , 0 . 98} ) , estimates for the same date in Bombali and Bo remained close to their initial values ( see Fig 3 , bottom row ) ., The reproduction number of Ebola estimated from the WHO data varied over time and among districts , as in previous estimates 4 ., The transmission rate of Ebola may be particularly variable in a community context , due in part to variation in the relative risk of different transmission routes that may occur in a community setting , particularly care-giving outside of hospital 29 ., To quantify the effect of reporting variation on estimates of Rt in Ebola incidence data , we compared results for time series that were corrected for underreporting ( using our midline estimates of reporting rate in each district over time ) with uncorrected time series ( Fig 4 ) ., While in the Western Area districts ( around the capital of Freetown ) correcting for temporally variable underreporting did not significantly change estimates of Rt , in Bo and Bombali the corrected estimates diverged significantly from the uncorrected estimates , particularly earlier in the epidemic ., Accounting for variation in reporting can significantly modify our understanding of disease spread and control , but variation in reporting rate over space and time is rarely accounted for during analyses of epidemic dynamics ., In particular , most models of Ebola transmission to date have assumed constant reporting rates across space and over time 5 , 6 , 12 , 17–19 , 21 , 30 , 31 ., Here we used community-based data on non-hospitalized deaths to infer variation in patterns of reporting , finding significant spatiotemporal variability in case ascertainment ., This spatiotemporal variability echoes recently described heterogeneity in transmission patterns in non-hospitalized Ebola data , where the importance of superspreading events was demonstrated 32 ., Correcting for these reporting variations improved the accuracy and precision of estimates of transmission patterns ., How do fluctuations in reporting rate influence estimates of the reproductive rate of an epidemic ?, As the number of cases becomes large , the posterior mean of the estimated reproductive number approaches, Rt=∑s=t−τ+1tρsIs∑s=t−τ+1t∑u=1twuρt−uIt−u, ( 5 ), where τ is the number of time units over which the reproductive number is assumed constant , ρt is the reporting rate at time t , It is the number infectious at time t , and wt is the generation time distribution of the disease , representing the fraction of secondary cases that originate from the primary case t time units after the primary case becomes infectious 28 ., If the reporting rate is constant , ρt = ρ0 , then reporting does not affect estimates of Rt , because the constant ρ0 cancels out in the numerator and denominator of ( 5 ) ., However , variable reporting rates are confounded with generation time , and shape estimates of Rt in the same ways that variation in generation time can 33 ., For example , a sharp increase in reporting rate will lead to an overestimate of Rt by inflating the numerator relative to the denominator ., In the context of field outbreak response , such an increase in reporting might be caused by increased allocation of resources to contact tracing , or an increase in hospital bed capacity ., Variation in reporting rates can also affect measures of uncertainty in estimates of Rt ., For large numbers of cases , the coefficient of variation in the estimate is given by, CV ( Rt ) =1∑s=t−τ+1tρsIs, ( 6 ), which is equivalent to the standard deviation of the estimated Rt divided by its mean , and thus can affect the width of the credible intervals for Rt ., The denominator of ( 6 ) is proportional to the covariance between reporting and incidence over a time interval of length τ ., Thus , if reporting and incidence covary , the credible interval on estimates of the reproductive number will shrink when corrected for underreporting , all else equal ., Future epidemic models may be improved by incorporating a process-based representation of reporting dynamics ., More specifically , future models could treat reporting rate as a state variable , driven by human behaviors associated with both disease spread and public health response , and including inequities in access to medical care ., Improving our quantitative understanding of what determines reporting rates could also allow stronger links between field outbreak response teams and modeling teams , which would improve contextualization and understanding of data limitations , with the potential to improve predictive models of epidemics and enhance the design of control measures .

Introduction, Methods, Results and discussion

In the recent 2014–2016 Ebola epidemic in West Africa , non-hospitalized cases were an important component of the chain of transmission ., However , non-hospitalized cases are at increased risk of going unreported because of barriers to access to healthcare ., Furthermore , underreporting rates may fluctuate over space and time , biasing estimates of disease transmission rates , which are important for understanding spread and planning control measures ., We performed a retrospective analysis on community deaths during the recent Ebola epidemic in Sierra Leone to estimate the number of unreported non-hospitalized cases , and to quantify how Ebola reporting rates varied across locations and over time ., We then tested if variation in reporting rates affected the estimates of disease transmission rates that were used in surveillance and response ., We found significant variation in reporting rates among districts , and district-specific rates of increase in reporting over time ., Correcting time series of numbers of cases for variable reporting rates led , in some instances , to different estimates of the time-varying reproduction number of the epidemic , particularly outside the capital ., Future analyses that compare Ebola transmission rates over time and across locations may be improved by considering the impacts of differential reporting rates .

Epidemics are defined by a surge of cases of a disease , yet often a significant number of cases in an epidemic are never reported , for example because not all infected individuals have access to medical care ., This underreporting can introduce bias into analyses of disease spread , by distorting patterns in where and when the most cases are observed ., Conversely , quantifying underreporting can improve epidemic forecasts and containment strategies ., In this study , we analyze data from the recent Ebola epidemic in West Africa , including the time , location and Ebola status of 6491 individual community burials , conducted over 25 weeks in four districts in Sierra Leone ., We quantify how reporting rates varied over space and time , and show that estimates of transmission rates that are corrected for dynamic underreporting diverge significantly from uncorrected estimates , particularly earlier in the epidemic and outside the capital .

death rates, medicine and health sciences, infectious disease epidemiology, geographical locations, health care, population biology, infectious disease control, africa, public and occupational health, infectious diseases, geography, epidemiology, sierra leone, hospitals, people and places, population metrics, health care facilities, urban areas, disease dynamics, earth sciences, geographic areas, biology and life sciences

journal.pntd.0002099

Changes in Gene Expression of Pial Vessels of the Blood Brain Barrier during Murine Neurocysticercosis

The blood brain barrier ( BBB ) separates the peripheral circulation from the CNS and plays a critical role in homeostasis of the CNS environment ., In the healthy brain BBB selectively restricts molecular and cellular trafficking between the blood and brain tissue and between blood and cerebrospinal fluid ( CSF ) 1 ., The restrictive properties are largely controlled by specialized endothelial cells of the CNS vasculature which differ from those in the peripheral vasculature in terms of polarized expression of various transport systems , low transcytosis activity , high mitochondrial volume and sealing of the paracellular cleft between endothelial cells by continuous strands of interendothelial junction proteins including tight junctions 1 ., However , additional components of the BBB are present in different CNS compartments and vary according to their anatomical location in the CNS and nature of the vasculature ., The blood vessels present in leptomeninges ( pia ) in subarachnoid space are collectively termed pial vessels ., The BBB associated with pial vessels in adult brain are largely devoid of pericytes , astrocytic endfeet processes , additional basement membranes and parenchymal tissue in comparison to that of parenchymal vessels 2 , 3 , 4 ., Infection of the CNS leads to changes in barrier properties of the BBB allowing the leakage of serum components ( edema ) and infiltration of leukocytes resulting in CNS pathology 5 , 6 ., In addition , the BBB transport system is also affected further disturbing the homeostasis of the CNS environment 1 ., Neurocysticercosis ( NCC ) is a CNS infection caused by the metacestode ( larva ) of the tapeworm Taenia solium ., It is one of the most common parasitic infections of the CNS and a major cause of acquired epilepsy worldwide 7 ., Depending upon the size , location , and number of parasites as well as sex , age and immune status of the host , there are differences in disease severity and pathologies 8 ., Epidemiological studies show that among the various forms of NCC , subarachnoid NCC has the worst outcome and is associated with poor prognosis , more resistance to anti-helminthic drugs and more severe inflammation 9 ., The chronic inflammation of the vasculature and arachnoid thickening ( chronic basal meningitis ) leads to blockade of CSF further contributing to CNS pathology 8 ., Similarly , using a murine model for NCC by infection with the highly related parasite Metacestoides corti , prior studies from our laboratory have demonstrated that breakdown of the BBB and associated leukocyte infiltration depends on many criteria including the anatomical site , type of vascular bed , and infiltrating cell phenotype 6 , 10 , 11 ., Assessment of the integrity of the BBB by changes in the architecture of interendothelial junction proteins and leakage of serum proteins revealed that the BBB associated with pial vessels were compromised earlier and to a greater extent in comparison to the BBB associated with vessels present in other CNS compartments 12 , 13 ., In addition , previous studies have shown that during murine NCC , the temporal pattern of infiltrating leukocyte subsets is characterized by a large infiltration of macrophages and γδ T cells followed by αβ T cells and lastly B cells 14 ., Further characterization of leukocyte subset infiltration in different CNS compartments has established that the majority of the infiltration occurs via pial vessels 13 ., There is a lack of detailed analysis of BBB disruption in vivo in a CNS compartment-specific manner ., To address this deficiency and to obtain insights into changes occurring only to pial vessels , we designed a microarray-based , comprehensive study to analyze the changes in gene expression associated with the BBB comprised of pial vessels of the leptomeninges and subarachnoid spaces ., We utilized laser capture microdissection microscopy ( LCM ) to isolate pial vessels from mock- and parasite-infected mice and performed microarray analyses ., Our transcriptome data indicate an altered expression of genes related to the immune response and to physiological function and collectively provide insight into the dysfunction of the BBB during murine NCC associated with pial vessels ., This study was conducted in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the U . S . National Institutes of Health ., Experiments were carried out under the approved guidelines of the Institutional Animal Care and Use Committee ( IACUC ) , University of Texas at San Antonio ( approved IACUC protocol number MU003-07/11A0 ) ., Female Balb/c mice were purchased from National Cancer Institute program ( Bethesda , MD ) ., Parasite maintenance and intracranial infection were performed using a protocol developed earlier 14 ., M . corti metacestodes were maintained by serial intraperitoneal ( i . p . ) inoculation of 8- to 12-week-old female BALB/c mice ., For intracranial inoculations , parasites were aseptically collected from the peritoneal cavity of mice that had been infected for about 4–6 months ., Harvested parasites were extensively washed in HBSS ., After that , the metacestodes ( 70 microorganisms ) were suspended in 50 µl of HBSS and injected intracranially into 3–5-week-old female BALB/c mice using a 1-mL syringe and a 25-gauge needle using our protocol developed earlier ., The needle was inserted to a 2-mm depth at the junction of the superior sagittal and the transverse sutures ., This allows insertion of the needle into a protective cuff avoiding penetration of the brain tissue ., Control mice were injected with 50 µl sterile HBSS using the same protocol ., Before intracranial inoculation , mice were anesthetized intramuscularly with 50 µl mixture of ketamine HCL and xylazine ( 30 mg/ml ketamine and 4 mg/ml xylazine ) ., Animals were sacrificed at 3 weeks after inoculation ., Before sacrifice , animals were anesthetized with 50 µl of mixture of ketamine HCL and xylazine ., The thoracic cage was opened and 100–125 µl of a Rhodamine Red-X conjugated Ricinus communis agglutinin ( Rh-RCA ) lectin ( Vector Lab ) was injected through the left ventricle in heart ., After 2 minutes of Rh-RCA injection , perfusion was performed through the left ventricle with 15 mL of cold HBSS 15 ., Perfused brains were immediately removed , embedded in O . C . T . resin ( Sakura , Torrance , CA ) and snap frozen in 2-methyl butane ( Fisher Scientific , Pittsburgh , PA ) contained/cooled in liquid nitrogen and stored at −80°C for later use ., 10 µm thick horizontal cryosections were obtained from each brain on polyethylene naphthalate membrane slides ( Leica Microsystems , Wetzlar , Germany ) ., The tissues were fixed in −20°C acetone for 20 seconds and kept in dry ice ., Subsequently brain sections were dehydrated in 70% ( 10 s ) , 95% ( 20 s ) , 100% ( 3x , 30 s each ) and xylene ( 2x , 30 sec ) ., After dehydration , the slides were kept in desiccators until the time of dissection to avoid the humidity ., LCM was performed with Leica LMD 7000 micro systems ( Leica Microsystems , Wetzlar Germany ) as described previously 16 ., From LCM isolated endothelial cells , RNA was extracted with Pico Pure RNA isolation kit ( Arcturus Bioscience , Mountain View , CA ) according to manufacturers protocol ., DNase ( Qiagen , Valencia , CA ) treatment was performed directly within the purification column to remove any possible genomic contamination during the RNA extraction process ., The quality of the RNA was inspected with Agilent 2100 Bioanalzyer and NanoDrop ND-1000 ., Samples passing quality control assessment were then subjected to linear amplification and subsequently labeled with NuGEN Ovation Aminoallyl RNA Amplification and Labeling System ( NuGEN Technologies , San Carlos , CA ) as per manufacturers instructions ., Arrays were printed at the Duke Microarray Facility using the Genomics Solutions OmniGrid 100 Arrayer and mouse genome oligo set ( version4 . 0 ) ., The Mus musculus Operon v4 . 0 spotted microarray contains 35 , 852 longmer probes representing 25 , 000 genes and about 38 , 000 gene transcripts ( Operon Biotechnologies , Huntsville , AL ) ., The amplified and labeled product was hybridized to Mus musculus Operon v4 . 0 spotted microarray according to the manufacture protocol at 42°C with the MAUI hybridization system ( BioMicro Systems , MAUI hybridization System , Salt Lake City , Utah ) ., The array was then washed at increasing stringencies and scanned on a GenePix 4000B microarray scanner ( Axon Instruments , Foster City , CA ) ., The Genespring 11 program ( Agilent Technologies , Redwood City , CA ) was used to perform data processing and statistical analysis ., Intensity-dependent ( Lowess ) normalization was done on the entire data set ., To assess the quality of a data set , a principle component analysis was performed on samples on expression of all genes with mean centering and scaling ., Datasets were filtered based on values and probe sets with background-subtracted intensity of 44 or less were excluded from the analysis ., Subsequently , t-test analysis was performed to calculate the p-values using an asymptotic method and Benjamini-Hochberg , for multiple testing correction ., Differentially expressed probe sets were selected based on volcano plot with a 2-fold change and p-value cut off of 0 . 05 ., Differentially expressed genes were then clustered using Average Linkage with Pearson Correlation as the similarity measurement ., Molecular networks of the selected molecules and specific pathways were analyzed through Ingenuity Pathway Analysis software ( Agilent Technologies , Redwood City , CA ) ., RNA obtained from LCM isolated endothelial cells ( as described above ) was subjected to linear amplification by the WT-Ovation Pico System ( Nugen technology , San Carlos , CA ) ., Resulting cDNA was loaded onto Taq-Man Low Density Arrays ( Applied Biosystems , CA ) microfluidic cards either preloaded with fluorogenic probes and custom-designed primers and housekeeping genes β-actin , ribosomal 18S , and GAPDH ( glyceraldehyde 3-phosphate dehydrogenase ) 17 or commercially available Mouse Immune Array ( catalog number – 4367786 , Applied Biosystems , CA ) ., These plates were then loaded on an ABI Prism 7900 HT Sequence Detection System ( Applied Biosystems , CA ) ., The target expression levels were normalized to the levels of the house keeping genes 18S , β-actin and GAPDH in the same sample ., Expression of each specific gene in infected samples over mock was calculated by ΔΔCt method and results are represented as ΔΔCt over mock 18 ., Tissue preparation and immunofluorescence ( IF ) staining was performed using our protocol as described previously 13 ., Animals were sacrificed at 3 weeks after inoculation ., Before sacrifice , animals were anesthetized with 50 µl of mouse cocktail and perfused through the left ventricle with 15 mL of cold PBS ., Perfused brains were immediately removed , embedded in O . C . T . resin ( Sakura , Torrance , CA ) and stored at −80°C ., Serial horizontal cryosections of 10 µm in thickness were placed on saline prep slides ( Sigma-Aldrich , St . Louis , MO ) ., The slides were air dried overnight and fixed in fresh acetone for 20 s at room temperature ( rt ) ., Acetone-fixed sections were wrapped in aluminum foil and stored at −80°C or processed immediately for immunofluorescence ., Briefly , tissues were fixed in −20°C acetone for 10 min and then hydrated in PBS ., Non-specific immunoglobulin binding was blocked by 30 min incubation at rt with 10% serum from the same species from which the fluorochrome conjugated antibodies ( secondary antibodies ) were derived ., Sections were incubated for 40 min with primary antibodies diluted in 3% serum from the host of secondary antibody ., Sections were washed 7× for 3 min each after incubation with specified antibodies ., Secondary antibodies were incubated for 30 min at rt when necessary ., Then , sections were mounted using fluorsave reagent ( Calbiochem , La Jolla , CA ) containing 0 . 3 µM 4′ , 6′-diamidino-2-phenylindole dilactate-DAPI ( Molecular Probes , Eugene , OR ) ., Negative controls using secondary antibodies alone were included in each experiment and found to be negative for staining ., Fluorescence was visualized in a Leica microscope ( Leica Microsystems , Wetzlar Germany ) ., Images were acquired and processed using IP lab software ( Scanalytics , Inc . , Rockville , MD , USA ) and Adobe Photoshop CS2 ( Adobe , Mountain View , CA ) ., The purified primary antibodies goat anti mouse CCL5 ( catalog number AF478 ) and CCL9 ( catalog number AF463 ) were bought from R&D systems and biotinylated CD31 antibody ( catalog number 553371 ) from Pharmingen ( San Diego , CA ) ., Rabbit anti Goat labeled with Rhodamine Red- X and donkey anti rabbit rhodamine red X secondary antibodies were purchased from Jackson ImmunoResearch ( West Grove , PA ) 13 ., M . corti parasites were collected aseptically from 4–6 months ip infected mice and washed rigorously with HBSS and then incubated with half the volume of HBSS+ gentamycin at 37°C , 4% CO2 for 72 hrs in a 25 CM2 culture flask ., After incubation , parasites were removed by filtering with a nylon mesh and the supernatant ( MCS ) was collected and kept at −80°C for future use ., bEND . 3 cells were purchased from ATCC and subcultured using DMEM+10%FBS ., Cell were seeded in chamber slides and stimulated with parasite supernatant , parasite homogenate or PBS for control ., After , 72 hrs of stimulation , IF staining was performed ., Briefly , cells were washed with PBS and incubated with 70% ETOH for 10 minutes followed by 3 PBS washes for 3 min each ., Subsequently , cells were blocked with 10% serum from the host of secondary antibody , followed by 40 min incubation with primary antibodies and 30 min with secondary antibodies as described in previous ( IF section ) section ., Chamber slides were mounted using fluorsave reagent ( Calbiochem , La Jolla , CA ) containing DAPI ., Images were acquired and processed as described in the previous section ., We administered Rh-RCA lectin ( Rhodamine conjugated Ricinus communis agglutinin ) systemically at 3 wk post infection ( p . i . ) and mock-infected mice to label the pial vessels as described in Materials and Methods ., The 3 wk p . i . time point was used because this is consistently the peak of leukocyte infiltration ., Brain sections from in vivo labeled , perfused brain tissues were prepared and analyzed for labeling of the blood vessels after dehydration ., We found that 5 µg/mg of body weight was sufficient to label the blood vessels ( Fig . 1 ) ., LCM was performed as described previously 16 ., RH-RCA labeled Pial vessels , distinctly located in subarachnoid spaces along with leptomeninges were collected by LCM ., Subsequently , total RNA was isolated from LCM enabled samples , and linear amplification was done in order to perform microarray experiments as described in Material and Methods ., Microarray hybridization experiments were performed to assess differentially expressed genes during infection using operon spotted chip arrays , and the data were processed by Genespring 11 to quantify differentially expressed probe sets ( see Materials and Methods ) ., Quality control on samples was done by principle component analysis which showed separation between mock and infected samples based on their gene expression profile while clustering the infected samples and mock samples together respectively ( data not shown ) ., In total , 2154 probe sets passed the screen when the probe sets were filtered for intensity with a lower cut off 44 ., Out of these , 768 probe sets met a corrected p-value ( Benjamini-Hochberg cut off of 0 . 05 ., Of the 768 probe sets , 578 probe sets were found to be differentially expressed with a fold change of ≥2 ., Differentially expressed probe sets with a fold change of ≥2 were subjected to hierarchical cluster analysis using Average Linkage with Pearson Correlation as the similarity measurement of gene expression ( Fig . 2 ) ., Operon chips contain oligo probe sets representing transcripts belonging to annotated genes as well as Expression Sequence Tags ( EST ) which represent yet to be defined genes ., All the 578 differentially expressed probes were uploaded to Ingenuity Pathway Analysis ( IPA ) software to find out known genes associated with differentially expressed probe sets ., IPA is a web-based application that uses a knowledge base created by previous findings of molecular interactions in the context of biological events ., Once a gene is uploaded into IPA during core analysis , it maps the gene and places them in relevant molecular networks , biofunctions and specific pathways ( https://analysis . ingenuity . com/ ) ., Out of 578 probe sets , 380 ( 285 upregulated and 95 down regulated ) were found annotated or mapped by IPA ( Table S1 ) ., In order to understand the biological significance of the differentially expressed genes , biofunctions and networks of genes involved in biofunctions were analyzed using IPA ., Under biofunction analysis genes were categorized into three different classes of biofunctions such as disease and disorder , molecular and cellular function , and physiological system development and function ( Table 1 ) ., The disease and disorder category included Immunological disease , infectious disease , inflammatory response , connective tissue disorders and inflammatory disease ( p\u200a=\u200a8 . 17E-24 to 2 . 83E-05 ) ., The category of molecular and cellular functions included Cellular function and maintenance , cellular movement , cell death , cellular development and cellular growth and proliferation ( p\u200a=\u200a1 . 00E-33 to 3 . 92E-05 ) ., Genes in the category of physiological system development and function were associated with Hematological system development and function , tissue morphology , immune cell trafficking , tissue development and humoral immune response ( p\u200a=\u200a9 . 38E-32 to3 . 87E-05 ) ( Table 1 ) ., Many of the genes were classified in more than one biofunction category due to the broad and overlapping nature of the categories as well as an individual gene influencing multiple biofunctions ., We analyzed the differentially expressed genes using IPA to assess how genes interact with each other as part of biological pathways ., The resulting networks are generated based on the random selection of focus genes with maximum connectivity and several interconnected focus genes put together as a network in order of high to low scores ., Scores are derived from p-values and are calculated through Fishers exact test which represents the probability of finding the focus genes of a network in a set of n genes randomly selected from a global molecular network of genes ., Based on focus genes differentially expressed during infection , 23 networks were identified ., 22 networks that yielded a score of more than 3 are shown in Table S2 ., Network analysis indicated that genes involved in the metabolism of lipids , carbohydrates and amino acids are affected ., Further , immune response related genes were identified in multiple networks along with genes involved in cell growth , death and connective tissue disorder ( Table S2 ) ., Pictorial representation of three of the networks is shown in Fig . 3 ., Fig . 3 B , C and D show the networks “inflammatory response , cell-to-cell signaling and interaction , cellular movement” “cellular movement , hematological system development and function , immune cell trafficking” and “antimicrobial response , cell-to-cell signaling and interaction , embryonic development” respectively involving immune response related genes ., A number of genes were chosen from different functional categories to be verified for their gene expression pattern by Taqman real time polymerase chain reaction ( RT-PCR ) using the amplified cDNA derived from pial endothelial cells isolated by LCM ., Results obtained from RT-PCR experiments confirmed the expression pattern of a number of genes ., Data showed that SELP , CD274 , LGALS3 , MRC1 , FIZZ1 , β2M , C3 , CCL2 , CCL5 and STAT1 were significantly upregulated ( Table 2 ) similar to microarray ., To assess protein expression , brain sections from mock-infected and infected mice ( 3 wk p . i . ) were analyzed by IF microscopy for chemokines including CCL5 ( Fig . 4A ) and CCL9 ( Fig . 4B ) ., In sections from mock-infected mice , CCL5 was undetectable ., Infection resulted in a substantial up-regulation of CCL5 which co-localized with CD31 , an endothelial cell marker ., Similarly , CCL9 was scarcely detected in the blood vessels from mock-infected samples ., CCL9 was highly up-regulated as a result of infection and appears to be secreted ., In addition , it co-localizes with undefined strand-like structures that appear to form a gradient starting from the outer surface of pial vessels ( abluminal ) towards the direction of infiltrating cells ., The degree of CCL9 expression was higher in inflamed vessels exhibiting leukocyte egress ., Since chemokines can be secreted and deposited on extracellular matrix , it was important to confirm that endothelial cells can produce these chemokines ., To test this , bEND . 3 ( brain endothelial cell line ) cells were stimulated with either M . corti secretory/released antigens ( MCS ) or whole parasite homogenate in HBSS ( WP ) and analyzed for the production of CCL5 and CCL9 ., We found that both parasite preparations induced an increased expression of CCL5 and CCL9 by bEND . 3 cells compared with controls in the absence of antigen ( Fig . 5 ) ., The BBB acts as an interface between the periphery and the CNS and tightly regulates the components of the immune response to prevent unnecessary inflammation/pathology in the healthy brain ., It is known that the nature of the vasculature and associated functions differ greatly depending upon their location in different CNS compartments 4 ., In a number of CNS infections , pial vessels of the BBB are particularly prone to disruption with leakage of leukocytes and serum components leading to meningitis 19 ., This increased vulnerability is possibly due to lack of additional barrier components and potential exposure to antigens compared with parenchymal vessels 20 , 21 , 22 ., Previously , gene expression analysis of endothelial cells has been performed either in an in vitro setting or with whole brain endothelial cells 23 , 24 , 25 , 26 , 27 , but not with endothelial cells present in specific anatomical compartments ., Moreover , the effect of parasitic infection on endothelial cell biology has not been studied ., The focus of this study was to characterize the infection-induced molecular signature of LCM isolated pial endothelial cells by evaluating global gene expression by microarray analyses ., LCM allowed us to isolate cells present in a specific location which has an added advantage over other marker-based techniques such as FACS ., However , one pitfall is that the potential contamination of the BBB endothelium with the leukocyte that may be extravasating or adhering to the endothelial cells ., Our data analysis confirmed that differential gene expression data obtained through microarray hybridization experiment is mainly contributed by endothelial cells comprising the BBB as common lymphoid or myeloid cell markers were not detectable in the data set ., In addition , the expression of the following BBB specific transporter markers were induced during infection: TFRC ( related to iron metabolism ) , ABCG1 ( cholesterol homeostasis ) , SLC15A3 ( proton oligopeptide co-transporters ) , SLC7A5 ( cationic amino acid transporters and the glycoprotein-associated amino acid transporters ) , ABCC3 ( multidrug resistance associated protein 3 ) and ABCC5 ( multidrug resistance associated protein 5 ) ., Other BBB specific markers were downregulated including SLC9A3R2 ( sodium/hydrogen exchanger ) , SLC6A9 ( neurotransmitter transporter , glycine , sodium and chloride dependent neurotransmitter ) 23 , 24 , 25 , 26 , 27 ., Network analysis shows that apart from transporters several other sets of immune related genes including MRC1 , complements ( C3 , C6 , and C1R and complement factor properdin ) , TNF super family members and interferon inducible genes including STAT1 are induced in NCC infection which can potentially lead to endothelial cell activation 23 , 24 , 28 ., Interferon inducible genes have been shown to be induced in an in vitro study with endothelial cells in HIV and Cryptococcus neoformans infection model 25 , 29 ., STAT1 has been shown to promote inflammatory mediators and leukocyte transmigration at the BBB 30 ., Interferon signaling mediated through the Jak Stat pathway is critical to induce several of these genes in endothelial cells including chemokines and MHC class I antigen presentation related genes 23 , 24 , 28 ., Among immune related genes chemokines play a critical role in leukocyte trafficking , differentiation and angiogenesis or angiostasis 31 , 32 ., Leukocyte trafficking is a multistep process in which chemokines induce the migration of leukocytes toward a chemokine gradient ., Interaction between chemokines expressed by endothelial cells with their receptors on leukocytes triggers a signaling process that increases the avidity of integrin to their receptors on endothelial cells causing firm adhesion of leukocytes and facilitated transmigration towards chemokine gradient 33 ., Chemokines are divided into C , CC , CXC , and CX3C subgroups based on conserved cysteine residues 31 ., The present study advances the understanding about chemokine expression profile in endothelial cells comprising the BBB which are the first CNS cells to encounter peripheral leukocytes in vivo ., Many of the chemokines upregulated ( Table S1 ) in response to infection are summarized in Table 3 along with their putative receptor and influence on specific leukocyte subsets ., Our in vivo and in vitro data shows that CCL9 is expressed abundantly by endothelial cells and appears to coat the strands in a gradient fashion ., Such strands have been observed in the areas of inflammation in other disease conditions such as EAE and toxoplasmic encephalitis 34 ., The origin and composition of these strands are still not clear ., They have been described to extend from blood vessels to parenchyma and are thought to provide structural support for leukocytes migration 34 ., In the case of NCC , these strands coated with CCL9 might also provide a physical scaffold structure with a chemotactic signal for migration of leukocytes into the CNS ., The functional correlation for CCL9 in terms of leukocyte subset recruitment remains to be defined in the CNS ., However , in the periphery CCL9 has been implicated in recruitment of myeloid cells to peyers patches and osteoclasts through the CCR1 receptor ., Furthermore , it is also critical to recruit immature myeloid cells through CCR1receptor during liver metastasis 35 ., In addition , CCL17 and CCL22 are also noteworthy as they have been implicated in trafficking of CCR4 positive regulatory and Th2 T cells subsets 33 ., Chemokine can selectively influence the trafficking of leukocyte subsets ., Therefore , the expression profile of chemokines in the BBB provides insight into the trafficking of different leukocyte subsets such as M1 and M2 macrophages , granulocytes , γδ T cells , αβ T cells and B cells known to infiltrate during NCC 13 , 14 , 36 , 37 , 38 ., In summary , our data delineate infection-induced changes in the expression of genes associated with both immunity and disease , and collectively provide insight into the dysfunction of the BBB and mechanisms associated with leukocyte infiltration during murine NCC .

Introduction, Materials and Methods, Results, Discussion

In murine neurocysticercosis ( NCC ) , caused by infection with the parasite Mesocestoides corti , the breakdown of the Blood Brain Barrier ( BBB ) and associated leukocyte infiltration into the CNS is dependent on the anatomical location and type of vascular bed ., Prior studies of NCC show that the BBB comprised of pial vessels are most affected in comparison to the BBB associated with the vasculature of other compartments , particularly parenchymal vessels ., Herein , we describe a comprehensive study to characterize infection-induced changes in the genome wide gene expression of pial vessels using laser capture microdissection microscopy ( LCM ) combined with microarray analyses ., Of the 380 genes that were found to be affected , 285 were upregulated and 95 were downregulated ., Ingenuity Pathway Analysis ( IPA ) software was then used to assess the biological significance of differentially expressed genes ., The most significantly affected networks of genes were “inflammatory response , cell-to-cell signaling and interaction , cellular movement” , “cellular movement , hematological system development and function , immune cell trafficking , and “antimicrobial response , cell-to-cell signaling and interaction embryonic development” ., RT-PCR analyses validated the pattern of gene expression obtained from microarray analysis ., In addition , chemokines CCL5 and CCL9 were confirmed at the protein level by immunofluorescence ( IF ) microscopy ., Our data show altered gene expression related to immune and physiological functions and collectively provide insight into changes in BBB disruption and associated leukocyte infiltration during murine NCC .

Neurocysticercosis ( NCC ) is one of the most common parasitic diseases of the CNS caused by the metacestode ( larva ) of the tapeworm Taenia solium ., Epidemiological studies show that among the various forms of NCC , subarachnoid NCC is associated with poor prognosis , more resistance to anti-helminthic drugs and more severe inflammation ., The chronic inflammation of the vasculature and arachnoid thickening ( chronic basal meningitis ) leads to blockade of CSF further contributing to CNS pathology ., Using a murine model for NCC , we have found that among the different types of vasculature associated with the blood-brain barrier ( BBB ) , pial vessels of BBB are compromised earlier and to a greater extent during NCC ., In addition , pial vessels are likely the most important entryway for leukocyte infiltration during NCC ., The aim of this study was to characterize infection-induced changes in the genome-wide gene expression of pial vessels ., Our approach was to isolate pial vessels of the BBB by in vivo labeling of vessels followed by laser capture microdissection microscopy ( LCM ) ., Further , microarray analysis of pial vessels showed infection-induced changes in the expression of genes associated with both immunity and disease , and collectively provides insight into the dysfunction of the BBB and mechanisms associated with leukocyte infiltration during murine NCC .

immunopathology, medicine, cerebrovascular diseases, emerging infectious diseases, neurological disorders, neurology, immunology, biology, microbiology, host-pathogen interaction

journal.pntd.0004300

A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases

Neglected tropical diseases ( NTDs ) devastate the lives of approximately 1 billion people , with a further 1 billion at risk 1–3 ., These diseases mainly affect those who live in poverty in Africa , Asia and the Americas ., Current treatments for these diseases present several issues and limitations such as cost , difficulties in administration , poor safety profiles , lack of efficacy , and increasing drug resistance , among others 4 ., Furthermore , there has been limited commercial interest in developing improved therapeutics , mostly because of the costly and risky nature of the drug discovery process 5 , 6 and the expected low return of investment when dealing with poor patient populations 7 ., As a consequence , only ~1% of all new drugs that reached the market in recent years were for neglected diseases 1 , 4 ., The situation for human diseases that affect the developed world is radically different ., In this case , many important contributions to drug discovery are made every year from academic and government laboratories , leading to the approval of ~20 new drugs per year on average 8 ., As part of this process of drug discovery , we accumulate information about many bioactive compounds ( their activities , targets and mechanisms of action ) , which can be used in repositioning strategies ., Drug repositioning ( or repurposing , or reprofiling ) is the process of finding new indications for existing drugs 9 ., The benefits of this approach are many , the main being the lower costs of development 5 , 9–11 ., A number of success stories help support the case for these type of approaches ., Two of the best known examples are sildenafil ( Viagra ) , which was repositioned from a common hypertension drug to a therapy for erectile dysfunction 11 and thalidomide , repurposed to treat multiple myeloma and leprosy complications 12 ., Because of the enormous cost savings associated with repositioning an approved drug , this strategy is particularly attractive for NTDs ., For these , there are also a number of successful repositioning stories: eflornithine , which was developed as an anticancer compound is being used to treat African trypanosomiasis ( sleeping sickness ) , whereas pentamidine , amphothericin B ( originally an antifungal drug ) and miltefosine were all repositioned from other indications for the chemotherapy of leishmaniasis ( other examples were discussed recently , see 13 , 14 ) ., Target prioritization , and drug repositioning are particularly amenable to the use of computational data mining techniques , which offer high-level integration of available knowledge 15 ., These strategies take advantage of bio- and chemoinformatic tools to make full use of known targets , drugs , and disease biomarkers or pathways , which in turn lead to a faster computer-to-bench or computer-to-clinic studies ., Exploring a large pharmacological space in this way has led to novel insights on the targets and modes of action of existing drugs 16–24 ., Unfortunately , these and other integrative mining strategies were focused in attacking the problem from the point of view of diseases of the developed world ., Fortunately it is relatively straightforward to use a number of inference strategies to map informative associations to other species ., Kruger and coworkers recently showed that ligand binding to > 150 human proteins is mostly conserved across mammalian orthologs , therefore providing support for this type of inferences 25 ., It is also worthwhile mentioning that particularly in the case of neglected diseases , drug repositioning need not be taken in a strict sense to include only drugs approved for clinical use in humans ., Widening the criteria to reposition drugs for veterinary use , or further , any bioactive compound ( hits/leads ) may significantly increase the chances of success by helping to guide efforts in academia and pharma ., These will ultimately feed the pipeline of drug discovery for these important diseases ., After completion of a number of key pathogen genome projects , we developed a database resource to help prioritize candidate targets for drug discovery in NTDs 26 , 27 ., Initially , target prioritizations were based on gene and protein features , with limited use of information on availability of bioactive compounds to guide these prioritizations ., Since then we have integrated information on a large number of bioactive compounds into the TDRtargets . org database 28 ., These were derived from public domain resources , and from a number of high-throughput screenings of an unusual scale for NTDs 29–31 ., This has brought the current status of chemogenomics data integration in NTDs to a stage where large scale data mining exercises are now feasible ., Complex networks can efficiently describe pairwise similarity relations between drugs and between proteins ., Under this paradigm non-trivial interconnectivity patterns can be mined to uncover hidden organization principles , or to identify unnoticed relevant entities and/or novel putative drug-target associations 18 , 23 , 32–40 ., In this work we addressed the construction of a multilayer network of protein targets ( gene products ) , chemical compounds , and their relations , in order to guide drug discovery efforts ., Because we focused on tropical diseases , we were interested in leveraging the information contained in the network ( mostly derived from well-studied organisms ) to direct the selection of targets and compounds for further experimentation in these neglected pathogens ., In this context we tackled two well differentiated problems ., First , we analyzed the prioritization of targets for drug discovery in the absence or scarcity of bioactivity data for an organism of interest ., For a selected pathogen ( a query species ) , we took advantage of chemogenomics and bioactivity data available in the network , to get a global prioritized list of promising targets ., In a second analysis , we used the information embedded in the network to suggest candidate targets for orphan compounds , i . e . chemicals that have been shown to be active in whole-cell or whole-organism screenings but whose targets are currently unknown ., In this case , we aimed to obtain reduced prioritization lists of target proteins for the query molecule ., All target data used in this work was obtained from the TDR Targets database 26 , 28 , which includes complete genomes from a number of pathogens causing neglected tropical diseases , as well as model organisms: Plasmodium falciparum , Trypanosoma brucei , Trypanosoma cruzi , Leishmania major , Mycobacterium tuberculosis , Brugia malayi , Schistosoma mansoni , Toxoplasma gondii , Plasmodium vivax , Leishmania braziliensis , Leishmania infantum , Leishmania mexicana ., In addition we integrated data from complete genomes from non-pathogen organisms: vertebrates ( human , mouse ) , plantae ( Arabidopsis thaliana , Oryza sativa ) , invertebrates ( Drosophila melanogaster ) , and nematodes ( Caenorhabditis elegans ) , fungi ( Saccharomyces cerevisiae ) , and bacteria ( Escherichia coli ) ., Pfam domain annotations for all targets were obtained from the InterPro database resource , using interproscan 41 ., Metabolic pathway , and EC number annotations for all targets were obtained from the KEGG database resource 42 ., Orthology relationships between targets were obtained from the OrthoMCL database 43 or calculated by mapping proteins against OrthoMCL ortholog groups using BLASTP 44 ., As a result we had our proteins mapped to 69 , 926 ortholog groups ( a singleton is considered also as a separate ortholog group of size = 1 ) ., Information on chemical compounds ( structures , bioactivity information ) was obtained from the ChEMBL database 45 ., This information was complemented by manually curated data from the TDR Targets database on compounds active against pathogens ( see below ) ., We estimated chemical similarity between molecules by performing an all vs all fingerprint-based similarity analysis using checkmol 46 ., The algorithm for fingerprint generation has been described 46 , but briefly , for each molecule the molecular graph is disassembled into all possible linear fragments with a length ranging from 3 to 8 atoms ., Strings representing atom types as well as bond types of these linear fragments are then passed to two independent hash functions in order to compute two pseudo-random numbers in the range 1–512 , which are used to set two positions in the 512-bit binary fingerprint ., For similarity search operations , the hash-based fingerprint of the query structure was used to compute the Tanimoto similarity coefficient ( Tc ) 47 for each pairwise combination of query/candidate hash-based fingerprints ., Because pairs of molecules with low Tc values have insubstantial chemical similarity , for the Drug-network layer we only considered similarity relationships with Tc values ≥0 . 8 as these are expected to be both significant in statistical terms 48 and in terms of their expected biological activity 49 ., As a result we retained about 44 . 4 106 informative pairwise relations and used the corresponding Tc values to weight the corresponding links ., In addition , for each bioactive molecule d ∈ VD , we identified exact substructure relationships using matchmol ., These substructure relationships , unlike other similarity measurements , were asymmetrical ( a 2D/graph representation of a molecule was completely included within another one , but not viceversa ) ., We filtered out substructure relationships for very small molecules as these were more likely to be contained within larger and more complex molecules rather unspecifically without a strict correlation with expected targets or modes of action ., After analyzing the distribution of molecular weight and number of parental structures of each compound ( parental molecules are those that contain a compound as part of its structure ) we filtered out edges involving molecules with low molecular weight ( MW < 150 ) and large number of parental structures ( Nparents>100 ) ., We found that the adopted molecular weight threshold appeared as a reasonable and conservative maximal bound for filtering out highly promiscuous structures ( i . e . molecules included in more than 100 parental compounds ) ., For larger molecular weights the number of affected molecules would have been much more sensitive to the adopted threshold level ( see S3 Fig ) ., Taking into account Tanimoto similarities and substructure relationships , we set up the drug layer graph GD ( VD = {d1 , … , dM} , E = {cij}i , j = 1…M ) ., We considered weighted inter-compounds edges cij ∈ R ( 0+ ) defined as:, cij=max{TC ( di , dj ) *I ( TC ( di , dj ) ≥0 . 80 ) , 0 . 8*I ( di⊂dj ) }, ( 1 ), where I ( x ) is an index function that equals 1 if its argument is a true proposition and 0 otherwise , and di ⊂ dj means that di is an exact substructure of dj ., In words , each substructure edge received a weight value of 0 . 8 , and each valid Tanimoto edge ( Tc ≥ 0 . 8 ) was weighted considering the corresponding Tc value ., The overall chemical similarity information between a pair of compounds was then integrated into a single link taking into account the maximal available weight that could be established between them ., Links between compounds and proteins were derived from bioactivity information , obtained from different sources ( ChEMBL , PubChem , TDR Targets ) , as well as a focused manual curation of the literature performed for this work ., Due to the great diversity of assays and forms of reporting bioactivity values , we selected a number of assays for which we have the greatest amount of data , and we defined a cutoff value for each bioactivity type , in order to classify the compound as active or inactive ( Table 1 ) ., The bioactivity classes that were taken into account represent 95% of the total bioactivities in our dataset ., In the case of orphan compounds that are active against P . falciparum ( see Results ) bioactive molecules correspond to the assays detailed in the Table 2 ., For the i-th affiliation-type node , fi ∈ VF ( which represents a shared functional relation among proteins , such as an ortholog group , a Pfam domain , or a defined biochemical pathway , we defined a Relevance Score , RSi , as a proxy of its informative relevance with regard to drug-target predictions tasks ., To this end , we performed an overrepresentation test ( Fisher exact test ) to quantify the overrepresentation in each affiliation category of druggable proteins , where the criteria for druggability are the cutoffs described in Table 1 ., Taking into account the corresponding Fisher test p-value , pvi , we defined the attribute node’s relevance score as, RSi=−log10 ( pvi ), ( 2 ) The protein and affiliation node layers defined a bipartite graph which can be represented by an adjacency matrix Mbip∈Rnp×nf:, Mijbip={1if proteiniis annotated to categoryfj0otherwise, ( 3 ), We projected this bipartite network into a mono-partite graph , the Projected Protein Layer ( PP-layer ) , where protein nodes were connected through weighted links if they share common affiliation nodes ., The corresponding adjacency matrix MPP∈Rnp×np was defined as, MPP=MbipS ( Mbip ) T, ( 4 ), where S∈Rnf×nf was a diagonal scoring matrix for affiliation nodes ., We considered two alternative definitions for the scoring matrix S . In the first case , S = Sr , diagonal elements were defined as, Srii=f ( RSi ) ={1ifRSi≥quantile ( RS , 0 . 8 ) ( RSimax{RSi} ) αotherwise, ( 5 ), where α was a tunable parameter that was set by maximizing the performance of recovering known druggable targets in cross validation exercises ( see below ) For the second alternative , in view of the broad degree distribution observed for affiliation nodes , we also considered an extra factor that relativized the score of large categories ., In this case diagonal elements of S = Sr were defined as, Srkii=f ( RSi ) ={1kiifRSi≥quantile ( RS , 0 . 8 ) 1ki ( RSimax{RSi} ) αotherwise, ( 6 ), where ki is the degree of the i-th affiliation node , and α was a tunable parameter ( see below ) ., Both scoring matrices , Sr and Srk , led to different projected PP-layers and induced two alternative two-layered weighted graphs G ( V = {VD , VP} , E = {EDD , EDP , EPP} ) , namely Gr and Grk ., These graphs were used to address different prioritization tasks throughout this manuscript ., In either case the free parameter α was set by maximizing the performance of recovering druggable targets ., Let’s consider a weighted graph G = G ( V = {ni}i = 1…N` , E = {eij}i , j = 1…N ) , where eij∈R0+ are weighted edges , and a vertex seed set S = {s1 , … , sk} ., The voting scheme assigns to each node ni not included in the seed set a prioritization score , PS , according to the following expression:, PSi=∑j=1…kwjeji, ( 7 ), where wj is a real number that serves to weight the contribution of seed sj , and eji the weight value of the link joining nodes nj and ni ., When we prioritized targets from a query proteome Q , we set wj = 1∀j ( i . e . we considered uniform and equally weighted seeds ) ., On the other hand , when we prioritized candidate targets for an orphan compound dk , we set wj according to the similarity between dk , and its direct neighbor drugs which reported bioactivities against protein sj:, wj=∑i:di∈N ( dk ) ckieijDP, ( 8 ), where cki is the weight of the edge between dk and di molecules introduced in Eq 1 , eijDP is 1 if there was a bioactivity link between drug di and protein pj ( and 0 otherwise ) and N ( dk ) the set of direct neighbors of drug dk ., The PP-layer results from a projection of a bipartite network graph ., The procedure used for this projection is dependent on the single parameter α ( see Eqs 2 and 3 ) ., In order to analyze the effect of α on the ability to recover known targets from an entire genome , we calculated ROC curves , and compared the partial AUC-0 . 1 for different α values following a tenfold cross validation procedure ., The results are summarized in S4 Fig It can be noticed that the predictive performance remained near maximal , without significant variations , for a broad range of the parameter space , α ∈ 0 . 2 , 1 , suggesting that the method is robust to different α selections ., From this point forward , we considered α = 0 . 6 , the midpoint in this interval ., An important remark is that α = 0 - which corresponds to disregarding the relevance score in the definition of the S matrix ( see Eqs 4 and 5 ) —had a significantly lower performance than the α = 0 . 6 case ( pv < 10−24 , Wilcoxon test ) ., We integrated genomic , biochemical and medicinal chemistry data from several public domain resources ( see Methods ) ., These data is available from the TDR Targets database and includes genome data from pathogen and model organisms ., As a starting point we considered sequence information from ~ 1 . 7 105 proteins derived from 37 complete genomes ( S1 Table ) and from known druggable targets from other 184 species ., We also considered a number of affiliation-type features for these proteins , which would allow us to establish relations between proteins , like sharing of protein domains , clustering in the same ortholog groups and participation in the same metabolic pathways ., These features were selected because they provide complementary information on the similarity of these proteins , from the point of view of drug discovery , and because they can be easily computed for whole genomes ., In addition , we considered structural information from ~1 . 5 106 bioactive compounds , and their associated bioactivity data against pathogen and non-pathogen organisms , obtained from open chemical databases and high throughput screenings 29–31 , 45 , 51 ., In order to organize and provide a global description of the available heterogeneous data , we considered a multipartite , multilayered network graph G ( V = {VD , VP , VF} , E = {EDD , EDP , EPF} ) ., In this network three types of vertices VD , VP , VF represented bioactive compounds , proteins , and functional affiliation entities , respectively ., Relationships between pairs of compounds , between compounds and known protein targets , and between proteins and functional affiliation classes where represented by the corresponding edges EDD , EDP , EPF ., Fig 1A depicts a graphical representation of this network , where three layers , each including a different type of vertex can be recognized ., The first layer contained chemical compounds as nodes ( VD = {d1 , d2 , …} ) ., Weighted pairwise links between compounds ( EDD ) were established if they were chemically similar based on their 2D representations ., More specifically , we connected two compounds if the Tanimoto similarity coefficient of their 2D fingerprints was >0 . 8 ( which is a very conservative similarity cutoff 48 ) , or if a compound was an exact substructure of the other ., In this case the directionality of the relationship was preserved ( see Methods for details ) ., Nodes in the second layer ( VP = {p1 , p2 , …} ) represented proteins from 221 pathogen and non-pathogen ( model ) organisms ., Complete proteome coverage in the network was available for 37 species representing a wide phylogenetic range ( S1 Table ) ., No connections were initially established between nodes in this layer ., Instead , we considered a third layer in which nodes ( VF = {f1 , f2 , …} ) represented functional affiliation-type entities as nodes ., These entities were Pfam domains 52 , ortholog groups 53 , 54 and metabolic pathways 42 ., We established links ( EPF edges ) between layer-2 nodes ( proteins ) and layer-3 nodes ( functional affiliation-type entities ) based on current predictions derived from standard sequence analysis pipelines and annotation ( see Methods ) ., Lastly , we have used bioactivity data information to establish links ( EDP edges ) between protein targets ( layer-2 ) and chemical compounds ( layer-1 ) ., These links were established after manual curation of the textual description of the assays , targets , and measured activities ., Because bioactivities integrated into the TDR Targets resource contained also negative evidence ( inactive compounds at relevant concentrations against a particular target ) , a significant amount of manual curation of these data was required for construction of the network ., Therefore , EDP edges in the final network graph represented sensible bioactivity information available for each protein target ( bioactivity thresholds and criteria are described in Methods ) ., A summary of the information and entities included in the network is available in Table 3 ., Once the data was integrated in our network model , we proceeded to identify informative functional affiliation-type annotations that were relevant for drug discovery ., Therefore , in the next step , we discarded 52 , 916 VF nodes that were not linked to at least one druggable protein in our dataset ( in this context “druggable” was defined operationally as a protein with at least one link to a compound in layer-1 ) ., The final resultant network comprises 2 , 252 informative affiliations to Pfam domains , 2 , 789 affiliations to ortholog groups , and 145 affiliations to metabolic pathways ., The second and third layers of the network defined , on their own , an affiliation or membership network , which is a special type of bipartite network 55 , 56 ., An important feature of this kind of networks is that the inter-layer connectivity pattern can be used to infer intra-layer associations for each layer , via projection procedures 56 ., In our case , adjacent links of shared functional affiliation nodes , VF , were used to define weighted links , EPP , between protein nodes , VP ., These inferred edges condensed similarity information at the level of the biological and functional concepts contained in layer-3 ., We have implemented two projection methodologies ., In the first case we took into account a relevance score , RS , for each affiliation node based on the statistical significance level of the over-representation of associated druggable proteins as obtained through a Fisher’s exact test ( see Methods , an example is provided in Table 4 ) ., For the second alternative , in view of the broad degree distribution observed for affiliation nodes ( see S1 Fig ) , we also considered an extra factor that relativized the score of large categories ( see Methods for technical details ) ., The rationale of this correction is to down-weight the contribution of very promiscuous annotation nodes ( e . g . highly frequent protein domains such as the ATP-binding cassette , present in many functionally-unrelated protein families and orthologs ) ., Although their presence helps to increase the connectivity of the protein network , it also skews the protein prioritization scoring and , as a general rule , favors specific kind of proteins towards the first places in the resulting rankings ( see below ) ., Taking into account either projection methodology , layer-2 and layer-3 could be collapsed into a single protein-projected directed and weighted layer ( PP-layer , see Fig 1B ) ., The PP-layer along with the original drug-layer ( D-layer ) , defined a new graph1 ) 1 ) G ( V = {VD , VP} , E = {EDD , EDP , EPP} ) that allowed us to propagate drug-target information to address different drug-discovery problems as described below in the next sections ., When necessary , we will note the resulting graphs as Gr ( projection using affiliation node’s relevance scores ) or Grk ( projection using relevance scores and penalizing high degree affiliation nodes ) when the first and second projection methodologies were used , respectively ., In this section we considered the problem of prioritizing targets from a query proteome Q for which compound bioactivity data is scarce or lacking altogether , as this is frequently the case for pathogens causing neglected tropical diseases ., In this strategy we aimed to take advantage of the information contained in the network for other organisms to guide the prioritization of targets in our query species ., The rationale of the approach relies on the assumption that relevant drug-target associations from other organisms , in concert with similarity relations between proteins ( embedded in the G’ network as EDP and EPP edges respectively ) could be used to propagate meaningful associations through the network and therefore suggest novel drug connections for proteins in Q . To prioritize targets , we devised the following algorithm ., First we identified the set of druggable targets in the PP-layer of network G’ ., These were protein nodes that were connected to at least one compound via an EDP edge ( e . g . protein cal . 575054in Fig 1A ) ., In the next step , these nodes were used as seeds for a neighbor voting scheme algorithm ( VS ) implemented over the PP-layer ., As a result of this voting procedure , proteins in Q will receive a score which essentially is the weighted sum of all the EPP direct links to seed nodes ( i . e . known targets ) ., See Methods for further details ., In order to illustrate the performance of this strategy we considered two query species Q each of which have known druggable targets: a mammalian proteome ( Q = M . musculus , often used as a model for human drug development ) , and a proteome from a protozoan parasite ( Q = T . cruzi , Chagas Disease ) ., We deliberately chose a data-rich and a data-poor organism for this exercise to showcase the performance of the approach under these two contrasting situations ., Whereas 8 , 429 EDP edges involving 280 VP nodes were present for M . musculus , only 319 EDP edges were adjacent to 19 T . cruzi protein nodes ., The validation proceeds in each case by removing from the graph G , all EDP bioactivity edges involving proteins of Q before projecting layer-3 into layer-2 and weighting EPP edges ., In this way , we ensured that no information extracted from the query organism was employed to build the two-layer G’ network used to prioritize targets in Q . After weighting and projecting the modified network graph , we assessed the performance of the prioritization strategy using Receiver Operating Characteristic ( ROC ) curves ., Fig 2 depicts ROC curves for predicted drug-target associations considering G’rk ( black ) and G’r ( orange ) for M . musculus ( solid line ) and T . cruzi ( dashed line ) ., Table 5 summarizes the performance of the prioritization procedures reporting the normalized AUC-0 . 1 values ( see inset in Fig 2 ) ., The performance of a third prioritization strategy was also reported in the table for the sake of comparison ., In this case , we considered a straightforward approach based on calculation of plain sequence similarity between druggable nodes in layer-2 against proteins in Q . For this purpose we used the FASTA sequence-alignment tool 57 , which produces longer alignments than BLAST ( as it does not split the region of similarity into high-scoring-pairs as BLAST does ) ., The high performance of our network model at the task of recovering the known targets in each organism reflects the fact that data from close relatives of both organisms are contributing substantially to the connectivity of these nodes in the network graph ., As an example there are 60 , 540 EDP edges connecting 455 VP nodes in the case of rat data , whereas there are 43 , 325 EDP edges connecting 3 , 567 VP nodes for other protozoan and bacterial targets ., For both organisms , prioritizations based on the G’rk network model presented the best performance ., Down-weighting the relevance score of affiliation nodes by their degree provided a significant improvement , as prioritizations considering G’r resulted in much poorer performances , especially for the T . cruzi case ., Noticeably , the origin of the performance discrepancies between both network-based approaches were related to a strong correlation between prioritization scores in the G’r network and the strength ( a connectivity topological feature ) of Vp nodes ., This finding makes evident that G’r prioritizations were a priori biased towards specific protein classes , i . e . those associated to high-strength Vp nodes ( see Supplementary S1 Text ) ., It is worth mentioning that despite its simplicity , the voting scheme ( VS ) adopted for these network-based prioritization strategies has already proved to be competitive relative to more sophisticated algorithms in many scenarios , with the additional benefit of being extremely fast 59 ., We verified that this was also the case in the context of our prioritization problem ., In particular , we considered a prioritization strategy based on a network flow analogy ( functional flow methodology ) 60 and verified that it gave similar or inferior performance than VS ( see S2 Table ) ., Finally , we compared the top ranked targets according to the network-based VS voting algorithm and the FASTA methodologies to see if the information provided by these alternative prioritization procedures were correlated ., We considered the top 1% proteins ranked by the analyzed methodologies in each species ( top 136 and 66 targets for M . musculus , and T . cruzi , respectively ) ( see S2 Fig ) ., Even though we found statistically significant overlaps between G’rk and FASTA predictions ( Fisher Exact Test , p = 9 . 45 10−28 and p = 2 . 79 10−2 for M . musculus , and T . cruzi , respectively ) most of these were specific to the considered prioritization strategy ., This finding revealed that even if the two kinds of affiliation-type entities with the largest network coverage ( i . e . orthology groups and Pfam domains ) involved some sort of sequence similarity idea , the network based predictions were non-trivial from this point of view ., Overall , these results also suggested that by considering different types of information in the network , we might gain alternative and complementary insights about potential targets for a query species ., The most relevant and promising application of this kind of approach , is to prioritize new putative targets as interesting cases of study ., To this end , we performed the procedure described above , hence taking advantage of the information contained in the network for known druggable targets across all species and analyzed the top ranked proteins for three kinetoplastids: Trypanosoma cruzi ( TCR ) , Trypanosoma brucei ( TBR ) and Leishmania major ( LMA ) ( the TriTryps 61 ) ., The top 10 proteins resulting from this prioritization exercise are shown in the S3 Table ., A detailed analysis of the candidate targets prioritized is not within the scope of this work ., However , it is worth mentioning the finding of a number of interesting targets that have been already characterized in these parasites ., As shown in S3 Table , the majority of the proteins obtained at the top of the ranking using this kind of prioritization method were mostly protein kinases , one of the largest known protein superfamilies 62 ., Apart from also being a rich source of highly druggable targets , from the point of view of the network this is a protein class with strong ties ( abundant or heavy edges ) between family members ( both because of orthology and shared Pfam domains ) , and with abundant bioactivity links ( EDP edges ) due to the recognized target promiscuity of kinase inhibitors 63 ., The first protein in the ranking obtained for Trypanosoma cruzi was demonstrated to interact with and phosphorylate several parasite proteins 64 , including some of the trans-sialidase family 65 ., Transfection with a construct containing PKI ( inhibitor of PKA ) kills epimastigotes ( genetic experiment ) , whereas treatment with the isoquinolinesulfonamide compound H89 , a PKA inhibitor , killed 98% of the parasites within 48 hs ( pharmacologic experiment ) 64 ., The 5th and 6th proteins obtained in the L . major and T . cruzi lists respectively is a casein kinase I isoform 2 ., This protein has been proven to be a target for 4 inhibitors in L . major 66 ., These compounds also inhibited the growth of cul

Introduction, Methods, Results, Discussion

Drug development for neglected diseases has been historically hampered due to lack of market incentives ., The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases ., In this work we took advantage of data from extensively studied organisms like human , mouse , E . coli and yeast , among others , to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes , and bioactive drug-like molecules ., We modeled genomic ( proteins ) and chemical ( bioactive compounds ) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species , chemical similarities between 1 . 7 105 compounds and several functional relations among 1 . 67 105 proteins ., These relations comprised orthology , sharing of protein domains , and shared participation in defined biochemical pathways ., We showcase the application of this network graph to the problem of prioritization of new candidate targets , based on the information available in the graph for known compound-target associations ., We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches ., Moreover , our model provides additional flexibility as two different network definitions could be considered , finding in both cases qualitatively different but sensible candidate targets ., We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens ., In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound ., Moreover , we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature .

Neglected tropical diseases are human infectious diseases that are often associated with poverty ., Historically , lack of interest from the pharmaceutical industry resulted in the lack of good drugs to combat the majority of the pathogens that cause these diseases ., Recently , the availability of open chemical information has increased with the advent of public domain chemical resources and the release of data from high throughput screening assays ., Our aim in this work was to make use of data from extensively studied organisms like human , mouse , E . coli and yeast , among others , to prioritize and identify candidate drug targets in neglected pathogen proteomes , and drug-like bioactive molecules to foster drug development against neglected diseases ., Our approach to the problem relied on applying bioinformatics and computational biology strategies to model large datasets spanning complete proteomes and extensive chemical information from publicly available sources ., As a result , we were able to prioritize drug targets and identify potential targets for orphan bioactive drugs .

journal.pcbi.1000155

The Effect of a ΔK280 Mutation on the Unfolded State of a Microtubule-Binding Repeat in Tau

Alzheimers disease ( AD ) pathology is characterized by extracellular aggregates of Aβ-amyloid ( Aβ ) and intraneuronal tau aggregates , known as senile plaques and neurofibrillary tangles ( NFTs ) , respectively 1 ., Despite much focus on Aβ amyloid in AD research , tau seems to play an important role as well ., For example , the number of NFTs and not the number of senile plaques in the neocortex correlates with the severity of dementia in AD patients , and there are data that imply that abnormalities in tau alone may cause neurodegeneration 2 ., In light of these observations , a detailed characterization of the structure of tau protein may provide insights into the pathogenesis of AD and other neurodegenerative disorders associated with tau pathology ., However , probing the structure of tau is difficult because tau protein is natively unfolded ( or intrinsically disordered ) in solution ., Several studies suggest that tau retains its function after heat or acid-induced denaturation and both CD and X-ray scattering experiments imply that tau does not adopt a well-defined folded structure in solution 3–5 ., Consequently , obtaining structural and hence functional information on tau is problematic because the direct observation of unfolded states is typically difficult to achieve experimentally ., Initially , unfolded proteins were described as random coils whose properties are derived from Florys statistical description of chain molecules 6 ., For such polymers , the radius of gyration , RG , follows the scaling law RG\u200a=\u200aR0Nν , where R0 is the radius of gyration of a monomeric subunit ( a function of the persistence length ) , N is the number of subunits in the polymer , and ν is a scaling factor that depends on the solvent characteristics ., The most common measure of whether a protein behaves as a random coil is to test whether its radius of gyration follows this scaling law ., However , while a structurally disordered molecule can exhibit random coil statistics , the converse is not necessarily true; i . e . , random coil statistics do not imply that the structure is completely disordered 7 ., Slight structural preferences may exist for some natively unfolded proteins and small changes in the distribution of conformers within an unfolded ensemble may play a role in the normal and pathological functioning of intrinsically disordered systems ., A recent study , for example , suggests that inducer-mediated tau polymerization involves an allosterically regulated conformational change 8 ., This is consistent with the notion that the formation of tau fibrils is associated with a shift in the conformational distribution of tau such that the unfolded state has a preference for proaggregatory conformations in the presence of an inducer ., In light of this , constructing detailed ensembles that model the unfolded ensemble of tau may facilitate the identification of structural properties that promote aggregation ., As full-length tau contains more than 400 amino acids ( 441 residues for the htau40 isoform 9 ) constructing detailed ensembles that model the unfolded state of this protein is a daunting task ., Fortunately , tau contains three or four imperfect microtubule-binding repeats ( MTBRs ) near the C-terminus of the protein , and almost all known mutations of tau that are associated with inherited forms of neurodegenerative diseases are located in MTBR domains or their nearby flanking regions 10 ., As these data suggest that MTBRs play an important role in the progression of inherited tauopathies , we first focus on building ensembles that model the structure of individual MTBRs ., It is important to note , however , that we do not strive to model the structure of a given MTBR fragment alone in solution ., Rather , our goal is to generate ensembles that model the range of conformations that a MTBR can adopt when it is part of full length tau ., In the present study we focus on building ensembles for the second MTBR , henceforth referred to as MTBR2 ., This repeat is of particular interest because it contains both a six amino-acid repeat , PHF6* , which is a minimum interaction motif that can initiate tau aggregation in vitro 11 , 12 , and the site of the proaggregatory mutation , ΔK280 , which is associated with some forms of frontotemporal dementia 13–16 ., We have developed a method , called energy-minima mapping and weighting ( EMW ) , to construct ensembles that model the unfolded state of proteins ., The underlying assumption that forms the basis of this approach is that the unfolded state can be modeled as a set of energetically favorable conformers , where each conformer corresponds to a local energy minimum ., The method involves constructing a library of energetically favorable conformations and selecting conformations from this library to form ensembles that are consistent with a given set of experimental data ., We use EMW to build ensembles for wild-type ( WT ) MTBR2 and the corresponding ΔK280 mutant ., By comparing data from the two sets of ensembles , we deduce structural preferences in the ΔK280 ensemble that explain its increased propensity to form tau aggregates ., The EMW method begins by constructing sets of energetically favorable conformations for a sequence of amino-acids within a natively unfolded protein ( Figure 1 ) ., In the case of tau we focus on MTBR2 since this region contains the aggregation-initiating sequence PHF6* as well as the site of a mutation that is associated with increased tau aggregation in vitro 17 ., A set of local energy minima is then constructed for this subsequence , hence forming the candidate ensemble ( Figure 1 ) ., Associated with each structure in this ensemble is a weight , ωi , which corresponds to the probability that the given subsequence adopts the ith conformation in the candidate ensemble ., We say that an ensemble is fully specified when the local energy minima and weights are known ., Initial weights for structures in the candidate ensemble are calculated from the relative energies of each structure , as shown in Figure 1 ., However , as sampling is performed on a relatively small subsequence these weights may not reflect the relative probabilities of different conformations when the subsequence is part of the larger protein ., For example , compact states may be preferred over extended states when the subsequence is in isolation but not when part of tau ., Therefore , the composition of the ensemble is optimized and the members of the candidate ensemble are reweighted in light of experimental data that is obtained on a larger segment of tau protein ., Sampling small subsequences increases the chance that we will observe a relatively large number of accessible states for this system ., Using experimental data obtained on a larger region of tau ( and not just the subsequence of interest ) helps to ensure that the calculated ensemble represents the local structure of the sequence as it appears within full length tau ., A central component of EMW is that we do not strive to construct a single model for the unfolded state ., We recognize that the construction of unfolded ensembles that agree with any given set of experimental data is largely an underdetermined problem; hence it is likely that there are a number of different ensembles that are consistent with a given set of experimental data ., Consequently , we constructed several ensembles that are all consistent with the experimental measurements and focused our analysis on local structural motifs that are present in all ensembles ., For this study , we focused on NMR data that are available for both WT MTBR2 and a ΔK280 mutant ., These data were kindly provided by Marco Mukrasch , Daniela Fischer , and Markus Zweckstetter 17 , 18 ., Using the EMW method , 100 ensembles were constructed for both wild-type ( WT ) and ΔK280 sequences of MTBR2 ( a total of 200 ensembles ) ., Each ensemble was constructed to minimize the difference between calculated 13Cα chemical shifts and the corresponding experimentally determined 13Cα chemical shifts ., The number of structures in each ensemble corresponds to the minimal number of structures needed to fit the available chemical shifts ., Preliminary calculations found that 15 conformers were needed; i . e . , fewer structures resulted in worse fits to the 13Cα chemical shifts and more structures did not significantly improve the quality of fits ., We note that other models examining residual structure in the unfolded state have utilized a similar number of representative conformers 19 ., Application of EMW yielded ensembles that were in excellent agreement with experimentally determined absolute 13Cα chemical shifts ( Figure 2A and 2B ) ., The average RMS error between the calculated 13Cα chemical shifts and the corresponding experimental values was 0 . 1 ppm—well below the error associated with SHIFTX chemical shift predictions and similar to the error associated with experimental chemical shift measurements on K18 constructs 17 , 20 ., However , given that measured absolute chemical shifts for the 20 amino acids vary significantly according to the amino-acid type , reasonable correlations to absolute chemical shifts may be achieved by simply predicting amino-acid specific random coil values ., Given this , we analyzed the relationship between the chemical shifts , after subtracting out residue-specific random coil chemical shift values; i . e . , the secondary chemical shifts ., Overall , there is excellent agreement between calculated secondary chemical shifts and the corresponding experimental values for each residue in the sequence ( Figure 2C and 2D ) ., These data demonstrate that the calculated models yield agreement with experiment on a per residue basis ., In the next step of our protocol , carbonyl carbon ( 13CO ) chemical shifts were used to test whether the resulting ensembles can predict experimental observations that were not used to construct the model ., This helps to ensure that our models are not “overly fit” to the 13Cα chemical shifts ., In general , a model that is over-fit to a given set of experimental data can reproduce that data remarkably well but cannot reproduce data that was not used to generate the model ., Therefore we consider an ensemble to be validated if new experimental results can be accurately predicted from the ensemble ., For both the WT and ΔK280 sequences , each of the 100 ensembles was ranked based on its ability to predict 13CO chemical shifts ., Based on these data the thirty best ensembles were chosen for further analysis ., The RMS difference between the calculated 13CO chemical shifts and the corresponding experimental values are below 0 . 9 ppm; i . e . , below the error associated with available chemical shift prediction algorithms ( Table 1 ) 20 ., To further demonstrate that these thirty ensembles can reproduce additional data not used in the model constructed , we computed the error between calculated amide hydrogen ( 1HN ) chemical shifts and the corresponding experimental values ., The resulting values agreed with the experimentally measured ones to within 0 . 3 ppm ( Table 1 ) ., As expected , structures that comprise the WT ( Figure 3A ) and ΔK280 ( Figure 3B ) ensembles are heterogeneous in that they sample a wide range of conformations ., Since each of the 30 ensembles represents an independent representation of the unfolded state , we searched for local structural motifs that are found in all of the ensembles ., More precisely , the existence of a local conformation that is consistently adopted by a given subsequence in MTBR2 suggests that this conformation is needed to reproduce the experimental results ., We therefore consider conserved motifs to represent local conformational preferences ., We begin with an assessment of the local conformation of PHF6* in both the WT and ΔK280 ensembles ., Since PHF6* in the WT sequence spans residues 275–280 , the ΔK280 mutant sequence has a deletion in the six-residue stretch corresponding to PHF6* ., However , since residue 281 is also a lysine , the ΔK280 mutant contains an equivalent PHF6* subsequence at its N-terminus ( Figure 4 ) ., This allows us to directly compare the conformation of PHF6* in both sequences ., To identify preserved conformations of PHF6* , we first determined the different types of structures that this subsequence can adopt by clustering structures using only the backbone atoms of PHF6* ( Figure 5 ) ., The probability that a given cluster occurs in an ensemble is equal to the sum of the weights of structures in that ensemble that contains a motif in the cluster ., Preserved structural motifs are defined as clusters that have a nonzero weight in every ensemble ( Figure 5 ) ; i . e . , a preserved motif is found in all ensembles ., For comparison , we repeated this procedure for all contiguous six-residue subsequences within MTBR2 , yielding a collection of approximately 300 clusters that represent all possible structural motifs in our ensembles that any six-residue sequence in MTBR2 can adopt ., Using the criterion outlined above , roughly 5% of these clusters were preserved across all ensembles ., In WT MTBR2 , clustering based on the conformation of PHF6* yielded 12 distinct conformations ., However , only one of these states was present in all 30 ensembles ( Figure 6A and 6B ) ., Similarly , while PHF6* clusters into 11 distinct conformations in the mutant ΔK280 ensembles , only one conformation was preserved ( Figure 6C and 6D ) ., In both cases , the preserved conformation of PHF6* is extended and has φ , ψ angles that fall within the broad region of the Ramachandran plot corresponding to β-structure ., This observation is consistent with the notion that PHF6* a priori adopts extended conformations that can readily form cross β-structure with other tau monomers 21 ., Since the formation of cross β-structure is believed to play an essential role in the formation of protein aggregates , these data are consistent with the notion that PHF6* promotes aggregation by forming β-structure between tau monomers 11 , 12 ., To explore the effect of the ΔK280 mutation on the local structure of MTBR2 , we analyzed the structure of the subsequences 278INKKLD283 and 278IN-KLDL284 in the WT and ΔK280 sequences , respectively ., For WT MTBR2 , two conformations for 278INKKLD283 were found in all ensembles ., The first is a loop/turn that is associated with a change in the direction of the mainchain ( Figure 7A and 7B ) ., In this structure residue K280 has φ , ψ angles of approximately −102° and −30° , respectively; i . e . , mainchain dihedral angles consistent with an α-helical/turn conformation ., The second conformation is more extended , having φ , ψ angles that place its residues within the broad region corresponding to extended β-structure ( Figure 7C and 7D ) ., In the mutant sequence , residue K280 is absent and the corresponding sequence , 278IN-KLDL283 , has one preserved conformation ., The deletion of residue 280 , which can adopt an α-helical/turn conformation in the native sequence , leads to a relative increase in results in extended states in this region ( Figure 7E and 7F ) ., The deletion , however , also introduces a slight kink in the mainchain of the sequence ( Figure 7F ) ., In a prior work , N–H residual dipolar coupling ( RDC ) values were measured for residues in the WT K18 construct in polyacrylamide gel 22 ., While most residues in MTBR2 have relatively large negative RDC values , S285 has a large positive value 22 ., This difference can be explained by either a change in the local alignment tensor at S285 , or the presence of α-helical/turn structure at this site 23–26 ., Accelerated molecular dynamics simulations of WT K18 , however , confirm that the sequence 283DLSN286 samples turn conformations with relatively high frequency 22 ., In light of these observations , we explored the structure of the six residue segment , 282LDLSNV287 , which includes residue S285 ., This region adopts two conformations that are preserved across all WT ensembles ., One of the conformations contains a loop/turn ( Figure 8A and 8B ) where residue S285 has φ , ψ angles of −63° and −39° , respectively; i . e . , near the optimal α-helical values ( Figure 8B ) ., The alternate conformation is extended and does not result in a change in the direction of the mainchain ( Figure 8C and 8D ) ., However , in the ΔK280 mutant , 282LDLSNV287 has one structure that is preserved across all ensembles ( Figure 8E and 8F ) ., In this structure S285 again adopts φ , ψ angles ( −95° and −63° , respectively ) that are consistent with an α-helical/turn conformation ( Figure 8F ) ., These data agree with the RDC data mentioned above and suggest that this region in both the WT and mutant sequences is able to adopt turn-like conformations in solution as well as in a polyacrylamide gel ., Dynamical simulations provide a valuable tool for the analysis of unfolded proteins , providing insights that would be difficult to obtain from experiments alone 27 ., A number of simulation methods have been developed to model the unfolded states of proteins and useful insights have been obtained with these techniques ., Many of these approaches generate ensembles by directly incorporating experimental constraints into molecular dynamics simulations in order to facilitate conformational sampling ., These methods bias molecular trajectories to sample conformers that are consistent with a given set of experimental data ., One problematic issue with biased sampling , however , is that it can suffer from over-fitting—a process that may yield a distribution of conformers that does not accurately model the range of structures that comprise the unfolded state 27 ., Given this concern , a number of unbiased methods have been developed to generate ensembles for unfolded proteins ., These approaches utilize fast algorithms , which do not employ a physical potential energy function , to obtain representative structures of the unfolded state , and in some cases experimental data can then be used to improve the resulting ensembles 28–31 ., The algorithm ENSEMBLE , for example , adjusts population weights for pregenerated conformers to improve agreement with experimental data in a manner similar to that described here 30 ., A unique feature of the present method is that it does not strive to generate a single ensemble that represents the unfolded state ., Given that accurate modeling of an unfolded protein is an undetermined problem , it is likely that there are a number of different ensembles that agree with any given set of experimental data ., Moreover , given the immense number of potential conformations that an unfolded protein can adopt , this may be true even when a relatively large number of experimental constraints are used to construct the ensemble ., Hence our goal was to construct several candidate ensembles , each of which agrees with a given set of experimental constraints , and focus our analysis on local structural features that are preserved across all ensembles ., Local structural features that are found in all independent ensembles likely represent motifs that are required to reproduce the experimental data ., In other words , given the underdetermined nature of the problem , it is not clear how to determine when one has the “correct” ensemble ., However , local structural motifs that consistently appear in all independent ensembles are likely to also be present in the “correct” ensemble ., Consequently , we consider locally preserved structural motifs to represent local conformational preferences ., An important consideration in our method is the choice of experimental data that is used to build and validate the constructed ensembles ., In principle , EMW can use any set of experimental measurements to optimize and validate model ensembles ., Indeed , as more structural information is made available , additional data can and should be used to further refine the set of model ensembles ., In this regard , we note that although a number of NMR measurements have been made on native tau constructs , the data available for constructs containing a ΔK280 mutation is relatively limited ., In a prior study , nuclear chemical shifts and HSQC spectra were measured for the K18ΔK280 construct , which contains all four MTBRs and the ΔK280 mutation 17 ., Data were obtained for both free K18ΔK280 and for K18ΔK280 in the presence of the polyanion heparin and microtubules 17 ., However , as we are interested in building structural models for MTBR2 in solutions free of compounds that promote tau self-association ( e . g . , heparin ) and free of proteins known to bind tau , we focused on measurements obtained with the free K18ΔK280 construct ., Additionally , as there are a number of existing methods that relate chemical shift measurements to three dimensional protein structures 20 , 32–34 we considered 13Cα , 13CO , 1HN , and 15N chemical shift measurements; i . e . , the only available chemical shifts for K18ΔK280 17 ., Furthermore , established methods for estimating NMR chemical shifts can predict carbon and amide proton chemical shifts with an error of approximately 1 ppm or less , while the error associated with predicting nitrogen chemical shifts is substantially larger ( ∼2–2 . 5 ppm ) 20 , 33–35 ., Therefore we focused on the 13Cα , 13CO , and 1H chemical shifts for this study because these data represent measurements that can be calculated with the greatest accuracy and that are available for both native tau constructs and the ΔK280 mutant ., It has long been recognized that chemical shifts of a given residue are , in general , largely a function of the local environment of the residue in question 36 , 37 ., Since we generate ensembles that agree with chemical shifts , a limitation of the results reported here is that we do not explicitly include experimental data that more directly reveal information about non-local interactions ., While long range contacts have been identified in some natively unfolded proteins ( e . g . , 19 ) , the dimensional scaling characteristics of intrinsically disordered proteins suggests that stable long-range contacts are sparse in these systems 38 ., Nevertheless , we suggest that the combination of a physical potential energy function , which can in principle model long range interactions , and experimentally determined chemical shifts can provide insight into the structure of proteins in general ., In this regard we note that data are emerging that suggest that backbone chemical shifts , when used in conjunction with a physical energy function , may be sufficient to adequately predict tertiary folds , and consequently stable non-local contacts , for some proteins 39 , 40 ., Although our work focuses on the structure of the MTBR2 without explicitly including other MTBRs , our findings may also have implications for full length tau ., Once a representative set of conformers for MTBR2 is generated , we strive to ensure that the calculated chemical shifts agree with chemical shifts obtained using a construct that contains all MTBRs ., This helps to guarantee that the ensemble models the structure of MTBR2 as it appears in full length tau ., In short , we are not interested in the structure of MTR2 as it appears alone in solution; instead we hope to deduce structural features of MTBR2 as it appears in full length tau ., In addition , as MTBR2 contains an aggregation-initiating sequence that is known promote tau aggregation in vitro as well as the site of a mutation that leads to in increased tau aggregation in vitro and in vivo , studies of both its WT and mutant forms may lead to insights into the mechanism of tau aggregation 12 , 15 , 41 ., The ability to form intermolecular β-sheet conformations appears to be a relatively general property of polypeptide chains that are associated with disorders of protein misfolding and aggregation 42–45 ., Therefore it is likely that an inherent propensity to form extended conformations , that are consistent with β-structure , will promote aggregation in natively unfolded systems ., When EMW is applied to MTBR2 , we find that the aggregation-initiating sequence , PHF6* , adopts an extended conformation in both the WT and ΔK280 ensembles , a finding consistent with the observation that these peptides can initiate tau aggregation 11 , 12 ., Interestingly , in a prior work we demonstrated that a related hexapeptide , PHF6 , preferentially adopts an extended state that can facilitate the formation of cross-β-structure between tau monomers 21 ., The present study suggests that this property is preserved when aggregation-initiating sequences are part of their corresponding MTBRs ., That is , PHF6* a priori adopts extended conformations that can readily form hydrogen-bonded β-structure ., Additionally , a recent survey of amyloidogenic proteins suggests that fibrillogenesis for natively unfolded proteins involve the formation of partially folded intermediates that can subsequently go on to form amyloid fibrils 45 ., Our findings are consistent with these observations ., That is , our results imply that formation of a locally stable , and extended , conformation plays a role in the formation of tau aggregates ., Recently , several studies have attempted to characterize residual structure of MTBRs in tau 17 , 18 , 22 , 46–48 ., These studies can be roughly divided into two categories: descriptions of ensemble average characteristics based on NMR measurements 17 , 18 , 22 , 46 , and NMR solution structures of local regions obtained by adding organic solvents to stabilize a unique fold 47 , 48 ., Since the presence of organic solvents leads to significant changes in the conformational distribution of states , as evidenced by dramatic changes in the CD spectra 5 , 47 , 48 , the physiologic relevance of these latter results remains unclear ., However , early characterizations of MTBRs in nonorganic solvents , found that the PHF6 region likely has a higher propensity for extended , β-strand-like conformations—a finding in accord with our data 18 , 46 ., Given that both WT and ΔK280 tau contain aggregation-initiating sequences ( Figure 4 ) , it is not clear how β-strand propensity in this region explains the difference in aggregation potential between the two sequences ., Therefore to deduce structural features of the ΔK280 mutant that explain its proclivity to form aggregates , we analyzed the structure of MTBR2 in the vicinity of the mutation site ., Unfolded ensembles of WT MTBR2 contain two conformations at the mutation site that were present in all ensembles—a loop/turn conformation and an extended state ., In contrast to the WT MTBR2 ensembles , models of ΔK280 in the same region had one conformation that was present in all ensembles ., This state is relatively extended and contains a kink at the site of the deletion ., While the slight disruption in the extended state of the mutant may also influence the ability to form hydrogen-bonded cross-β-structure , a loop/turn at the C-terminus of PHF6* constitutes a much greater impediment to the formation of β-structure ., Since residue K280 has a relative preference for nonextended states , deletion of this residue leads to increased sampling of extended states downstream from PHF6* ., The relative preference for extended structures downstream from PHF6* in the ΔK280 mutant suggests that the ability to propagate β-structure distal to PHF6* can affect the aggregation potential of tau ., These observations therefore explain how the deletion of a single residue can change the aggregation potential of tau ., We also find that in both WT and mutant ensembles residue S285 can adopt φ , ψ angles consistent with an α-helical/turn structure ., Recent data on the WT sequence are also consistent with these observations as RDC values and molecular dynamics simulations suggest that S285 adopts an α-helical/turn structure ., Since those experiments were performed in polyacrylamide gel , our data suggest that this structure also occurs with relatively high frequency in solution ., It is also worthwhile to note that although we find that a six-residue region including K280 can adopt a similar loop/turn conformation , the associated RDCs for this region are not associated with a change in sign , like that observed at S285 27 ., Nonetheless , unlike RDC measurements for folded proteins , RDC values for unfolded proteins can be difficult to interpret 49 ., This is due , in part , to the fact that prior to the measurement of RDC values , the protein of interest must first be embedded in an alignment medium 26 ., This induced steric alignment of unfolded proteins may lead to results that do not fully capture the range of structures that an unfolded protein can adopt in solution ., Hence the absence of particular RDC values in polyacrylamide gel ( or any other alignment media ) does not necessarily imply that a given conformation is not present in solutions containing the unfolded protein of interest ., The formation of tau aggregates is likely a complex process as a number of factors have been shown to influence the formation of tau aggregates in vitro 1–3 ., Consequently , there may be additional factors that contribute to the increased ability of the ΔK280 mutant to form aggregates; e . g . , a ΔK280 mutation leads to an overall decrease in the strength of the intermolecular charge-charge repulsion between tau monomers that self-associate 12 ., Nonetheless , our data demonstrate that small changes in the sequence of tau can lead to localized structural changes in the unfolded ensemble that may affect taus ability to form cross-β-structure ., Overall , our data suggest that small sequence-specific changes can promote tau aggregation and that interventions that prevent the propagation of β-structure downstream from aggregation-initiating sequences , may form the basis for therapies that prevent tau aggregation ., The EMW method constructs ensembles for unfolded proteins that are consistent with a given set of experimental data ., Our model for an unfolded ensemble consists of structures corresponding to local energy minima and associated probabilities ( weights ) that are assigned to the different conformations ., For this work , the experimental measurement used to optimize and validate the model ensembles are chemical shifts for the second tau microtubule binding repeat 17 ., In principle , EMW can be used with any given set of experimental data ., In this application we focus on chemical shifts that were available for both the K18 and K18ΔK280 constructs ., The EMW method can be decomposed into three steps, ( i ) conformational sampling ,, ( ii ) model optimization , and, ( iii ) ensemble validation ., Conformational sampling uses high temperature molecular dynamics ( MD ) followed by minimization of the resulting structures ( i . e . , quenched dynamics ) to create a library of widely varying conformations representing minima on the potential energy surface ., Model optimization is performed to select a subset of these structures and optimize weights that represent the relative prevalence of each structure ., Validation is performed by computing additional chemical shifts that not used to construct the ensemble and comparing these data to experimentally measured carbonyl carbon shifts ., In what follows we outline each step of the EMW method ., We searched for conformations of six-residue subsequences that are present in every ensemble ., Six residues was a natural characteristic size for a local region of interest , as it is the length of PHF6* ., To this end , all structures in each ensemble of either WT or ΔK280 MTBR2 were clustered using a matrix consisting of the pairwise RMSD backbone deviation of the each contiguous six-residue segment ., Structures were clustered using MATLAB ( Mathworks ) such that the maximum RMSD between two structures in a cluster was 2 . 5 Å ., A range of maximum RMSD values ( 1–6 Å ) were examined empirically , and it was found that a cutoff of 2 . 5 Å

Introduction, Results, Discussion, Methods

Tau is a natively unfolded protein that forms intracellular aggregates in the brains of patients with Alzheimers disease ., To decipher the mechanism underlying the formation of tau aggregates , we developed a novel approach for constructing models of natively unfolded proteins ., The method , energy-minima mapping and weighting ( EMW ) , samples local energy minima of subsequences within a natively unfolded protein and then constructs ensembles from these energetically favorable conformations that are consistent with a given set of experimental data ., A unique feature of the method is that it does not strive to generate a single ensemble that represents the unfolded state ., Instead we construct a number of candidate ensembles , each of which agrees with a given set of experimental constraints , and focus our analysis on local structural features that are present in all of the independently generated ensembles ., Using EMW we generated ensembles that are consistent with chemical shift measurements obtained on tau constructs ., Thirty models were constructed for the second microtubule binding repeat ( MTBR2 ) in wild-type ( WT ) tau and a ΔK280 mutant , which is found in some forms of frontotemporal dementia ., By focusing on structural features that are preserved across all ensembles , we find that the aggregation-initiating sequence , PHF6* , prefers an extended conformation in both the WT and ΔK280 sequences ., In addition , we find that residue K280 can adopt a loop/turn conformation in WT MTBR2 and that deletion of this residue , which can adopt nonextended states , leads to an increase in locally extended conformations near the C-terminus of PHF6* ., As an increased preference for extended states near the C-terminus of PHF6* may facilitate the propagation of β-structure downstream from PHF6* , these results explain how a deletion at position 280 can promote the formation of tau aggregates .

Alzheimers disease pathology is characterized by two types of protein aggregates that are found in the brain—amyloid plaques and neurofibrillary tangles ., Several studies suggest that these aggregates also play an active role in the disease process ., Thus , an understanding of disease pathogenesis may be facilitated by a detailed characterization of the proteins that comprise these aggregates ., Our study aims to model structural characteristics of tau protein , which is found in neurofibrillary tangles ., Modeling of tau is particularly difficult because the protein is intrinsically disordered and therefore must be modeled as an ensemble of structurally dissimilar states ., We developed a novel modeling approach that incorporates experimental measurements to generate ensembles of conformations that model the unfolded state of tau ., By analyzing structural properties in these model ensembles for both normal and disease-associated forms of the protein , we identify structural features that may facilitate tau aggregation .

biophysics/protein folding

journal.pbio.2004752

Agent-specific learning signals for self–other distinction during mentalising

Social interactions are underpinned by an ability to infer the mental states of self and others , referred to as mentalising 1 ., The discovery of mirror neurons in the macaque premotor cortex 2 introduced the notion that in mentalising , the primate brain might directly simulate another agent’s cognitive process ., More recently , functional magnetic resonance imaging ( fMRI ) studies 3–8 and intracranial recordings 9 in humans , as well as single-cell recordings in monkeys 10 , have shown that when a subject observes another agent interact with its environment , the subject’s brain encodes not only the other agent’s motor activity but also their reward prediction errors ( RPEs ) ., In other words , subjects appear to simulate the reinforcement learning processes of other agents ., These simulated learning signals localise to specific cortical regions , such as the anterior cingulate gyrus 9–11 ., A functional segregation of learning signals can allow the brain to encode information about whether learning is arising out of the individual’s own behavioural interactions with the environment or whether learning is taking place vicariously through observing the behaviour of another agent ., In a similar vein , it has been suggested that the medial prefrontal cortex ( mPFC ) supports a functional axis that encodes whether behaviour is executed or imagined 12 , 13 ., For simulation to be useful in social interactions , the brain must discriminate signals attributed to self from simulated signals attributed to other agents 14–17 ., An impairment in this self–other distinction is a defining feature of autism spectrum disorder 18–21 ., Similar impairments have also been reported in conditions such as schizophrenia 22 , 23 , psychopathy 17 , and borderline personality disorder 24 ., An aberrant self–other distinction might also underpin the social dysfunction seen in psychopathologies , including depression 25 , 26 and addiction 27–29 ., A prefrontal coding scheme that discriminates between instrumental and observational learning , or executed and imagined behaviour , could provide a useful heuristic for a self–other distinction but would be insufficient for discriminating amongst signals attributed to different agents as a general-purpose computation ., For instance , the false belief task 30 , a standard test of mentalising ability , requires that subjects make inferences about an environment and then selectively attribute one belief-state to self and a different belief-state to another agent for whom the environment is only partially observable ., These belief-states are not informed by the behaviour of the subject or the other agent but arise through passively observing the environment ., In this case , neural coding schemes that discriminate between executed behaviour and observed or imagined behaviour cannot facilitate a self–other distinction , and a more fundamental computation for selectively attributing signals to different agents is required ., Thus , an open question for simulation theory is how self–other distinction is achieved 16 ., If inferring another agent’s mental state requires the brain to simulate that agent’s computations , how are the outputs of those computations identified as ‘belonging to other’ ?, One possibility is that variables for learning and decision-making are encoded in distinct neural activity patterns , depending on the agent to whom these signals are attributed ., Such architecture would entail an encoding of agent identity intrinsic to representations of these low-level signals ., A second possibility is that a learning signal is always encoded in an agent-independent pattern ., In this case , the learning signal and the identity of the agent to whom the signal is attributed would need to be encoded in 2 separate activity patterns ., Here , we test whether learning signals are encoded in agent-specific activity patterns , and thus whether self–other distinction requires agent identity to be encoded separately from a low-level learning signal ., We used a novel paradigm inspired by false belief tasks , in which subjects learned about a fluctuating state in the environment ., In so doing , they were also required to intermittently switch their frame of reference between self and other ., The 2 agents ( self and other ) received different information such that their belief trajectories were uncorrelated , enabling us to measure self-attributed and other-attributed learning signals independently ., Unlike previous paradigms eliciting simulated signals , subjects did not observe the agent’s behaviour , and there was no reinforcement of learning by either reward or punishment ., Learning for self and learning for other thus recruited the same input channels and were identically salient and identically motivating ., The task design rules out a potential confound of simulated reward learning 3–8 wherein self-attributed reward-related decision signals ( such as RPEs ) pertain to rewards expected to be received by the participant , while other-attributed reward-related decision signals do not ., We measured the neural encoding of learning signals using magnetoencephalography ( MEG ) in order to measure whether the representations of these signals are agent-specific and also how agent-specificity evolves over the time course of a single trial ., Of note , our task design required sparse probe trials and therefore a larger total number of trials than would be possible to acquire in a single fMRI session ., We present data from 38 healthy adults ( see Materials and methods for participant details ) ., During MEG scanning , they observed a sequence of samples from a Bernoulli distribution , with a drifting Bernoulli parameter P . This is the probability , on each trial , of seeing 1 of 2 possible outcomes ., Another participant , who sat outside the scanner in a different room , played the exact same game ( see Materials and methods ) ., This other subject was able to observe some of the samples seen by the scanned participant ( ‘shared’ trials ) but not all of them ( ‘privileged’ trials ) ., Additionally , the nonscanned participant was occasionally presented with misleading samples ( ‘decoy’ trials ) ., Therefore , the nonscanned participant sampled evidence that induced a false belief about P ( Pfb ) ., These 3 types of trial ( ‘privileged’ , ‘shared’ , and ‘decoy’ ) were balanced in frequency and distributed evenly throughout the task in a pseudorandom order ., In a version of the game we refer to as the social version ( SV ) , ‘privileged’ and ‘decoy’ trials were signalled to the scanned participant , who thus had access to information about both P and Pfb ., On ‘self’ probe trials , the scanned participant was required to report their estimate of P , by positioning an arrow along a virtual continuous scale that ranged from a probability of 0 ( certain to see one outcome ) to 1 ( certain to see the other outcome ) ., On ‘other’ probe trials , the scanned participant had to put themselves in the shoes of the nonscanned participant and report their estimate of Pfb ., Crucially , the information that the scanned subject used to compute Pfb was sampled at the same rate as the information used to compute P . We refer to the subject’s belief about P as B , and we refer to their belief about Pfb as Bfb ., The structure of sampling trials and probe trials is outlined in Fig 1A ., All subjects also played a nonsocial version ( NSV ) of this game , which did not involve another participant ., Here , the scanned participant had to keep track of the belief-state of a fictional ‘computer’ that received limited and misleading information and stored a false estimate of P ( Pfb ) ., On ‘other’ probe trials in the NSV , the scanned participant was asked to imagine themselves in a counterfactual situation , wherein they acted using the false information provided by the computer ., Thus , in the SV , participants switched their frame of reference between self and other , whilst in the NSV , participants switched their frame of reference between self and a counterfactual self ., The only structural differences between the SV and NSV pertained to the cover stories , the images used for stimuli , and the wording of the ‘other’ probe trials ( see Materials and methods ) ., Note that for both the SV and NSV , we pregenerated trial sequences that minimised correlation between the belief trajectories of the 2 agents ( Fig 1D , Fig 1E; also see Materials and methods ) ., For each subject , we assessed behavioural accuracy independently for the 2 versions of the task ( SV and NSV ) as well as on the 2 types of probe trial ( ‘self’ and ‘other’ ) ., This resulted in 4 conditions overall , for which accuracy was defined relative to chance performance ( see Materials and methods ) ., Where an accuracy of 0 is equivalent to chance-level performance , mean accuracies ( with SDs ) for the SV were 0 . 11 ( 0 . 04 ) in ‘self’ probe trials and 0 . 11 ( 0 . 04 ) in ‘other’ probe trials ., For the NSV , these were 0 . 11 ( 0 . 04 ) for ‘self’ probe trials and 0 . 10 ( 0 . 05 ) for ‘other’ probe trials ( see S1 Fig ) ., The group performed significantly better than chance in all 4 conditions as assessed with 4 separate one-sample t tests on the mean accuracies per subject ( P < 0 . 0001 in all 4 conditions ) ., There were no differences in accuracy between the 2 probe trial types , or between the 2 versions of the game ( ANOVA: main effect of probe trial type: F1 , 148 = 1 . 54 , P = 0 . 22; main effect of game version: F1 , 148 = 0 , P = 0 . 96; interaction: F1 , 148 = 0 . 06 , P = 0 . 81 ) ., Thus , all 4 conditions were similar in difficulty ., We fitted 21 models to the probe trial behaviour of each subject , separately for the SV and NSV ., There were 3 principal groups of model ( see Table 1 for a summary and Materials and methods for details ) ., Group A models assumed that subjects’ beliefs were constructed from an average over recently sampled information ., Group B models were based on an assumption of Rescorla-Wagner ( RW ) updating 31 , in which the models derive prediction errors ( PEs ) on each trial from the difference between the actual and expected outcomes ., PEs updated the beliefs of self , while PEo was a simulation of the other agent’s PE , for updating the beliefs of other in the SV or ‘counterfactual self’ in the NSV ., A subset of group B models also included ‘leak’ parameters that allowed PE signals to erroneously update the wrong agent’s belief , thus capturing an inability to maintain separate belief updates for the 2 agents ., All group B models also assumed that the PEs had a value of 0 on ‘decoy’ trials whilst PEo had a value of 0 on ‘privileged’ trials ., Group C models were like group B models except that they did not make this assumption; instead , they allowed PEs and PEo to update the beliefs of self and other , respectively , in all 3 trial types ., We compared models separately for the SV and NSV using the Bayesian Information Criterion ( BIC ) ., For both the SV and NSV , model 8 had the lowest mean BIC value ( Fig 2A ) ., This model incorporated 2 separate PE signals and included 4 free parameters: a learning rate ( α ) regulated the update of the beliefs of the 2 agents , a memory decay parameter ( δ ) controlled the rate of ‘forgetting’ for the beliefs of the 2 agents , and 2 temperature parameters ( τs , τo ) governed choice stochasticity on ‘self’ probe trials and ‘other’ probe trials , respectively ( see S3 Fig for parameter recovery ) ., This model generated synthetic choice data qualitatively similar to subjects’ real choice data ( Fig 2B ) ., Noting large intersubject variability in BIC values , we also employed a random-effects Bayesian model selection 32 to compare the winning model with the second best model ( S4 Fig ) and found , for both the SV and NSV , an exceedance probability in excess of 0 . 99 ., This is the probability that the winning model better explains a randomly chosen subject’s data ., We also assessed the correlation between parameter estimates fitted to the SV and parameter estimates fitted to the NSV ( Fig 2C ) ., For each model , we obtained a correlation coefficient for each parameter and then took the mean of those coefficients as a summary statistic for the between-game consistency of the model ., Because parameter values were not normally distributed , we computed the nonparametric Spearman’s rank correlation coefficient ., We found that model 8 also had the highest between-game consistency ., Thus , this model captured consistent dispositions in subjects’ choice behaviour across the 2 games ., After fitting models to the behavioural data , we then had parameter estimates for each model and each subject ., We used model 8 along with each subject’s parameters for this model to generate trial-wise estimates of latent PEs and beliefs , which we then used in subsequent analyses on the MEG data ., Note that PEs and belief values generated by other models were very similar , and consequently , our findings were not sensitive to the selection of a particular model ., We next asked whether |PEs| and |PEo| were encoded in the MEG signal , recorded during task performance , using a mass-univariate analysis ( Fig 3 ) ., For each subject , we fit 2 separate linear regression models at each sensor and each peristimulus time point ., We obtained trial-wise estimates of |PEs| and |PEo| using the winning model’s estimated free parameters fitted to the choice data ., The first model regressed |PEs| against the event-related field ( ERF ) on ‘privileged’ and ‘shared’ sampling trials ( i . e . , trials in which PEs was nonzero ) ., The second regression model regressed |PEo| against ERFs on ‘decoy’ and ‘shared’ trials ( i . e . , trials in which PEo was nonzero ) ., This resulted in 4 statistical maps over sensors and time , 2 for the SV and 2 for the NSV ., We converted each of these maps into a 3D image ( 2 spatial dimensions and 1 temporal dimension ) of baseline-corrected effect sizes ( see Materials and methods ) ., To make group-level inferences , we conducted a one-sided Wilcoxon signed-rank test at each pixel to determine whether the group median was significantly greater than 0 ., We thresholded the resulting 3D image with a cluster-forming threshold ( P < 0 . 001 ) and identified clusters of contiguous suprathreshold pixels , which could extend through space and time ., We determined whether any clusters were significantly larger than chance with a nonparametric permutation test to generate null distributions of cluster extent ., In each of the 4 regression models , we found clusters significantly larger than chance at a 0 . 05 family-wise error ( FWE ) level ., In the SV , the clusters extended through parietal and occipital sensors , whilst in the NSV , the clusters extended through frontal and parietal sensors ., For the SV PEs , the largest cluster extended from 330 ms to 390 ms and comprised 2 , 628 pixels ( threshold 612 ) ., For the SV PEo , the largest cluster extended from 340 ms to 420 ms and comprised 2 , 032 pixels ( threshold 624 ) ., For the NSV PEs , the largest cluster extended from 370 ms to 440 ms and comprised 1 , 621 pixels ( threshold 554 ) ., For the NSV PEo , the largest cluster extended from 310 ms to 370 ms and comprised 847 pixels ( threshold 569 ) ., Despite finding significant clusters at the group level , we also noted large intersubject differences in these spatiotemporal patterns ( e . g . , S5 Fig ) ., We wanted to test whether we could distinguish a neural pattern encoding a PEs from a pattern encoding a PEo and thus determine whether a self–other distinction can be achieved on the basis of these signals ., A typical way to identify the neural pattern encoding a PE is to regress the magnitude of the PE ( derived from our learning model ) against the brain activity , across trials ., This would yield a single beta estimate at each sensor , capturing the slope of the relationship between PE and brain activity at that sensor ., However , in order to use powerful multivariable methods like support vector machine ( SVM ) classification to look for differences in the spatial patterns of PEs and PEo , it was necessary to obtain multiple samples of each pattern ., One way to achieve this is to divide the data into multiple partitions ( without replacement ) and repeat the analysis in each partition to obtain multiple independent samples of the spatial pattern for each type of PE ., This is the approach we opted for , using the smallest possible partitions: pairs of trials ( Fig 4A ) ., To maximise power without introducing bias , we randomly partitioned trials into pairs under the constraint that each pair contained 1 trial above the median |PE| and 1 trial below the median |PE| ., Thus , the difference in brain activity between the 2 trials within a pair corresponded to a representation of |PE| ., We performed this random partitioning independently for PEs and PEo ., This resulted in 2 sets of difference images , corresponding to neural representations of |PEs| and |PEo| ., Finally , we could then apply multivariable methods to classify whether each difference image was a representation of |PEs| or |PEo| ., It should be noted that this method differs slightly from typical pattern-based neuroimaging analyses described in , for example , 33 ., Usually , such an analysis looks for a neural representation of some variable ., This is achieved by training a classification or regression model to distinguish patterns of neural activity corresponding to different values of that variable ., Above-chance accuracy of the model indicates that the brain activity contains information about the variable ., However , in our case , we were interested in a difference in the representation of a variable between 2 conditions ., Because the representation itself is defined by a difference in neural activity between a large PE and a small PE , we were looking for a difference of differences ., Thus , it was necessary to train classifiers on patterns of subtracted activity rather than activity patterns from individual trials ., We started with N trials in total ., First , we partitioned all ‘privileged’ and ‘shared’ trials ( 2N/3 trials ) by median split on |PEs| ., We then randomly sampled 2 trials , one from either side of this partition , and subtracted the ERF on the low |PEs| trial from the ERF on the high |PEs| trial , at every sensor and time point ., For ease of reference , we call this contrast image a ‘pseudotrial’ ., We continued randomly sampling pairs of trials without replacement to obtain a total of N/3 pseudotrials ., Each of these pseudotrials describes the difference in activity between a trial with a high |PEs| and a trial with a low |PEs| ., The brain activity in the difference image thus constituted a representation of |PEs| ., Second , we partitioned all ‘decoy’ and ‘shared’ trials ( 2N/3 trials ) by median split on |PEo| ., We carried out the same procedure as for PEs , resulting in a second set of N/3 pseudotrials , each of which constituted a representation of |PEo| ., At each time point , we trained a classifier to distinguish PEs pseudotrials from PEo pseudotrials ., We tested classifiers in crossvalidation , yielding a time course of classification accuracies ( CAs ) ., The absolute difference in CA underlying reliable effects was , in some cases , as small as 1% ., In observing this , we note that effect sizes cannot be inferred from absolute CAs 34–36 ., Therefore , to make statistical inferences , we adopted a permutation-based method to determine whether any CA was significantly better than chance ., This procedure has been recommended for making inferences on ‘information-based’ neural measures such as CA 35 ., To derive a threshold for statistical significance , we repeated the whole pseudotrial analysis many times , each time using data generated from a permuted trial sequence ., For every permutation , we took the maximal CA ( or maximal difference in CA between the SV and NSV ) across all time points ., We thus generated a null distribution of maximal CAs ( or maximal CA differences ) ., The 95th percentile of the distribution was taken as our threshold for statistical significance ., This procedure allowed us to make statistical inferences without making assumptions about how CAs ( or CA differences ) are distributed , whilst also correcting for multiple comparisons across time points , at a 0 . 05 FWE level ., We found that self and other could be classified significantly above chance level from the spatial patterns of activity that represented |PEs| and |PEo| approximately 300 ms after stimulus onset ( Fig 4B ) ., However , CA did not exceed chance level when we conducted this same analysis on the NSV data ., Moreover , at approximately 300 ms , there was a significant difference between CA in the SV and CA in the NSV ., Thus , distinct spatial activity patterns for |PEs| and |PEo| were evident in the SV but not in the NSV ., This implies information about self and other is intrinsic to the representations of low-level learning signals , whilst information about self and counterfactual self is not ., To test the robustness of this finding , we performed 2 additional variants of the analysis , by constructing pseudotrials from subjects’ trial-wise ‘signed beliefs’ ( B and Bfb ) and ‘unsigned beliefs’ ( |B − 0 . 5| and |Bfb − 0 . 5| ) ., The former are the subject’s trial-wise estimates of the underlying Bernoulli parameter from the perspective of each agent ., The latter are the absolute distances of these estimates from 0 . 5 , which represents an equal probability of either outcome ., The ‘unsigned belief’ is thus a measure of confidence in what the next outcome will be ., It should be noted that here , we can use all N trials to generate pseudotrials ., Thus , we end up with N/2 pseudotrials for each class and N pseudotrials in total ., We found that classifiers trained on pseudotrial data , generated from either of these latent variables , could predict agent identity ( self or other ) significantly above chance in the SV ., However , in the NSV , the classifiers could only predict agent identity ( self or counterfactual self ) for pseudotrials generated from ‘signed beliefs’ , and in this instance , the signal was weaker and occurred later in time than was the case for the SV ( Fig 4B ) ., Furthermore , we found that CAs for the SV were significantly larger than CAs for the NSV at multiple time points for both of these pseudotrials ., Finally , for comparison , in a separate analysis classifying between the visual stimuli , we obtained similar decoding accuracies in the SV and NSV ( S6 Fig ) ., An important question is whether the neural distinction in learning signals is related to a behavioural measure of self–other distinction ., A subset of our behavioural models ( models 11 to 19 ) included a ‘leak’ ( λ ) parameter that governed the extent to which PEs was erroneously used to update Bfb and/or PEo was erroneously used to update B , thus indexing an inability to discriminate between 2 different agents’ learning processes ., We estimated λ values by selecting the best-fitting λ-containing model for each individual subject ., If the best model contained 2 λ parameters , we took the mean of the 2 values ., We derived 2 estimates of λ for each subject , one for the SV and one for the NSV ., We then computed , for each subject , a metric describing overall neural self–other distinction ., In order to do this , we took the maximal CA from each of the time courses from the 3 types of pseudotrial ( Fig 4B ) and summed these 3 numbers ., This provided one number for neural agent decoding in the SV and another number for neural agent decoding in the NSV ., Because λ in the SV and λ in the NSV were strongly correlated across subjects , we examined the difference between the SV and NSV ., Due to the non-normally distributed parameter estimates , we computed a nonparametric Spearman’s rank correlation coefficient ., We found a strong negative correlation ( Fig, 5 ) between the neural decoding contrast ( SV − NSV ) and the estimated λ contrast ( SV − NSV ) : Spearman’s rho: −0 . 43 , P < 0 . 01 ., We also tested the accuracy of a linear regression model that used neural decoding contrasts to predict the estimated λ contrasts ., Here , we used crossvalidation with random subsampling ( train on half , test on half ) and recorded the correlation between predicted and observed values on every fold ., The median Pearson coefficient across 10 , 000 folds was 0 . 31 , which was significantly greater than chance as determined by a nonparametric permutation test ( P = 0 . 039 ) ., We also found a significant positive correlation ( Spearman’s rho: 0 . 32 , P < 0 . 05 ) when , instead of using raw parameter estimates , we used the relative model evidence ( BIC ) of a subject’s best lambda-containing model and best nonlambda model ., In other words , subjects whose SV behaviour is better explained by a model with lambda parameters than their NSV behaviour show less neural self–other distinction in the SV than in the NSV ., These findings show that , in subjects for whom agent identity could be more accurately decoded in the SV than in the NSV , there was also more behavioural evidence for segregating the beliefs of self and other in the SV than in the NSV ., This suggests that the distinctiveness of neural patterns encoding learning signals attributed to 2 different agents is predictive of how well a subject behaviourally succeeds in distinguishing between these 2 agents’ beliefs ., Finally , we asked whether neural agent decoding relates to intersubject differences in subclinical personality traits ., All subjects filled out 5 questionnaires of interest: Beck Depression Inventory ( BDI ) , Empathy Quotient ( EQ ) , Interpersonal Reactivity Index ( IRI ) , Inventory of Callous-Unemotional traits ( ICU ) , and the Community Assessment of Psychic Experience ( CAPE ) ., These questionnaires were specifically chosen to assess the presence of psychopathological traits previously proposed to relate to a dysfunctional self–other distinction or more general social cognitive deficits 17 , 21 , 22 , 25 ., These questionnaires assessed autistic ( EQ ) , schizotypal ( CAPE ) , antisocial ( ICU ) , and depressive ( BDI ) traits as well as general capacities for empathy and sympathy ( EQ , IRI ) ., We also obtained measures of response bias using an additional questionnaire , the Balanced Inventory of Desirable Responding ( BIDR ) 37 ., None of the subjects were considered to have an unacceptably high response bias ( see Materials and methods ) ., We performed dimensionality reduction on age and gender-controlled personality questionnaire data ( see Materials and methods ) using a principal components analysis ( PCA ) , having included all subscales of the 5 questionnaires of interest , giving 9 dimensions in total ( Fig 6A ) ., The first principal component ( PC1 ) explained 32% of the variance in the questionnaire data and loaded negatively with both subscales of the CAPE questionnaire ( schizophrenia ) , BDI ( depression ) , ICU ( antisocial behaviour ) , and 1 subscale of the IRI ( personal distress in social situations ) ; it also loaded positively with EQ and other subscales of the IRI ( Fig 6A ) ., Thus , the PC1 negatively captured psychopathological features in our personality data in a nonspecific manner ., We projected the personality data into the space of this principal component to obtain a score for each subject ., First , we correlated the neural self–other distinction metric , as described in the previous section , with the PC1 scores ., When using the contrast of ( SV − NSV ) , this yielded a significant correlation: R = 0 . 39 , P = 0 . 017 ( Fig 6B ) ., We also tested the accuracy of a linear regression model that used neural decoding contrasts to predict the PC1 scores , using the same method as described for Fig 5 ., The median Pearson coefficient across 10 , 000 folds was 0 . 34 , which was significantly greater than chance as determined by a nonparametric permutation test ( P = 0 . 04 ) ., Therefore , subjects for whom we obtained higher CAs in the SV than in the NSV scored higher on the PC1 ., In other words , subjects for whom it was easier to neurally decode self from other than to decode self from counterfactual self scored higher on a nonspecific anti-psychopathological component ., When looking at the SV and NSV neural self–other distinction metrics separately , we found a significant positive correlation for the SV ( R = 0 . 43 , P < 0 . 01 ) but no significant correlation for the NSV ( R = 0 . 01 , P = 0 . 94 ) ., We then investigated the temporal evolution of this relationship for each of the 3 types of pseudotrial ., At each peristimulus time sample , we correlated the subjects’ PC1 scores with ( CA SV − CA NSV ) to generate a time course of Pearson coefficients ( Fig 6C ) ., Using permutation-based thresholding , to correct for multiple comparisons in time and between the 3 types of pseudotrial , we found a significant positive correlation ( P < 0 . 05 FWE ) approximately 110 ms after stimulus onset ( Fig 6C ) when using the ‘signed belief’ pseudotrials ., This falls within the window of significant self–other distinction in signed beliefs ( 100–340 ms ) as shown in Fig 4B ., We show that a representation of a learning signal ( PE or belief ) is encoded with a different neural spatial pattern when the signal is attributed to self as compared to when it is attributed to another agent ., Intersubject variability in this difference correlated between subjects with a behavioural measure of self–other distinction and with subclinical psychopathological traits ., This suggests that self–other distinction is realised by an encoding of agent identity that is intrinsic to low-level learning signals , and the fidelity with which this occurs is an important dimension of variation between individuals ., In our experiment , subjects had to solve 2 simultaneous computational problems ., The first problem was predicting what the next outcome would be ., The second problem was identifying whether this belief-state about the next outcome should be attributed to one agent or another , a computation that requires a self–other distinction ., We found a spatial segregation between self-attributed and other-attributed learning signals ., This means that the neural representations of beliefs and PEs in this task also contained information about the agent to whom these signals belong , and consequently , the neural resources that compute the next outcome also inevitably contribute to computing a self–other distinction ., It is of interest , therefore , that the degree of spatial segregation was correlated with a behavioural measure of self–other distinction derived from our learning models ., Previous work has shown that neuronal populations in the macaque anterior cingulate cortex preferentially encode simulated RPEs 10 and the future decisions 38 of another monkey ., Conversely , human fMRI data has identified common activations in the mPFC that represent RPEs 6 or subjective preferences 12 for both self and other in an agent-independent manner ., Likewise , mirror neurons recorded from the macaque premotor cortex are also agent independent 2 ., A self–other distinction in the affective domain has been reported in terms of dissociable networks for experienced versus vicarious pain 39 , 40 , though other reports suggest that these are both subserved by the same structures 41 , 42 ., The above accounts are conflicting with respect to whether self- and other- attributed signals share common or distinct neural activations ., One possible reason for this is that previous studies eliciting simulated signals were not indexing a self–other contrast per se but rather a contrast of executed behaviour versus observed behaviour , in which the subject receives feedback from the observee’s behaviour ., In these cases , learning or decision variables are discriminated not by virtue of the agent to whom the

Introduction, Results, Discussion, Materials and methods

Humans have a remarkable ability to simulate the minds of others ., How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown ., Here , we investigated how fundamental neural learning signals are selectively attributed to different agents ., Specifically , we asked whether learning signals are encoded in agent-specific neural patterns or whether a self–other distinction depends on encoding agent identity separately from this learning signal ., To examine this , we tasked subjects to learn continuously 2 models of the same environment , such that one was selectively attributed to self and the other was selectively attributed to another agent ., Combining computational modelling with magnetoencephalography ( MEG ) enabled us to track neural representations of prediction errors ( PEs ) and beliefs attributed to self , and of simulated PEs and beliefs attributed to another agent ., We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed , consistent with a neural self–other distinction implemented via agent-specific learning signals ., Strikingly , subjects exhibiting a weaker neural self–other distinction also had a reduced behavioural capacity for self–other distinction and displayed more marked subclinical psychopathological traits ., The neural self–other distinction was also modulated by social context , evidenced in a significantly reduced decoding of agent identity in a nonsocial control task ., Thus , we show that self–other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals ., The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker .

In order for people to have meaningful social interactions , they need to infer each other’s beliefs ., Converging evidence from humans and nonhuman primates suggests that a person’s brain can represent a second person’s beliefs by simulating that second person’s brain activity ., However , it is not known how the outputs of those simulations are identified as ‘yours and not mine’ ., This ability to distinguish self from other is required for social cognition , and it may be impaired in mental health disorders with social cognitive deficits ., We investigated self–other distinction in healthy adults learning about an environment both from their own point of view and the point of view of another person ., We used computationally identified learning variables and then detected how these variables are represented by measuring magnetic fields in the brain ., We found that the human brain can distinguish self from other by expressing these signals in dissociable activity patterns ., Subjects who showed the largest difference between self signals and other signals were better at distinguishing self from other in the task and also showed fewer traits of mental health disorders .

recreation, learning, decision making, social sciences, mathematical models, neuroscience, learning and memory, cognitive psychology, cognition, brain mapping, neuroimaging, research and analysis methods, random walk, imaging techniques, games, behavior, human learning, mathematical and statistical techniques, psychology, biology and life sciences, magnetoencephalography, cognitive science

journal.pcbi.1000735

Quantitative Modeling of Escherichia coli Chemotactic Motion in Environments Varying in Space and Time

Bacterial chemotaxis is one of the most studied model systems for two-component signal transduction in biology 1 ., In Escherichia coli , the relevant proteins and their interactions in the chemotaxis signaling pathway have been studied over the past decades and a more or less complete qualitative picture of chemotaxis signal transduction has emerged ( Figure 1A ) ., It is now known 2 that external chemical signals are sensed by membrane-bound chemoreceptors called methyl-accepting chemotaxis proteins ( MCP ) , which form a functional complex with two types of cytoplasmic proteins: the adaptor protein CheW and the histidine kinase CheA ., Upon binding to an attractant ( repellent ) ligand molecule , the receptor suppresses ( enhances ) the autophosphorylation activity of the attached CheA , and transduces the external chemical signal to inside the cell ., The histidine kinase CheA , once phosphorylated , quickly transfers its phosphate group to the two downstream response regulator proteins CheY and CheB 3 ., The small protein CheY-phosphate ( CheY-p ) , before it gets dephosphorylated by the phosphatase enzyme CheZ , can diffuse from the receptor complex to the flagellar motor ., CheY-p can bind to FliM proteins of the flagellar motor , increasing the probability of changing its rotation from counterclockwise ( CCW ) to clockwise ( CW ) , which in turn causes the motion of the E . coli cell to change from run to tumble ., After a brief tumble , the cell runs again in a new random direction ., The directed motion of bacterial chemotaxis is achieved when the run length is longer in a favorable direction 1 ., Significant progress has been made in several key areas towards quantitative understanding of the E . coli chemotaxis signaling pathway ., First , through experiments and modeling , it is now well established that the high sensitivity of the E . coli chemotactic response 4 , 5 , 6 is partly caused by the cooperative interaction between neighboring MCP complexes 7 , 8 , 9 , 10 within the polar receptor cluster ., Another important feature of the E . coli chemotaxis signaling pathway is its ability to adapt to a wide range of environments by slow methylation and demethylation of the MCP receptors at four specific residues ., Two enzymes are involved in adaptation: CheR adds methyl groups to the chemoreceptor , while CheB-p removes them 3 ., Both of these processes depend on the receptor kinase activity , and this feedback mechanism is believed to be responsible for the near perfect adaptation of the system 11 , 12 , 13 , 14 , which maintains the kinase activity within the sensitive range of the motor ., A general model framework was recently developed to describe the adaptation kinetics and receptor cooperativity , and all previous experiments with time-varying signals can be explained consistently within this model 15 ., Finally , the response of the E . coli flagellar motor to intracellular CheY-p level was measured quantitatively by Cluzel et al 16 at the single cell level ., The dose-response curve has a high Hill coefficient , possibly caused by cooperative interactions between the FliM proteins in the FliM ring ., As pioneered by Dennis Bray 10 , 17 , computer modeling has been used in studying bacterial chemotaxis motion 18 , 19 , 20 , 21 ., With improved quantitative understanding of the chemotaxis signaling pathway , up-to-date knowledge of the key pathway components can be integrated to form a system-level model of the signaling network to quantitatively study various chemotaxis behaviors ., In this study , we used a coarse-grained Signaling Pathway-based E . coli Chemotaxis Simulator ( SPECS , an acronym introduced here for convenience ) model to study chemotaxis behaviors in a series of environments with increasing spatiotemporal complexity ., We originally developed the SPECS model to explain the recent microfluidics experiments with stationary linear gradients in 21 ., Here , we focused on using this model to study E . coli chemotaxis motion in spatiotemporally-varying environments and to understand how the chemotaxis motion is controlled by the cells internal molecular signaling processes , in particular its adaptation dynamics ., Quantitative comparisons with the classical capillary assay 22 , 23 , where the attractant concentration changes both in space and time , were made to test and verify the model ., We argue that the SPECS model can be used to predict quantitatively the motion of E . coli cells in any given spatiotemporally varying chemical field , such as in the natural environment ., Following Tu et al . 15 , each functional MCP receptor complex can be either in the active or the inactive state; these states are separated by a free energy difference , , where is the number of the responding receptor dimers in the complex ., The ligand-receptor binding time , estimated from the measured ligand-receptor dissociation constant 24 and the diffusion limited on-rate , is much shorter than the receptor methylation time scale ., The measured CheA auto-phosphorylation time ( ∼0 . 025s ) 25 is also much shorter than ., Therefore , the kinase activity can be determined by the quasi-equilibrium approximation: ( 1 ) Using the Monod-Wyman-Changeux allosteric model to describe the receptor cooperativity 26 , 27 , the free energy difference can be written as: ( 2 ) where is the methylation level-dependent free energy difference; and are the dissociation constants of the ligand to the inactive and the active receptor , respectively ., Quantitatively , for MeAsp , which is the chemo-attractant studied here , we use the parameters determined by fitting the pathway model to the in vivo FRET data 28 ., The free energy contribution due to methylation of the receptor is taken to be linear in : as used in 15 and supported by recent experiments 29 ., The parameters and can be estimated from the dose-response data 6 , 30 of the cheRcheB mutants with different methylation levels; for MeAsp , they are roughly ., The kinetics of the methylation level can be described by the dynamic equation 15: ( 3 ) The general methylation rate function is expressed by a linear approximation with and as the linear rates for methylation and demethylation processes ., The simple form is based on the assumption that CheR only methylates inactive receptors and CheB-p only demethylates active receptors , which are required to achieve near perfect adaptation in the kinase activity 11 , 13 , 14 , 31 ., More complicated Michaelis-Menten equations can be used 32 , but they do not affect the results here as the system ( pathway ) normally operates in the linear range ., For simplicity , we take to fix the steady state activity ; another value was used without affecting the results ., The methylation rates can be estimated by the adaptation time from experiments with step function stimuli 4; for MeAsp , we use ., The dependence of the chemotaxis motion on is studied in this paper ., A simple phenomenological model is used here to model the E . coli cell motion ., Let represent the tumble and run states of the cell ., For the time period , a cell switches from state to state with probability ., The response curve measured by Cluzel et al 16 determined the ratio between the two probability rates for one flagellar motor ( see Supporting Information ( SI ) Figure S1 for details on effects of multiple flagella ) : ( 4 ) with and ., We assume the tumble time is roughly constant ( independent of ) by setting , where ., is the average duration of the tumble state ., Correspondingly , the probability rate to switch from the run state to the tumble state is: ( 5 ) In our simulation , is assumed to be proportional to the kinase activity: without considering the nonlinear dependence 33 ( is defined later in this paragraph ) ., This linear approximation is justified by the relatively small range of activity variation in our study ., Including the CheY-p dephosphorylation dynamics explicitly with dephosphorylation time did not significantly change the results ( see Figure S1 ) ., Spatial effects are neglected as the diffusion time for CheY-p across the cell length is short 34 and the CheY-p level was measured to be spatially uniform in wt E . coli cells 35 ., In steady state , and the average run time is ., Therefore , is set by ., After a tumbling episode , a new run direction is chosen randomly with the run velocity 36 ., In our simulations , a small time step is chosen to resolve the average tumbling time ., As first pointed out by Berg and Brown 37 , one important factor in chemotaxis is the rotational diffusion of the cell due to the Brownian fluctuation of the medium ., This can be simply captured by adding a small Gaussian random angle to the direction of the velocity in every run time step ., : ( 6 ) The amplitude of this rotational diffusion angle is roughly 10° as estimated by the fact that it takes ∼10sec ., for the cell to lose its original direction of motion ( i . e . , turn more than 90° ) by pure rotational diffusion ., For the boundary condition , we assume that when a cell swims to a wall , it swim along the wall for some time ( 1–5 sec . ) before swimming away 21 , 38 ., The boundary condition can affect the cell distribution near the wall , but should not strongly affect the overall behavior of the cell distribution in the bulk ., In the natural environment , chemical signals not only vary in space , they also fluctuate in time ., The fluctuation of a chemical signal ( ligand concentration ) sensed by a moving cell can be caused by:, 1 ) randomness in the cell motion , i . e . , the run-tumble motion and the rotational diffusion of the cell; and, 2 ) temporal variation of the environment itself ., Here , we investigated the effects of the latter due to ligand ( spatial ) gradients that also vary with time ., In particular , based on the feasibility of future experimental tests of our predictions , we studied the case that E . coli swims in a finite channel of length where the attractant concentration is linear inside the channel with a slope that oscillates in time with a frequency : ( 10 ) with a fixed maximum ligand spatial gradient ., was used in this study ., We simulated the motion of cells and their dependence on the frequency in a channel with , the same geometry as used in our previous study of the stationary linear gradient 21 ., To separate the effects of the time-varying gradient from those caused by the fast time variations due to run-tumble transitions ( ∼1 s ) and rotational diffusion ( ∼10 s ) , we studied the dependence of cell motion on ligand concentration oscillation with relatively low frequency ., We found that the average position ( center of mass ) of the E . coli cells oscillated with the same periodicity as the ligand concentration ( Figure 5A ) ., The amplitude of the response depends on the frequency ( Figure 5B ) ., For spatial gradients changing with very low frequencies , the response amplitude becomes comparable to the size of the channel and stays almost constant independent of due to the boundary effects ., For higher frequencies , the amplitude the average cell motion decreases with ., This dependence of cell motion on the frequency of the gradient can be understood by studying the mean-field dynamics of the average position of the cell by assuming an instantaneous response to the ( logarithmic ) ligand gradient: ( 11 ) where is the motility constant defined in the last section ., For high frequency , the amplitude of the cell motion is much less than the channel size , , and the above equation can be solved approximately to obtain: ( 12 ) which shows that the response amplitude decreases with frequency as , consistent with our simulation results ( Figure 5B ) ., Equation ( 12 ) breaks down and the amplitude saturates at low frequencies , determined by the finite channel size ( Figure 5B ) ., Quantitatively , the full Eq ., ( 11 ) does not yield to any simple scaling dependence of response amplitude on ., How do the adaptation dynamics affect the cells response to time-varying gradients ?, We investigated this question by varying ( was co-varied to keep fixed ) ., We found that for smaller values of , a transition frequency appears within the frequency range studied here ( Figure 3C ) ., For frequencies higher than , the decay in response amplitude became significantly steeper than for frequencies below ., The transition frequency is determined by the adaptation rate , with faster rates resulting in higher ( Figure 5C , inset ) ., This transition to faster decay in response amplitude is likely caused by the finite adaptation ( response ) time of the underlying signaling system , which leads to the dependence of the instantaneous drift velocity on the relative gradients in its past with a exponentially decaying function with time scale : If the time-dependent term in the denominator of the integrant of the above equation is neglected for small amplitudes of cell motion , we can estimate the amplitude of cell motion at high frequencies: , which has a similar time-dependence as in Eq ., ( 12 ) but with an extra factor due to the finite adaptation time ., For high frequencies , this extra factor causes the response amplitude to decay with an extra factor , consistent with our simulation results ( Figure 5C ) ., The responses of E . coli to pure temporal oscillatory signals have been studied experimentally 39 , theoretically 15 , and within the framework of information theory 41 ., However , for an E . coli cell moving in a time-varying spatial gradient , its signaling pathway dynamics becomes much more complex as the signal ( ligand concentration ) changes due to both its temporal and spatial variations convoluted by the cell motion , which is in turn determined by the pathway dynamics ., The interplay between spatial and temporal signals coupled to cell motion can lead to rich cellular behaviors , which we have just started to explore ., The quantitative dependence of cell motion on and depends on the details of the ligand spatial profile and a simple analytical form is not available ., However , the damped chemotaxis motion in environments with high-frequency gradients should be generally true due to the finite adaptation time of the cell ., This frequency-dependent chemotaxis behavior can be tested by future experiments in spatial gradients that also change with time with tunable frequencies ., The capillary assay is an ingenious experimental method developed more than a century ago by W . Pfeffer and later perfected by J . Adlers group to study bacterial chemotaxis 22 , 23 ., A capillary tube containing a solution of attractant is inserted into a liquid medium ( the pool ) containing bacteria ., A gradient of the attractant is subsequently developed due to diffusion and bacterial cells swim into the tube following the gradient ., The number of bacteria entering the capillary is counted at a given time ( 45–60 min ) , as a measure of the cells chemosensitivity ., This method is still in use today because of its simplicity and also because the spatiotemporally varying attractant profile mimics the realistic situation of attractant released from a stationary source ., Here , we modeled the responses of cells in the capillary assay and quantitatively compared the results with the experimental measurements ., The time-dependent attractant concentration was determined by solving the diffusion equations: ( 13 ) where Cc and Cp stand for the ligand concentrations in the capillary and the suspension pool respectively ., is the diffusion coefficient of the attractant ligand in water ., Using the cylindrical symmetry of the geometry , the ligand concentration was solved in cylindrical coordinates with the boundary conditions: ( 14 ) where is the radius of the capillary tube ., The initial conditions at are that the ligand concentrations in the capillary and the suspension pool are C0 and C1 respectively: ., The time-dependent concentration profiles are shown in Figure 6A ., Starting from being at , the ligand concentration at a particular position in the pool peaks at a given time depending on its location ( Figure 6A , inset ) ., Furtelle and Berg 42 calculated the attractant profile by solving the diffusion equations asymptotically away from the mouth of the capillary ( ) , and their analytical asymptotic solution is shown together with our exact numerical solution in Figure 6B at different times along the center line of the capillary tube ( ) ., The two solutions show remarkable agreement except for near the mouth of the capillary , where the Furtelle and Berg solution breaks down ., Later , we show this inaccuracy near the capillary mouth can cause large differences in computing the result of a capillary assay ., From the spatial-temporal profile of the attractant , cell motion can be calculated by using SPECS ., We considered the bacterial cells started randomly in a region of around the capillary mouth inside the pool ( Figure 6C ) ., The probability of a cell at an original position that eventually ends in the capillary at 45min was calculated ( Figure 6D ) ., Even cells originally far away from the mouth of the capillary can enter it , with decreasing with both z and r ., Finally , we calculated the chemotactic responses in capillary assay for different values of and/or and compared the results directly with the experiments by Mesibov et al 22 , 23 ., The results showed that the number of bacteria accumulating in the capillary 45 min after the capillary is inserted is a function of ( ) for both the experiments and our simulation ( Figure 6E ) ., The results from our model , with no adjustable parameters , agree quantitatively with the experiments ., The dashed line in Figure 6E represents the results of our cell motility simulation by using the Furtelle and Berg solution for the ligand concentration ., Evidently , even though the Furtelle and Berg solution is accurate away from the capillary mouth , its inaccuracy near the capillary mouth changes the results significantly ., The accuracy of the ligand profile near the mouth of the capillary is important because cells that eventually enter the capillary need to pass the mouth ., In another set of experiments by Mesibov et al 22 , both and are changed while keeping their ratio fixed at ., The sensitivity curves are plotted as the number of bacteria accumulating in the capillary after one hour versus ( Figure 6F ) ., The simulation results ( filled triangles ) showed quantitative agreement with the experiments 17 , further verifying our model ., Qualitatively , the shape of the capillary assay response can be understood as the chemotaxis response ( sensitivity ) is small for either very large or very small ligand concentrations ., Quantitatively , the peak response concentration is larger than the center of the chemoreceptor sensitivity region 6 due to the fast decay of the ligand concentration from inside the tube toward the pool ( Figure 6A ) ., In this paper , a coherent picture of how E . coli chemotaxis motion depends on the spatio-temporally varying chemical environment and its intracellular signaling dynamics has emerged from our modeling study ., The chemotaxis drift velocity is mainly determined by three factors: , where , and depend on the ligand concentration , the gradient of the logarithmic concentration , and the frequency of the temporal-variation of respectively ., For ligand concentrations within a wide ( chemosensitive ) range ( as focused on in this paper ) , the concentration-dependent factor , and depend linearly on the gradient of the logarithmic concentration until it saturates at a high relative gradient as demonstrated by the second factor ., For a ligand gradient that varies with high frequency , is damped by the third factor due to the finite adaptation time in the underlying signaling pathway ., We found that the saturation gradient is controlled by the adaptation rate , but the motility constant is not ( although weak dependence on cannot be completely ruled out ) ., The nontrivial scaling dependence , observed in our simulation , can be explained analytically by the dynamics of the internal signaling pathway and the narrow range of kinase activity over which the flagellar motor can response ., Calibrated quantitatively by the most up-to-date in vivo FRET experiments , our model ( SPECS ) captures the essential characteristics of the underlying signaling network , in particular the receptor-receptor cooperativity and the near-perfect adaptation kinetics , within a simple unified mathematical description ., We described the internal state of a cell at the coarse-grained ( cellular ) level without modeling the details of individual signaling molecules as used in other simulation methods such as StochSim 17 , AgentCell 18 , and E . solo 10 , which are particularly suited to studying noise in the intracellular signaling process ., This coarse-grained approach , similar to that used in RapidCell 20 , greatly reduces the computational requirements for the simulation ., For example , the SPECS model allows us to simulate E . coli populations of 103–104 cells in a linear ligand concentration profile and 102–103 cells in a capillary assay in real time with a standard desktop computer ( Matlab code available upon request ) ; and the simulation results agree quantitatively with both the recent microfluidics experiments and the classical capillary assay measurements , without any fitting parameters ., Predictions , such as bacterial chemotactic responses in exponential ligand profiles and oscillatory ligand gradients , are made with our model and can be tested by future experiments ., Indeed , the SPECS model can be used to predict E . coli chemotaxis motions in arbitrary spatial-temporal varying environments efficiently and accurately ., Perhaps equally important as predicting cellular behaviors , the SPECS model , which captures the essential features of the underlying pathway , enables us to understand these behaviors based on the key intracellular signaling dynamics , some of which can be difficult to study directly by experimental methods ., For example , the constant drift velocity in an exponential ligand profile was found to be caused by a constant shift in the average kinase activity , which is maintained by a linearly increasing mean methylation level in balancing the exponentially increasing ligand concentration ., At the individual cell level , this constant activity shift is also shown to be responsible for the intriguing observation that the average backward run time in an exponential gradient is similar to the average run time in the absence of a gradient , while the forward run time is much longer ., The SPECS model can be used to study various noise effects as well ., The effect of the cell-to-cell variability for chemotaxis behavior in a linear gradient in a closed channel was studied by choosing ( from a broad distribution ) a random value for the internal parameters such as N , methylation rate constants , and swimming velocity in each individual cell ., We found that although individual cells now behaved differently , at the population level , the average steady state behavior , such as cell density , remained almost the same ( see Figure S4 ) except for a slight change near the boundary for the case with run velocity variation ., The main source of ( external ) temporal noise for the cells chemotactic sensory system comes from the run and tumble motion of the cell ., Even in a smooth spatial ligand gradient , the randomness in the cell motion can lead to large temporal fluctuations in the input ( ligand concentration ) to the E . colis chemotactic sensory system ., This source of external noise was included in our model ., The effects of fluctuations in intracellular signaling remain to be examined ., By adding noise to our pathway model , it would be interesting to see whether and how the internal signaling noise affects the population level behavior ., The model framework described here lays the foundations for modeling cellular motility behavior based on the relevant underlying signaling pathway dynamics without describing the details of the individual signaling molecules ., The current model can be extended in several directions to study many other interesting chemotaxis phenomena ., For example , the interaction between the cells and the liquid-solid surface were oversimplified in this paper ., Cells are known to turn with a preferred handedness when they run into a surface 43 and can remain near the surface for a long time 38 before they finally escape ., It would be interesting to see how different “boundary conditions” affect the overall behavior of cells ., In its natural environment , a cell must make decisions in the presence of multiple , sometimes conflicting cues ., Our model can be extended to include integration between different chemotactic signals 26 and applied to study bacterial motion in the presence of multiple stimuli gradients ., The same chemotaxis pathway seems to be able to sense and react to other non-chemical stimuli , such as temperature 44 and osmotic pressure 45 ., Our model can be modified to study the responses of cells to these non-chemical stimuli by incorporating the dependence of various ( kinetic and energetic ) biochemical parameters on the strength of these external stimuli ., Recently , we carried out such extensions to study the microscopic mechanism of precision-sensing in E . coli thermotaxis 46 ., Finally , the chemo-attractant ( MeAsp ) considered in this paper is non-metabolizable and its concentration gradient is formed independent of the cell population ., In other cases , such as in swarm plate experiments 47 , the attractant gradient is generated by consumption of the nutrient , which is also the chemo-attractant ., In addition , cells can communicate by emitting chemo-attractants 48 ., The consumption and generation of the attractant , together with cell division , need to be incorporated into our model to understand complex pattern formations in different swarm plate experiments ., We are currently pursuing some of these directions .

Introduction, Methods, Results, Discussion

Escherichia coli chemotactic motion in spatiotemporally varying environments is studied by using a computational model based on a coarse-grained description of the intracellular signaling pathway dynamics ., We find that the cells chemotaxis drift velocity vd is a constant in an exponential attractant concentration gradient L∝exp ( Gx ) ., vd depends linearly on the exponential gradient G before it saturates when G is larger than a critical value GC ., We find that GC is determined by the intracellular adaptation rate kR with a simple scaling law: ., The linear dependence of vd on G\u200a=\u200ad ( lnL ) /dx directly demonstrates E . colis ability in sensing the derivative of the logarithmic attractant concentration ., The existence of the limiting gradient GC and its scaling with kR are explained by the underlying intracellular adaptation dynamics and the flagellar motor response characteristics ., For individual cells , we find that the overall average run length in an exponential gradient is longer than that in a homogeneous environment , which is caused by the constant kinase activity shift ( decrease ) ., The forward runs ( up the gradient ) are longer than the backward runs , as expected; and depending on the exact gradient , the ( shorter ) backward runs can be comparable to runs in a spatially homogeneous environment , consistent with previous experiments ., In ( spatial ) ligand gradients that also vary in time , the chemotaxis motion is damped as the frequency ω of the time-varying spatial gradient becomes faster than a critical value ωc , which is controlled by the cells chemotaxis adaptation rate kR ., Finally , our model , with no adjustable parameters , agrees quantitatively with the classical capillary assay experiments where the attractant concentration changes both in space and time ., Our model can thus be used to study E . coli chemotaxis behavior in arbitrary spatiotemporally varying environments ., Further experiments are suggested to test some of the model predictions .

A computational model , based on a coarse-grained description of the cells underlying chemotaxis signaling pathway dynamics , is used to study Escherichia coli chemotactic motion in realistic environments that change in both space and time ., We find that in an exponential attractant gradient , E . coli cells swim ( randomly ) toward higher attractant concentrations with a constant chemotactic drift velocity ( CDV ) that is proportional to the exponential gradient ., In contrast , CDV continuously decreases in a linear gradient ., These findings demonstrate that E . coli senses and responds to the relative gradient of the ligand concentration , instead of the gradient itself ., The intracellular sensory adaptation rate does not affect the chemotactic motion directly; however , it sets a maximum relative ligand gradient beyond which CDV saturates ., In time-varying environments , the E . colis chemotactic motion is damped when the spatial gradient varies ( in time ) faster than a critical frequency determined by the adaptation rate ., The run-length statistics of individual cells are studied and found to be consistent with previous experimental measurements ., Finally , simulations of our model , with no adjustable parameters , agree quantitatively with the classical capillary assay in which the attractant concentration changes both in space and time ., Our model can thus be used to predict and study E . coli chemotaxis behavior in arbitrary spatiotemporally varying environment .

biophysics/theory and simulation, computational biology/systems biology, computational biology/signaling networks

journal.pntd.0000929

Temporal Fluctuation of Multidrug Resistant Salmonella Typhi Haplotypes in the Mekong River Delta Region of Vietnam

The Mekong river delta is located in the south of Vietnam ( Figure 1 ) in an area of 40 , 000 square kilometres ( 12% of Vietnams land mass ) and is home to over 20% of Vietnams population ., It is the area where the Mekong river divides into multiple channels and drains into the South China sea ., The low-lying nature of the land and the seasonal fluctuation in water level make the region particularly vulnerable to flooding ., The human-restricted disease typhoid fever is endemic to the Mekong delta region 1 , 2 , with a mean incidence of ∼80 cases per 100 , 000 people per year 1 , 2 , 3 , 4 ., Salmonella Typhi ( S . Typhi ) , the bacterium causing typhoid fever , is transmitted human-to-human in areas with poor sanitation ., The first multidrug resistant ( MDR; defined as resistance to chloramphenicol , ampicillin and co-trimoxazole ) typhoid outbreak in Vietnam occurred in Kien Giang in the Mekong river delta in 1993 5 , and since then the fluoroquinolones have become the first choice for the treatment of typhoid fever ., MDR S . Typhi is usually associated with self-transferrable IncHI1 plasmids carrying multiple resistance genes encoded within mobile genetic elements 6 , 7 , 8 , 9 , 10 ., Between 1994 to 1998 , over 80% of S . Typhi strains isolated in the Mekong delta region were reported to be MDR 11 , and declined to approximately 50% between 2002 and 2004 5 , 11 , 12 ., This decline may have been catalysed by the change in treatment policy and the widespread use of fluoroquinolones ( such as ciprofloxacin and ofloxacin ) , which are effective against MDR strains 13 , 14 ., While high-level resistance to fluoroquinolones remains uncommon in Vietnam and other endemic typhoid regions , there has been a sharp increase in the proportion of S . Typhi isolates that are resistant to nalidixic acid 11 ., Nalidixic acid ( Nal ) is a quinolone antimicrobial ( the precursor of fluoroquinolones ) and the main mechanism for Nal resistance in S . Typhi is mutation of the DNA gyrase gene , gyrA 11 , 15 ., S . Typhi strains with Nal resistance-conferring mutations in the gyrA gene usually have elevated minimum inhibitory concentrations ( MIC ) to fluoroquinolone antibiotics such as ciprofloxacin ( MIC ≥0 . 125 µg/ml ) 16 ., However , these organisms are not resistant according to CLSI guidelines , which are currently defined by MIC ≥4 µg/ml to ciprofloxacin 17 ., Even though these strains are not classified as resistant , they are of clinical importance since typhoid patients infected with such strains respond less well to fluoroquinolone therapy 14 , 15 , 18 , 19 ., Such patients frequently have a protracted fever and an increased rate of relapse , compared to those infected with strains that do not have an elevated MIC to fluoroquinolones ( MIC <0 . 125 µg/ml to ciprofloxacin and <0 . 25 µg/ml to ofloxacin ) 15 , 18 , 19 ., Resistance to Nal is therefore often used as a marker to predict how well a patient will respond to therapy with fluoroquinolones ., The incidence of typhoid fever has declined in Vietnam ., Between 1991 and 2001 approximately 17 , 000 cases of typhoid fever ( blood culture confirmed and syndromic cases ) were reported annually through the Vietnamese national surveillance system 1 , 2 , while only 4 , 323 and 5 , 030 annual typhoid fever cases were reported in 2004 and 2005 , respectively ( Source: National Institute of Health and Epidemiology , Ministry of Health , Vietnam ) ., However , 75% of these cases occurred in the Mekong delta 1 , 2 , likely associated with high population density and the propensity of the land to become saturated with floodwaters ., In this region , the occurrence of S . Typhi isolates that are MDR and Nal resistant severely limits treatment options ., More than 95% of S . Typhi isolated in the Mekong delta are now resistant to Nal , placing a considerable pressure on the effective use of fluoroquinolones 11 , 12 ., To compare alternative therapies for typhoid fever patients infected with strains that are MDR and Nal resistant , a randomized controlled trial comparing gatifloxacin ( a newer 8-methoxy fluoroquinolone ) and azithromycin ( a macrolide ) was conducted during 2004–2005 in the Mekong delta region 20 ., Typhoid patients ( adults and children ) were recruited into the study at three hospitals in the south of Vietnam ( details in Materials and Methods , locations are highlighted in Figure 1B ) ., Here , we used a high-throughput single nucleotide polymorphism ( SNP ) typing assay to investigate the population structure of S . Typhi collected during the study 20 , and to determine the genetic mechanisms of drug resistance in this S . Typhi population ., The study was conducted according to the principles expressed in the Declaration of Helsinki and approved by the Institutional Review Board of the Hospital for Tropical Diseases and the Oxford Tropical Research Ethics Committee ( OXTREC ) ., All patients provided written informed consent for the collection of samples and subsequent analysis ( written informed consent was provided by the parents or guardian of children under 18 years of age ) ., S . Typhi isolates were collected during a multicenter clinical trial 20 conducted between January 2004 and December 2005 at, ( a ) the Hospital for Tropical Diseases in Ho Chi Minh City ( n\u200a=\u200a10 ) ,, ( b ) Dong Thap Provincial Hospital , Cao Lanh , Dong Thap province ( n\u200a=\u200a25 ) and, ( c ) An Giang Provincial Hospital , Long Xuyen , An Giang province ( n\u200a=\u200a232 ) ., Locations of, ( b ) and, ( c ) are shown in Figure 1B ., Adults and children over 6 months of age were eligible to be included in the study if they had clinically suspected or culture-confirmed uncomplicated typhoid fever and if fully informed written consent had been obtained ., Patients were tested for typhoid carriage ( via stool culture ) during follow-up appointments at 1 , 3 and 6 months after discharge from hospital ., The 267 isolates presented in this study constitute nearly the full complement of 287 S . Typhi isolated from culture-confirmed typhoid patients enrolled in the trial; the recruitment flow for which is described in detail in 20 ., Antimicrobial susceptibility testing was performed at the time of initial isolation by disc diffusion according to Clinical Laboratory Standards Institute ( CLSI ) guidelines 17 ., Antimicrobial agents tested were: ampicillin , chloramphenicol , trimethoprim-sulfamethoxazole ( co-trimoxazole ) , nalidixic acid , ofloxacin , ciprofloxacin and ceftriaxone ( Oxoid , Basingstoke , UK ) ., Minimum Inhibitory Concentrations ( MICs ) for amoxicillin , chloramphenicol , nalidixic acid , ofloxacin , ciprofloxacin , gatifloxacin , ceftriaxone and azithromycin were determined by E-test ( AB Biodisk , Solna , Sweden ) ., Multidrug resistance ( MDR ) of isolates was defined as resistance to chloramphenicol ( MIC ≥32 µg/mL ) , ampicillin ( MIC ≥32 µg/mL ) and trimethoprim-sulfamethoxazole ( MIC ≥8/152 µg/mL ) ., Nalidixic acid resistance was defined by an MIC ≥32 µg/mL ., After initial isolation , S . Typhi was stored at −70°C in a 20% glycerol solution until required for further analysis and DNA extraction ., To revive frozen organisms , MacConkey and Xylose Lysine Decarboxylase ( XLD ) agar plates were inoculated from the glycerol solution and incubated at 37°C overnight ., To ensure correct identification , colonies were checked using slide agglutination with serotype specific antisera ( Vi , O9 ) and an irrelevant antisera as a negative control ( O4 ) ( Murex , Dartford , United Kingdom ) ., Two mL of nutrient broth were inoculated with single S . Typhi colonies and incubated overnight ., Overnight cultures were centrifuged and S . Typhi DNA was extracted using Wizard Genomic DNA Purification kit ( Promega , USA ) as recommended by the manufacturers guidelines ., DNA was stored at −20°C ., DNA was quantified using the Quant-IT PicoGreen dsDNA Reagent and Kit ( Invitrogen , UK ) ., S . Typhi DNA concentrations were adjusted to 50 ng/mL and 250 ng of DNA were pipetted into 96-well plates ., Each 96-well plate contained two isolates in duplicate and the sequenced S . Typhi isolate CT18 as control for assay reproducibility ., The chromosomal haplotype of S . Typhi isolates was determined based on alleles present at 1 , 485 chromosomal SNP loci identified previously from genome-wide surveys 12 , 21 and listed in 22 , 23 ., IncHI1 plasmid haplotypes were determined based on eight SNPs identified previously 22 , 24 and resistance gene sequences were interrogated using additional oligonucleotide probes ( listed in Table S1 ) ., All loci were interrogated using a GoldenGate custom oligonucleotide array according to the manufacturers standard protocols ( Illumina ) , as described previously 22 , 23 ., A maximum-likelihood phylogenetic tree based on chromosomal SNPs was constructed using the RAxML software 25 ., Clinical data were entered into an electronic database ( Epi Info 2003 , CDC , Atlanta , USA ) ., For comparison of patient characteristics according to infecting S . Typhi haplotypes , Kruskal-Wallis tests were used for analysis of continuous variables ( age , length of stay in hospital , fever clearance time ) and logistic regression was used for categorical variables ( presence of symptoms ) ., Odds ratios were adjusted for duration of fever prior to admission and use of antibiotics prior to admission by including these variables in the logistic regression model ., Where data was missing for a particular patient and variable , that patient was excluded from analysis of that variable ( N≤35 patients ) ., Two-tailed p-values are reported; statistical analysis was performed using the R package ( http://www . r-project . org/ ) ., Oligonucleotide primers for the amplification of the quinolone resistance determining regions in the S . Typhi gyrA gene were as follows 11: GYRA/P1 5′-TGTCCGAG ATGGCCTGAAGC-3′ and GYRA/P2 5′-TACCGTCATAAGTTATCCACG-3′ ., Predicted PCR amplicon size was 347 bp ., PCR was performed under the following conditions; 30 cycles of: 92°C for 45 seconds , 45–62°C for 45 seconds and extension at 74°C for 1 minute , followed by a final extension step at 74°C for 2 minutes ., PCR products were purified and directly sequenced using the CEQ DTCS - Quick Start Kit ( Beckman Coulter , USA ) and the CEQ 8000 capillary sequencer ., The resulting DNA sequence was analyzed using CEQuence Investigator CEQ2000XL ( Beckman Coulter , USA ) ., All sequences were verified , aligned and manipulated using Bioedit software ( http://www . mbio . ncsu . edu/BioEdit/bioedit . html ) and compared to other gyrA sequences by BLASTn at NCBI ., Patient addresses were recorded at the time of hospital admission ., The latitude and longitude of the residences of typhoid fever patients ( to the hamlet/village level ) was assigned from the collected address data using 1/50 , 000 scale maps ( Source: Cartographic Publishing House and VinaREN , Ministry of Natural Resources and Environment , Vietnam ) and cross-checked using the websites http://www . basao . com . vn and http://ciren . vn ., Location data was analysed using Quantum GIS version 1 . 4 . 0 ( http://www . qgis . org/ ) ., Locations were colour-coded according to S . Typhi haplotype and clustering of specific haplotypes was calculated using the nearest-neighbour analysis function ., Nearest-neighbour analysis examines the distances between each point and the closest point to it , and then compares these to expected values for a random sample of points from a CSR ( complete spatial randomness ) pattern ., Significant clustering was inferred by Z-score value ( standard normal variable ) of less than 0; a positive score was interpreted as dispersion of locations ., A recently developed typing system , based on the simultaneous interrogation of 1 , 485 S . Typhi chromosomal single nucleotide polymorphisms ( SNPs ) using a custom Illumina GoldenGate array 22 , 23 , was used to analyse each of the S . Typhi isolates ., This approach facilitates the unequivocal assignment of isolates to haplotypes , allowing closely related strains to be distinguished phylogenetically based on single nucleotide changes ., From 287 patients with culture confirmed typhoid fever recruited between January 2004 and December 2005 20 , 267 S . Typhi were available for SNP typing ., These included 264 S . Typhi isolated from blood culture at admission 20 one relapse isolate and two faecal carriage isolates ., A total of 24 S . Typhi ( 23 isolated from An Giang and one from Dong Thap , randomly distributed throughout the study period ) were not available for SNP typing ., A total of 261 S . Typhi isolates ( 98% ) were of the common H58 haplogroup ., The remaining isolates were of haplotypes H1 ( isolates BJ105 , BJ63 , BJ64 ) , H45 ( isolate BJ264 ) , H50 ( isolate BJ9 ) and H52 ( isolate BJ3; see Figure 2 and Table 1 ) ., The H58 S . Typhi isolates displayed variation at 10 SNP loci ( detailed in 23 ) , which differentiated seven distinct sub-H58 haplotypes , shown in Figure 2 ., However , 242 ( 93% ) of these isolates belonged to just three closely related H58 haplotypes , designated C , E1 and E2 in Figure 2 ( numbers given in Table 1 ) ., The genome of S . Typhi strain AG3 , isolated during the study ( March 2004 ) from a typhoid fever patient living in An Giang province , was sequenced previously 21 ., AG3 belongs to the H58-E2 haplotype , and the SNPs separating E2 from haplotypes E1 and C were originally identified by analysis of the AG3 genome ., Therefore , the ability to differentiate within the cluster of 242 S . Typhi isolates was dependant on the inclusion of strain AG3 in the initial genome sequencing study used to identify SNP loci 21 ., All but one S . Typhi isolated from the blood culture of patients admitted to An Giang Provincial hospital ( 231/232 ) , as well as the two faecal S . Typhi strains isolated from chronic carriers in An Giang , belonged to the S . Typhi H58 haplogroup ., The remaining S . Typhi isolate BJ264 ( see Figure 2 ) was of the H45 haplotype and was isolated from a typhoid fever patient who was resident in neighbouring Can Tho province ., One patient at An Giang Provincial hospital relapsed with symptoms of typhoid fever and had S . Typhi isolated from blood culture 11 days after the initial treatment ( gatifloxacin ) had been completed ., The mother of the patient was found to be a chronic S . Typhi carrier ., All three S . Typhi strains - the patients admission and relapse blood culture isolates and the mothers faecal isolate - belonged to the S . Typhi H58-E2 subtype ., The patients isolates were both MDR and carried the IncHI1 ST6 plasmid ( see below ) , whereas the mothers S . Typhi isolate was plasmid-free and susceptible to all first line antimicrobials at the time of isolation ., All three isolates were Nal resistant but sensitive to gatifloxacin ( MIC 0 . 19 mg/ml ) ., Stool cultures were taken at 1 month ( 96% of patients ) , 3 months ( 93% ) and 6 months ( 44% of follow-up ., Chronic faecal carriage of S . Typhi was detected in only one trial patient ., This was a MDR H58-C strain isolated from stool 6 months after treatment ( with gatifloxacin ) , which was indistinguishable at all assayed loci from the patients original blood culture isolate ., Both isolates were Nal resistant but sensititve to gatifloxacin ( MIC 0 . 19 mg/ml ) ., At Dong Thap Provincial Hospital , only 3 of the 25 S . Typhi isolates did not belong to the H58 haplogroup ., Two H1 isolates ( BJ63 and BJ64; Figure 2 ) were identical at all assayed loci and were isolated on consecutive days from two patients resident in Dong Thap ., A third H1 strain ( BJ105; Figure 2 ) differed from BJ63 and BJ64 at 16 chromosomal SNP loci and was isolated in Dong Thap 14 months after these isolates ., Two siblings from Dong Thap province were admitted on consecutive days in 2004 and were both infected with MDR S . Typhi of the haplotype H58-C ., Of the ten S . Typhi strains isolated at the Hospital for Tropical Diseases in Ho Chi Minh City , eight were members of the H58 haplogroup , with patients resident in Ho Chi Minh City ( n\u200a=\u200a4 ) , Long An ( n\u200a=\u200a1 ) , Kien Giang ( n\u200a=\u200a2 ) and An Giang ( n\u200a=\u200a1 ) provinces , reflecting the larger catchment area of the hospital ., The remaining two S . Typhi were of haplotypes H52 ( BJ3 ) and H50 ( BJ9 ) and were isolated from patients living in Binh Hoa province and Ho Chi Minh City , respectively ., There was no simple association between S . Typhi haplotype and patient age , length of stay in hospital , fever clearance time , vomiting , abdominal pain , hepatomegaly or relapse ( Table 2 ) ., However , upon admission , patients infected with S . Typhi haplotype H58-E2 tended to report lower frequencies of diarrhoea and headache and higher frequencies of constipation compared to patients infected with other haplotypes , including H58-C ( see Table 2 ) ., The GoldenGate assay incorporated probes targeting IncHI1 plasmid sequences , allowing for detection of the presence of IncHI1 plasmid within the genomic DNA extracted from each S . Typhi isolate ., The assay indicated that a total of 139 S . Typhi isolates harboured an IncHI1 plasmid ., All plasmids were of the IncHI1 ST6 sequence type 24 and all plasmid-bearing isolates belonged to the S . Typhi H58 haplogroup ( see Table 1 ) ., The MDR IncHI1 plasmid was more common among H58-C isolates than H58-E2 isolates ( 86% vs 19% , see Table 1 ) ., Of the 139 S . Typhi isolates giving positive signals for IncHI1 SNP loci , 137 ( 99% ) were classified as MDR by antimicrobial susceptibility testing conducted at the time of isolation ., One other IncHI1-positive isolate tested positive by GoldenGate assay for the genes sul1 , sul2 , dfrA7 , tetACDR , strAB , bla and cat ( resistance genes; functions outlined in Table S1 ) like the MDR isolates , yet had low MICs for chloramphenicol , ampicillin and trimethoprim-sulfamethoxazole ., An additional S . Typhi isolate , BJ5 , was resistant to ampicillin and trimethoprim-sulfamethoxazole but sensitive to chloramphenicol ., This was consistent with GoldenGate assay results , which gave positive signals for the repC replication initiation gene of IncHI1 , resistance genes strAB , bla , sul1 , sul2 , dfrA7 , but no signal for sequences from the cat gene encoding chloramphenicol resistance ., A further 17 S . Typhi isolates were recorded as MDR according to their antimicrobial susceptibility pattern at the time of isolation , but did not test positive for IncHI1 plasmid loci ., This likely reflects loss of the IncHI1 plasmid in culture or storage between the time of isolation and DNA extraction ., The MDR status of the infecting S . Typhi isolate was not associated with fever clearance time ( p\u200a=\u200a0 . 3 , two-sided T-test ) or treatment failure ( p\u200a=\u200a0 . 18 , Chi2 test ) ., A total of 257 S . Typhi isolates were resistant to nalidixic acid ( Nal ) ., All of these isolates belonged to the H58 haplogroup ( Table 1 ) and all were susceptible to gatifloxacin , ciprofloxacin and ofloxacin according to current CLSI guidelines 17 ., S . Typhi haplotypes H58-C , H58-E1 and H58-E2 were uniformly resistant to Nal , with the exception of a single H58-C isolate which had an intermediate MIC of 28 µg/mL ( resistance defined as MIC ≥32 µg/mL ) ., The sequenced H58-E2 isolate AG3 harbours a mutation changing serine ( TCC ) to phenylalanine ( TTC ) at codon 83 in the gyrA gene ( GyrA-Ser83Phe ) 21 , which is known to confer resistance to Nal 26 ., In the present study we sequenced the gyrA gene in 223 of the Nal resistant isolates ( 87% ) and found the same GyrA-Ser83Phe amino acid substitution in all isolates tested ., Figure 3 shows the spatial distribution of the residences of 160 typhoid patients ( this information was not available for the remaining patients ) ., Of the patients admitted at An Giang Provincial Hospital and Dong Thap Provincial Hospital , sufficient address detail to allow for assignment of latitude and longitude was provided in 61% and 73% of cases , respectively ., This represents 50% and 20% of all blood culture confirmed typhoid fever patients at An Giang Provincial Hospital and Dong Thap Provincial Hospital , respectively , during 2004–2005 ., In An Giang , patients homes clustered around the An Giang Provincial Hospital , but also around the Sông H u branch of the Mekong river ( see Figure 3 ) ., Most S . Typhi isolated from patients living near this point in An Giang province were of the H58-E2 haplotype ( orange in Figure 3 ) , and this group demonstrated significant clustering using nearest-neighbour analysis ( n\u200a=\u200a57 , Z-score =\u200a−14 . 145 ) ., In contrast , S . Typhi of the H58-C haplotype were isolated relatively frequently in neighbouring provinces and had a more sporadic clustering pattern ( red in Figure 3 ) ., While isolates from An Giang Provincial Hospital are overrepresented in this spatial analysis , the apparent increase in typhoid density in An Giang is consistent with total Typhi isolation rates at the two hospitals during the study period ( 284 at An Giang Provincial Hospital and 90 at Dong Thap Provincial Hospital ) ., The temporal distribution of S . Typhi haplotypes over 2004 and 2005 is shown in Figure 4 ., Typhoid fever cases peaked just prior to the onset of the wet season in each year , as has been observed previously in this region 1 , 3 ( see monthly rainfall , solid line in Figure 4 ) ., In 2004 , H58-E2 and H58-C were both prevalent ( 62 C , red in Figure 4; 103 E2 , orange in Figure 4 ) , whereas few isolates of H58-E2 Typhi were observed during 2005 ( 55 C , 4 E2; see Figure 4 ) ., The decline of H58-E2 may be associated with selection for the IncHI1 MDR plasmid , which was much more common in H58-C ( Table 1 ) ., As Figure 4 highlights , the majority of isolates collected during the second season were MDR and carried the IncHI1 plasmid ST6 ., During 2004–2005 , typhoid in the Mekong river delta region of Vietnam was almost exclusively caused by a single Nal-resistant clonal complex of S . Typhi ., This reflects a higher level of clonality than observed in other localised S . Typhi populations studied to date , which may be indicative of higher transmission rates in this location ., The high level of Nal resistance and multidrug resistance , frequently in the same strains , is concerning and continues to pose problems for the successful treatment of typhoid fever .

Introduction, Materials and Methods, Results, Discussion

Typhoid fever remains a public health problem in Vietnam , with a significant burden in the Mekong River delta region ., Typhoid fever is caused by the bacterial pathogen Salmonella enterica serovar Typhi ( S . Typhi ) , which is frequently multidrug resistant with reduced susceptibility to fluoroquinolone-based drugs , the first choice for the treatment of typhoid fever ., We used a GoldenGate ( Illumina ) assay to type 1 , 500 single nucleotide polymorphisms ( SNPs ) and analyse the genetic variation of S . Typhi isolated from 267 typhoid fever patients in the Mekong delta region participating in a randomized trial conducted between 2004 and 2005 ., The population of S . Typhi circulating during the study was highly clonal , with 91% of isolates belonging to a single clonal complex of the S . Typhi H58 haplogroup ., The patterns of disease were consistent with the presence of an endemic haplotype H58-C and a localised outbreak of S . Typhi haplotype H58-E2 in 2004 ., H58-E2-associated typhoid fever cases exhibited evidence of significant geo-spatial clustering along the Sông H u branch of the Mekong River ., Multidrug resistance was common in the established clone H58-C but not in the outbreak clone H58-E2 , however all H58 S . Typhi were nalidixic acid resistant and carried a Ser83Phe amino acid substitution in the gyrA gene ., The H58 haplogroup dominates S . Typhi populations in other endemic areas , but the population described here was more homogeneous than previously examined populations , and the dominant clonal complex ( H58-C , -E1 , -E2 ) observed in this study has not been detected outside Vietnam ., IncHI1 plasmid-bearing S . Typhi H58-C was endemic during the study period whilst H58-E2 , which rarely carried the plasmid , was only transient , suggesting a selective advantage for the plasmid ., These data add insight into the outbreak dynamics and local molecular epidemiology of S . Typhi in southern Vietnam .

Typhoid fever remains a serious public health issue in some parts of Vietnam , including the Mekong delta region ., Typhoid is caused by the bacterium Salmonella Typhi , which is frequently multidrug resistant and shows reduced susceptibility to fluoroquinolone-based drugs ., We assayed single nucleotide variation in the genomes of S . Typhi organisms isolated from 267 patients with typhoid fever in the Mekong delta between 2004 and 2005 , and identified genetically distinct S . Typhi strains ., We also detected the presence of genes or mutations that confer drug resistance in those strains ., We found that the vast majority of typhoid cases were caused by one of two subgroups of H58 S . Typhi , referred to as H58-C and H58-E2 ., The H58-E2 group appeared to cause an outbreak in 2004 , affecting patients living in a small zone near the Mekong River ., The other group , H58-C , was present throughout the study period and affected patients living in a broader area of the Mekong River delta ., Most of the H58-C strains were resistant to multiple drugs and carried a plasmid encoding multiple resistance genes ., However very few H58-E2 strains were multidrug resistant , which may explain why the strain did not persist after the initial outbreak .

public health and epidemiology/infectious diseases, microbiology/microbial evolution and genomics, infectious diseases/antimicrobials and drug resistance

journal.pgen.1004314

Whole Exome Re-Sequencing Implicates CCDC38 and Cilia Structure and Function in Resistance to Smoking Related Airflow Obstruction

Chronic obstructive pulmonary disease ( COPD ) is a leading cause of global morbidity and mortality 1 and whilst smoking remains the single most important risk factor , it is also clear that COPD risk is heritable 2 ., The genetics underlying COPD are still not fully understood although genome-wide association studies have identified 26 genomic regions showing robust association with lung function 3–6 and , of these , 11 have also now shown association with airflow obstruction 7–9 ., However , the proportion of the variance accounted for by the 26 common genetic variants representing these regions remains modest ( ∼7 . 5% for the ratio of forced expired volume in 1 second ( FEV1 ) to forced vital capacity ( FVC ) ) 5 ., Although over a quarter of the population with a significant smoking history go on to develop COPD 10 , some individuals are observed to have preserved lung function as measured by a normal or high FEV1 despite many years of heavy smoking ., We hypothesised that these “resistant smokers” may harbour rare variants with large effect sizes which protect against lung function decline caused by smoking ., Identification of these variants , and the genes that harbour them , could provide further insight into the mechanisms underlying airflow obstruction ., We undertook whole exome re-sequencing of 100 heavy smokers ( >20 pack years of smoking ) who had healthy lung function when age , sex , height and amount smoked were taken into account ., We employed 3 complementary approaches to investigate the genetic architecture of the resistant smoker genotype ( Figure 1 ) ., Firstly , we screened these 100 “resistant smokers” for novel rare variants ( i . e . not previously identified and deposited in a public database ) with a putatively functional effect on protein product and tested for enrichment of these novel variants in functionally related genes and pathways ., Secondly , using a comparision with two independent control sets with exome re-sequencing data , we looked for signals of association with the resistant smoker phenotype for individual variants ( including variants of all minor allele frequencies ) ., Thirdly , we looked for association of the resistant smoker phenotype with the combined effects of multiple rare and common variants within genes ., We found the strongest evidence of association with resistance to smoking for a non-synonymous variant in CCDC38 , a gene encoding a coiled-coil domain protein with a role in motor activity , previously identified as showing an association with lung function ., We also show evidence of cytoplasmic expression of CCDC38 in bronchial columnar epithelial cells ., In addition , we found evidence for an enrichment of novel rare functional variants in resistant smokers in gene pathways related to cilia structure and function ., Given that abnormalities of ciliary function are already known to play a role in reduced mucociliary clearance in COPD sufferers and smokers , these data suggest that genetic factors may play a significant role in determining the ciliary response of the airway to inhaled tobacco smoke ., 100 individuals from the Gedling 11 , 12 and Nottingham Smokers cohorts with good lung function ( FEV1/FVC>0 . 7 and % predicted FEV1>80% ) when age , sex , height and smoking history ( >20 pack years ) were taken into account were selected as “resistant smoker” cases ., Characteristics of the 100 resistant smoker case samples are shown in Table S1A and Figure S1 ., Exome re-sequencing and alignment was undertaken as described in the methods ., Two independent control sets were used; the robustness of findings using the primary control set ( n\u200a=\u200a166 ) were further assessed using a secondary control set ( n\u200a=\u200a230 ) ., We searched for novel variants among the resistant smokers , i . e . genetic variants which were not observed in either control set and which were not documented in public databases ., Bioinformatic tools allow for scoring of likely functional impact , including whether a variant is likely to be “deleterious”; here we use the term “putatively functional” since some variants which have a deleterious effect on the function of a given gene may result in a protective phenotype ., A total of 24 , 098 variants which were not already in databases of known variants or within segmental duplications were identified with high confidence using two independent calling algorithms ., A total of 6587 coding SNPs were scored using CAROL ( including non-synonymous , loss/gain of stop codon , synonymous and splice site/UTR variants ) and 1722 were predicted as being putatively functional ( CAROL score>0 . 98 ) and were within 1533 genes ., 16 of these 1533 genes each contained three novel putatively functional variants ( Table S2 ) ( no gene contained more than three such variants ) ., GBF1 contained three novel putatively functional variants of which one , chr10:104117872 , was identified in two case samples ., A further 157 genes each contained 2 novel putatively functional variants and the remaining 1360 genes contained one novel putatively functional variant ., In the resistant smokers , there was no overall enrichment or depletion of novel putatively functional variants among the 26 regions reported to be associated with lung function 5 , ( 16 were observed , the same number would have been predicted based on the sequence length of exons ) and no novel putatively functional variants were identified within the CHRNA3/5 region which has been previously associated with smoking 13 and airflow obstruction 9 ., Eight of the 1722 novel variants predicted to be putatively functional were identified in >1 case sample ., These are listed in Table S3 ., ATAD3C contained a novel putatively functional variant for which six case samples were heterozygous , SHANK2 contained a novel putatively functional variant for which three cases were heterozygous , and the remaining six genes each contained such a variant for which two cases were heterozygous ., One hundred and ninety two Gene Ontology ( GO ) terms reached nominal significance for the set of 1533 genes containing novel putatively functional variants in resistant smoker cases ., Of these , 22 high level GO terms were significant after Bonferroni correction for multiple testing and are listed in Table 1 14 ., The most significant GO term was the molecular function term “motor activity” which describes molecules involved in catalysis of movement along a polymeric molecule such as a microfilament or microtubule , coupled to the hydrolysis of a nucleoside triphosphate ., Other related GO terms also feature amongst the significant signals from this analysis ( including “cytoskeleton” , “microtubule motor activity” , “myosin complex” , “axoneme” , “cilium” and “cilium part” ) ( Table 1 and Table S4 ) ., We tested for association of known and novel exonic variants with the resistant smoker phenotype ., After exclusion of variants which were missing in >5% of either cases or controls , 269 , 822 ( of which 215 , 747 were listed in dbSNP137 ) variants remained ., Of the 269 , 822 variants , 94 , 138 were exonic and included in further analyses ., Similar distributions of variants across the minor allele frequency spectrum were observed for the cases , primary , and secondary controls ( results not shown ) ., After testing for association with resistant smoker status using primary controls , no SNPs reached genome-wide significance ( P<5×10−7 , based on Bonferroni correction for 94 , 138 tests ) ., Substantial under-inflation of the test statistics was seen ( lambda\u200a=\u200a0 . 6 ) ( Figure 2A ) , possibly due to the large number of rare variants ( lambda\u200a=\u200a0 . 92 if only variants with MAF>5% n\u200a=\u200a25 , 646 were considered , Figure 2B ) ., Twenty exonic SNPs showed nominal evidence of association with P<10−3 ( Table 2 ) ., The strongest signal from a non-synonymous SNP was within a region previously identified as being associated with lung function 5 ., The non-synonymous SNP in CCDC38 ( rs10859974 , OR\u200a=\u200a2 . 36 , P\u200a=\u200a2 . 34×10−4 ) is 17 . 43 kb away from , but statistically independent of , rs1036429 ( intronic , r2\u200a=\u200a0 . 064 ) which has previously shown genome-wide significant association with FEV1/FVC 5 ., SNP rs10859974 itself has shown weak evidence of association with FEV1/FVC ( P\u200a=\u200a0 . 032 ) 5 ., This SNP is predicted to cause a methionine to valine substitution at protein position 227; the valine allele is predicted to be protective ., Investigations into CCDC38 expression in bronchial tissue via immunohistochemistry identified moderate staining of CCDC38 in the cytoplasm of columnar epithelial cells , with weak staining in the sub-epithelial layer ( Figure 3 ) ., We found no evidence that rs10859974 or any of its proxy SNPs ( r2>0 . 3 ) were lung eQTLs for CCDC38 itself , although rs11108320 which is intronic in CCDC38 and in strong LD with rs10859974 ( r2\u200a=\u200a1 ) is an eQTL for nearby gene NTN4 ( significant at 10% False Discovery Rate ( FDR ) threshold ) ., Many additional SNPs located near or within CCDC38 and SNRPF were eQTLs for NTN4 ( Table S5 ) ., Nearby CCDC38 intronic SNPs in weaker LD ( r2\u200a=\u200a0 . 3 ) with rs10859974 were eQTLs for SNRPF ( Table S5 ) ., The strongest signal of association in the single-variant analysis was from a synonymous SNP , rs1287467 , in SH3BP5 ( OR\u200a=\u200a0 . 47 , P\u200a=\u200a1 . 47×10−4 ) ( Table 2 ) ., A SNP downstream of SH3BP5 ( rs1318937 , 1000G CEU MAF\u200a=\u200a0 . 108 , 16 kb from rs1287467 , r2\u200a=\u200a0 . 018 ) has shown evidence of association with alcohol dependence and alcohol and nicotine co-dependence 15 ., Synonymous SNP rs2303296 in ITSN2 was the second strongest signal of association ( OR\u200a=\u200a0 . 45 , P\u200a=\u200a2 . 31×10−4 ) and had previously shown weak evidence of association with FEV1 ( P\u200a=\u200a0 . 02 ) 5 but was not near to any previously identified genome-wide significant associations with lung function and has not shown evidence of association with COPD 9 ., Another SNP in ITSN2 , rs6707600 ( intronic , 1000 G CEU MAF\u200a=\u200a0 . 017 , 89 Kb from rs2303296 , r2\u200a=\u200a0 . 02 ) , has shown some evidence of association with antipsychotic response in schizophrenia patients 16 ., The second strongest signal from a non-synonymous SNP was rs4850 in UQCRC2 ( OR\u200a=\u200a4 . 87 , P\u200a=\u200a2 . 4×10−4 ) ., There were no nearby associations reported with any other trait for this gene ., The third strongest signal from a non-synonymous SNP was rs2297950 ( OR\u200a=\u200a0 . 51 , P\u200a=\u200a6 . 65×10−4 ) in CHIT1 which encodes chitinase 1 ( Chit1 ) ., The chitinase pathway has been implicated in asthma and lung function 17 and lung function decline in COPD patients 18 ., Chit1 expression in mice has been shown to be correlated with severity of bleomycin-induced pulmonary fibrosis ( with overexpression leading to increased severity and Chit1−/− mice exhibiting reduced pulmonary fibrosis ) 19 ., A non-synonymous SNP in LOXL3 , rs17010021 , was the only SNP with an association P<10−3 regardless of whether the primary or the secondary controls were used ( Table S6 ) ., This variant had a minor allele frequency of 0 . 048 and 0 . 061 in the primary and secondary control sets respectively , but the minor allele was not observed in any of the resistant smoker cases ., Synonymous SNP rs1051730 , in CHRNA3 ( 15q25 . 1 ) , has previously shown very strong evidence of association with smoking behaviour ( particularly cigarettes per day ) 13 , 20 , 21 ., This SNP showed weak evidence of association with the resistant smoker phenotype in our study ( P\u200a=\u200a0 . 03 when the secondary control set was used and P\u200a=\u200a0 . 06 when primary control set was used ) ., Association results for SNPs within 500 Kb of rs1051730 are in Table S7 ., No nominally significant enrichment of association signals in known pathways was identified in the exome-wide results of the single-variant analysis using MAGENTA 22 ., SKAT 23 and AMELIA 24 analyses were undertaken to assess whether multiple variants within a gene collectively showed evidence of association; these tests are agnostic to whether a given variant is previously known ., Quantile-Quantile plots for SKAT and AMELIA analyses are shown in Figure 4 ., Genes with nominally significant association ( P<10−3 ) for SKAT or AMELIA analysis using the primary controls are shown in Table 3 ( results of SKAT and AMELIA analyses using the secondary controls are shown in Table S8 ) ., No genes showed significant association after Bonferroni correction for multiple testing ( P<0 . 05/18000\u200a=\u200a2 . 8×10−6 ) for either analysis ( Table 3 and Table S8 ) ., Since the genes are likely to be correlated ( through LD structure or overlapping reading frames ) , SKAT provides a resampling function to control Family Wise Error Rate ( FWER ) ., No genes were significant after controlling FWER\u200a=\u200a0 . 05 ., None of the genes in Table 3 and Table S8 were within any of the 26 lung function associated regions 3–5 , the CHRNA3/5 smoking-associated region 13 or SERPINA1 ( mutations in which are known to cause alpha-1-antitrypsin deficiency ) 25 ., We also checked overlap between the gene-based association testing and single-variant tests ., A signal in TMEM252 ( which showed P<10−3 in the SKAT analysis regardless of which control set was used ) was driven by rs117451470 , a non-synonymous SNP , which had P\u200a=\u200a2 . 2×10−3 in the single-variant association analysis ( the other SNP in TMEM252 , a singleton novel synonymous variant , had P\u200a=\u200a0 . 38 in the single-variant analysis ) ., Signals in UQCRC2 ( strongest signal using SKAT ) , SPATA3D1 , PGAP3 and ADCK2 were also driven by variants with P<10−3 in the single-variant analysis ., Signals from TMX3 , IMPG2 and TCOF1 were not driven by single-variant signals ( all SNPs within these genes had P>0 . 01 in the single variant analysis ) ., IMPG2 was the strongest signal from the AMELIA analysis and all 8 SNPs within IMPG2 had no evidence of association in the single-variant analysis ( P>\u200a=\u200a0 . 18 ) ., We tested for enrichment of GO terms within the set of genes showing association with P<0 . 01 in the SKAT analyses ., Ten high level GO terms reached nominal significance ( P<0 . 05 ) for the set of 150 genes identified using SKAT but none were significant after Bonferroni correction for multiple testing 14 ., Understanding why some heavy smokers seem to show resistance to the detrimental effects of cigarette smoke on lung function should provide further insight into the genetics of lung function and COPD ., We undertook pathway enrichment , single-variant association testing and gene-based association testing analyses on whole exome re-sequencing data from a set of resistant smokers ., Although no individual SNP achieved genome-wide statistical significance ( P<5×10−7 ) , our strongest association signal for a non-synonymous SNP was in CCDC38; a gene which has previously shown strong and robust evidence of association with lung function 5 ., The intronic SNP previously shown to be associated with lung function ( FEV1/FVC ) and the non-synonymous SNP showing nominally significant association with the resistant smoker phenotype in this study are located close together ( 17 . 4 kb apart ) but are not well correlated ( the non-synonymous SNP has previously shown nominally significant evidence P<0 . 05 of association with FEV1/FVC ) ., A conditional analysis of these two SNPs was consistent with no statistical correlation between these signals ., Although the function of CCDC38 is not yet well understood , members of the coiled-coil domain protein family are known to have a role in cell motor activity ( e . g . myosin ) 26 and cilia assembly 27 , 28 ., Expression of CCDC38 has been identified in the human bronchi of two subjects , with strong cytoplasmic staining in the epithelium and moderate staining in the airway smooth muscle ( Human Protein Atlas http://www . proteinatlas . org 29: ENSG00000165972 ) ., We experimentally confirmed these findings using immunohistochemistry on lung sections ., We observed moderate cytoplasmic CCDC38 staining in bronchial columnar epithelial cells and some potential airway smooth muscle staining ., There is no evidence that SNP rs10859974 is an eQTL for CCDC38 itself , although proxies for rs10859974 are eQTLs for a nearby downstream gene , NTN4 , encoding Netrin-4 which may play a role in embryonic lung development 30 ., Gene Ontology terms shown to be significantly enriched among the novel putatively functional variants identified only in the resistant smokers also pointed to pathways relating to motor activity and the cytoskeleton , including cilia ., Another locus showing association with lung function ( 1p36 . 13 , 5 ) also contains a gene encoding a component of cilia ( CROCC which encodes rootletin , another coiled-coil domain protein ) and Crocc-null mice have been shown to have impaired cilia with pathogenic consequences to the airways 31 ., The enrichment of genes involved in cilia function amongst the results of our analyses supports the importance of cilia function in lung health ., Cilia abnormalities are known to be associated with smoking 32 , 33 , asthma 34 , and play a role in COPD 35 where reduced cilia function leads to reduced mucus clearance of the airways ., Improving mucociliary clearance is one of the aims of drug therapy for chronic bronchitis in COPD patients ( reviewed in 36 ) ., Impaired cilia function is known to cause a wide range of diseases ( collectively known as ciliopathies ) many of which include pulmonary symptoms 37 ., Primary Ciliary Dyskinesia ( PCD ) is a rare genetic disorder where respiratory tract cilia function is impaired leading to reduced ( or absent ) mucus clearance ., Mutations in genes which encode components of the cilia have been found to cause several forms of PCD and include the dynein , axonemal heavy chain encoding genes DNAH11 38 , 39 and DNAH5 40 within which resistant smoker-specific novel putatively functional variants were identified in this study ( 2 such variants were discovered in DNAH11 ) ., Whilst PCD affects resistance to infection and results in bronchiectasis , abnormal lung function can manifest early in life and progressive airflow obstruction has been observed in later life , although aggressive treatment may prevent the latter 41 ., Retinitis pigmentosa is a feature of several ciliopathies , including some with pulmonary involvement ( for example , Alstrom Syndrome ) ., Low frequency variants in IMPG2 ( interphotoreceptor matrix proteoglycan 2 ) collectively showed strong evidence of association ( using AMELIA ) ., Variants in IMPG2 are associated with a form of retinitis pigmentosa 42 ., Another retinitis pigmentosa gene , RP1 , was amongst the 16 genes containing 3 novel putatively functional variants in the resistant smokers ., RP1 encodes part of the photoreceptor axoneme 43 , a central component of cilia ., A recent study identified modulators of ciliogenesis using a high throughput assay of in vitro RNA interference of 7 , 784 genes in human retinal pigmented epithelial cells ( htRPE ) and identified 36 positive modulators and 13 negative modulators of ciliogenesis 44 ., These modulators included many genes which did not encode structural cilia proteins and thus were not obvious candidates for a role in cilia function ., None of the genes highlighted by the single-variant or gene-based analyses were confirmed as modulators of ciliogenesis although ITSN2 , which contained one of the top signals in our single-variant analysis , was included in the screen and showed suggestive evidence of a positive role in ciliogenesis but this was not confirmed in a second screen ., Two of the genes found to harbour a novel putatively functional variant in the resistant smokers were identified as positive modulators of ciliogenesis: GSN ( gelsolin ) which is a known cilia gene with a role in actin filament organisation and AGTPBP1 ( ATP/GTP binding protein 1 ) which has a role in tubulin modification ., Collectively , our data show an enrichment of novel putatively functional variants in genes related to cilia structure and function in resistant smokers ., Association between smoking and shorter cilia has been reported 32 ., The largest genome-wide association with lung function to date supports the notion that the majority of associated variants , including those associated with COPD risk , affect lung function development rather than decline in lung function in adults 5 ., If confirmed in other studies , it would be interesting to assess whether genetic influences on the function of cilia primarily affect growth or whether these affect more directly the extent of damage caused by tobacco smoke ., Very large GWAS have identified up to hundreds of common variants each with a modest effect on a variety of phenotypes ., However , collectively , these still only explain a very modest proportion of the additive polygenic variance ., It has been widely hypothesised that rare variation may account for some of this missing variance 45 ., Commercially available SNP arrays have tended to include mostly variants with minor allele frequencies upwards of 5% and rare variants have not been reliably imputed from these ., Re-sequencing approaches provide the most accurate platform for the study of exome-wide and genome-wide rare variation ., However , there is increasing evidence that rare variants may not account for the missing heritability for all traits 46 ., Our study did not find evidence for any individual rare variants with large effects in any of the known lung function associated loci or elsewhere in the exome ( albeit in a modest overall sample size ) , although we did identify significant enrichment of novel rare variants in sets of genes with known functions in pathways which are known to have a role in lung health ., For the single variant analyses , we used Fishers Exact Test ., Whilst this is an appropriate test to use for small cell counts ( for example , where minor allele counts are low ) , alternatives have been recently proposed including the Firth test , and although the optimal approach in the size of study we undertook is not clear from the comparisons shown to date , the Fishers Exact Test can be more conservative than the Firth test and this may have had some impact on the power of the study 47 ., Methods for the analysis of rare variant data are continuing to evolve ., Although this is the first exome re-sequencing study of resistance to airways obstruction among heavy smokers , our study does have potential limitations ., Sample size was limited both by availability of individuals with such an extreme phenotype as that we were able to study , and also by current sequencing costs ., We were able to utilise re-sequencing data available to the scientific community as control data and therefore maximise the discovery potential of our resources by re-sequencing to a sufficient sequencing read depth for confident rare variant calling ., By doing so , and selecting an extreme phenotype group from our sampling frame , we adopted a suitable design to test whether there was enrichment of rare variants of large effect in resistant smokers ., The same limitations also impact on the availability of suitable replication studies ., In particular , it would have been desirable to undertake replication to support the statistically significant findings of the pathway analysis ., However , in the absence of a suitable replication resource , the prior evidence for the role of cilia in lung health does lend support to our findings ., As it becomes possible to sample and re-sequence from very large biobanks it should become possible to circumvent these issues in years to come , particularly if the cost of sequencing falls ., As limited information was available on smoking status among the controls , we did not restrict controls to heavy smokers and there is therefore potential for genetic associations to be driven via an effect on smoking behaviour ., Nevertheless , our design is also consistent with the detection of association due to primary effect on airways and previous genome-wide association studies of lung function not fully adjusted for smoking have detected loci associated with lung function and COPD which were not associated with smoking behaviour 4 , 5 ., We saw only a weak association with variants at the CHRNA3/CHRNA5 locus ( the locus at which variants have shown the strongest effect with smoking behaviour 13 , 20 , 21 ) ., Misclassification impacts on power; we would have underestimated the power to detect SNP and gene-based associations if the prevalence of resistance to airways obstruction among heavy smokers was greater than we assumed ., In a cross-sectional study of this kind , survivor bias could occur if genetic variants influencing survival were under-represented or over-represented in the resistant smokers , but as the mean age of the resistant smokers was 56 . 4 , any survivor bias , if present , is unlikely to have had a major impact ., Finally , although we would expect the allele frequencies of the control sets we used to be representative of a general population control set across the vast majority of the genome , biases could potentially be introduced for any genetic variants related to the ascertainment strategy of the control sets ., For the main findings we report in this paper , we also present allele frequencies from a public database ( 1000 Genomes Project ) ; any such bias does not explain our main findings ., In the first deep whole exome re-sequencing study of the resistant smoker phenotype , we have identified an association signal in a region that has already shown robust association with lung function ( CCDC38 ) and demonstrate significant enrichment of novel putatively functional variants in genes related to cilia structure ., These findings provide insights into the mechanisms underlying preserved lung function in heavy smokers and may reveal mechanisms shared with COPD aetiology ., The Gedling study was approved by the Nottingham City Hospital and Nottingham University Ethics committees ( MREC/99/4/01 ) and written informed consent for genetic study was obtained from participants ., The Nottingham Smokers study was approved by Nottingham University Medical School Ethical Committee ( GM129901/ ) and written informed consent for genetic study was obtained from participants ., The Edinburgh MR-psychosis sample set was compliant with the UK10K Ethical Governance Framework ( http://www . uk10k . org/ethics . html ) and no restrictions were placed on the use of the genetic data by the scientific community ., For TwinsUK , ethics committee approval was obtained from Guys and St Thomas Hospital research ethics committee ., Tissue for immunohistochemistry was from Nottingham Health Science Biobank ( Nottingham , UK ) with the required ethical approval ( 08/H0407/1 ) ., For lung eQTL datasets: At Laval , lung specimens were collected from patients undergoing lung cancer surgery and stored at the “Institut universitaire de cardiologie et de pneumologie de Québec” ( IUCPQ ) site of the Respiratory Health Network Tissue Bank of the “Fonds de recherche du Québec – Santé” ( www . tissuebank . ca ) ., Written informed consent was obtained from all subjects and the study was approved by the IUCPQ ethics committee ., At Groningen , lung specimens were provided by the local tissue bank of the Department of Pathology and the study protocol was consistent with the Research Code of the University Medical Center Groningen and Dutch national ethical and professional guidelines ( “Code of conduct; Dutch federation of biomedical scientific societies”; http://www . federa . org ) ., At Vancouver , the lung specimens were provided by the James Hogg Research Center Biobank at St Pauls Hospital and subjects provided written informed consent ., The study was approved by the ethics committees at the UBC-Providence Health Care Research Institute Ethics Board ., 100 individuals with prolonged exposure to tobacco smoke and unusually good lung function ( resistant smokers ) were selected from the Gedling and Nottingham Smokers studies , described below ., The Gedling cohort is a general population sample recruited in Nottingham in 1991 ( 18 to 70 years of age , n\u200a=\u200a2 , 633 ) 11 and individuals were then followed-up in 2000 ( n\u200a=\u200a1346 ) when blood samples were taken for DNA extraction , and FEV1 and FVC were measured using a calibrated dry bellows spirometer ( Vitalograph , Buckingham , UK ) , recording the best of three satisfactory attempts 12 ., The Nottingham Smokers cohort is an ongoing collection in Nottingham using the following criteria; European ancestry , >40 years of age and smoking history of >10 pack years ( currently n\u200a=\u200a538 ) ., Lung function measurements ( FEV1 and FVC ) were recorded at enrolment using a MicroLab ML3500 spirometer ( Micro Medical Ltd , UK ) recording the best of three satisfactory attempts ., Our inclusion criteria was; aged over 40 with more than 20 pack years of smoking and no known history of asthma ., A total of 184 samples were eligible for this project after further exclusion of individuals with either FEV1 , FVC or FEV1/FVC less than the Lower Limit Normal ( LLN ) ( based on age , sex and height ) ., We calculated residuals after adjusting % predicted FEV1 for pack years of smoking and selected the 100 samples with the highest residuals for exome re-sequencing ( Figure S1 ) ., Primary controls were from the Edinburgh MR-psychosis set ( n\u200a=\u200a166 ) of the UK10K project ( http://www . uk10k . org/ ) and consisted of subjects with schizophrenia , autism or other psychoses , and with mental retardation ., No additional phenotype information was available for the primary controls ., The TwinsUK secondary control samples ( n\u200a=\u200a230 ) were all unrelated females selected from the high and low ends of the pain sensitivity distribution of 2500 volunteers from TwinsUK 48 , 49 ., Characteristics of the secondary controls are given in Table S1B ( note that phenotype information was only available for a subset of the samples ) ., These secondary controls were not included in the main analyses due to the difference in exome coverage ., Further phenotype information was not available for either control sample set ., For the 100 resistant smoker case samples , DNA was extracted from whole blood and the Agilent SureSelect All Exon 50 Mb kit was used for enrichment ., The 100 resistant smoker samples were individually indexed and grouped into 20 pools of 5 samples ., Each pool was sequenced in one lane ( 20 sequencing lanes in total ) using an Illumina HiSeq2000 ., Sequences were generated as 100 bp paired-end reads ., Exome-wide coverage of 97 out of 100 samples was >20 ( Figure S2 ) ., Three samples had mean sequence depth coverage <20 , of these , one appeared to have had poor enrichment ( high number of off-target reads ) , one had a low overall sequence yield and high number of duplicate reads and one had a high number of duplicate reads ( but good sequence yield ) ., To preserve power , and because there was no evidence that the sequence data quality for these samples was lower than for the other samples , these 3 samples were not excluded from further analyses ., A total of 166 exomes from the Edinburgh MR-psychosis study: a subset of the neurodevelopmental disease group of the UK10K project ( http://www . uk10k . org/ ) , were used as primary controls ., These were enriched using the Agilent SureSelect All Exon 50 Mb kit and sequenced using an Illumina HiSeq2000 to a mean coverage depth of ∼70x ( 75 bp paired-end reads ) ., The sequencing of the secondary controls from the TwinsUK pain study has been described elsewhere 49 ., In brief , raw sequence data was available for 230 exomes which had been enriched using the NimbleGen EZ v2 ( 44 Mb ) array and sequenced on an Illumina HiSeq2000 to a

Introduction, Results, Discussion, Materials and Methods

Chronic obstructive pulmonary disease ( COPD ) is a leading cause of global morbidity and mortality and , whilst smoking remains the single most important risk factor , COPD risk is heritable ., Of 26 independent genomic regions showing association with lung function in genome-wide association studies , eleven have been reported to show association with airflow obstruction ., Although the main risk factor for COPD is smoking , some individuals are observed to have a high forced expired volume in 1 second ( FEV1 ) despite many years of heavy smoking ., We hypothesised that these “resistant smokers” may harbour variants which protect against lung function decline caused by smoking and provide insight into the genetic determinants of lung health ., We undertook whole exome re-sequencing of 100 heavy smokers who had healthy lung function given their age , sex , height and smoking history and applied three complementary approaches to explore the genetic architecture of smoking resistance ., Firstly , we identified novel functional variants in the “resistant smokers” and looked for enrichment of these novel variants within biological pathways ., Secondly , we undertook association testing of all exonic variants individually with two independent control sets ., Thirdly , we undertook gene-based association testing of all exonic variants ., Our strongest signal of association with smoking resistance for a non-synonymous SNP was for rs10859974 ( P\u200a=\u200a2 . 34×10−4 ) in CCDC38 , a gene which has previously been reported to show association with FEV1/FVC , and we demonstrate moderate expression of CCDC38 in bronchial epithelial cells ., We identified an enrichment of novel putatively functional variants in genes related to cilia structure and function in resistant smokers ., Ciliary function abnormalities are known to be associated with both smoking and reduced mucociliary clearance in patients with COPD ., We suggest that genetic influences on the development or function of cilia in the bronchial epithelium may affect growth of cilia or the extent of damage caused by tobacco smoke .

Very large genome-wide association studies in general population cohorts have successfully identified at least 26 genes or gene regions associated with lung function and a number of these also show association with chronic obstructive pulmonary disease ( COPD ) ., However , these findings explain a small proportion of the heritability of lung function ., Although the main risk factor for COPD is smoking , some individuals have normal or good lung function despite many years of heavy smoking ., We hypothesised that studying these individuals might tell us more about the genetics of lung health ., Re-sequencing of exomes , where all of the variation in the protein-coding portion of the genome can be measured , is a recent approach for the study of low frequency and rare variants ., We undertook re-sequencing of the exomes of “resistant smokers” and used publicly available exome data for comparisons ., Our findings implicate CCDC38 , a gene which has previously shown association with lung function in the general population , and genes involved in cilia structure and lung function as having a role in resistance to smoking .

genome-wide association studies, chronic obstructive pulmonary disease, psychology, medicine and health sciences, addiction, genetic association studies, genome analysis, recreational drug addiction, genetics, biology and life sciences, computational biology, drug addiction, genetics of disease, pulmonology, human genetics

journal.pcbi.1004113

HPV Clearance and the Neglected Role of Stochasticity

Infection with the human papillomavirus ( HPV ) is responsible for a large fraction of anogenital and oropharyngeal cancers in both women and men ., Over 90% of cervical cancers are caused by HPV infections , and up to 60% of squamous cell carcinomas of the vulva , vagina , anus and penis are associated with high-risk types of HPV 1 ., More recently , it has been shown that infection with HPV also plays a critical role in the genesis of certain head and neck cancers , particularly in cancers of oropharynx and base of tongue 2 ., The incidence of these cancers in men has been increasing over the past decade , suggesting the emergence of a virus-related cancer epidemic 3 ., Even though the lifetime risk of HPV infections is as high as 80% 4 , most individuals clear the virus within 1–2 years 5 ., However , if infection with a high-risk type of HPV persists , the viral genes can interfere with the cellular control mechanisms and trigger neoplastic changes , which can eventually develop into an invasive carcinoma 6 ., To date , several aspects of the HPV infection dynamics remain poorly understood 7 , 8 ., In particular , the mechanisms of virus clearance are controversial 8 ., Clearance of HPV infection is usually attributed to an effective immune response , and the observation of longer clearance times in immunocompromised individuals further corroborates this assumption 9 ., On the other hand , the fact that development of antibodies preventing future re-infection after clearing of the virus ( known as seroconversion ) occurs only partially 10–14 suggests that mechanisms other than an effective immune response may contribute to viral clearance ., One potential contributor in the clearing of HPV that has received little attention is chance itself , or more precisely , the stochasticity of the stem cell dynamics in the infected epithelia ., Across different organs ( both anogenital and oropharyngeal ) , oncogenic types of HPV preferentially infect areas of stratified squamous epithelium ( SSE ) , and these SSE are not just a static backdrop to the unfolding infection process 2 , 15 ., They have a relatively fast turnover rate and the entire thickness of the epithelium is renewed every few weeks ., During the renewal process , stem cell-like progenitor cells ( hereafter denoted as S cells ) in the lowest layer of the tissue ( the basal layer ) produce commited daughter cells ( denoted as D cells ) that differentiate and move upwards into the intermediate and superficial layers , and eventually get sloughed off into the lumen 15 , see Fig . 1 ., The critical role of the dynamic differentiation and maturation process in the viral life cycle is well established 16 ., However , the hypothesis that stochastic dynamics in the basal layer could contribute significantly to the clearing of new infections has not been addressed elsewhere ., Until recently , the driving cellular processes in the basal layer were only poorly understood , but novel lineage tracing techniques have provided valuable insight into the stochastic dynamics of basal cells 17 ., Several mouse studies have used fluorescent labeling to observe lineage dynamics over time , and have concluded that while S cell division is prominently asymmetric ( yielding one S and one D cell ) , a small fraction of S cell divisions are symmetric , yielding either two stem cells or two differentiated daughter cells 18 , 19 ., Considering that HPV infections start with a small number of infected basal cells in the SSE 16 , it seems plausible that these stochastic division patterns in basal cells may have an impact on the persistence properties of the infection ., To investigate the relevance of cellular proliferation patterns and tissue homeostasis on HPV infection dynamics , we develop in this study a stochastic model of HPV infection in the SSE ., By explicitly accounting for the stochasticity in stem cell proliferation , as well as cytotoxic T-cell mediated elimination of infected basal cells , we investigate the potential role of chance in the viral clearing process ., Combining the model with a longitudinal data set of cervical HPV infections , we provide evidence for the critical role of stochasticity in HPV clearance ., Across affected anogenital and oropharyngeal sites , the dynamics of HPV infections are similar in nature ., There is a large overlap among HPV types found in lesions of different sites , and HPV-16 is the most common type found in all HPV-related invasive cancers 20 ., In addition , the viral replication strategy is essentially the same across affected sites 21 , 22 ., On the other hand , there are some organ-specific differences with respect to the biology of the affected stratified squamous epithelia ., In fact , cervical , anal and oropharyngeal infections are usually restricted to a confined metaplastic transformation zone that separates columnar and squamous regions of the epithelium , whereas infections of e . g . the vulva , vagina and penis do not take place in such a transformation zone 23 , 24 ., Nevertheless , the bottom-up renewal dynamics ( as explained below ) of the affected epithelia are very similar , and the parametric model developed here can be applied to different tissue types by virtue of adjusting the relevant parameters , such as density of stem cells in the basal layer and regeneration time of the epithelium ., The first objective was to combine the model introduced in Methods with the REACH data set to obtain estimates of the proliferation dynamics , the immune capacity , and the number of initially infected basal cells ., For this purpose , we made the assumption of a well-mixed basal layer: upon removal of an infected cell , division of an S cell occurs with probability pS = nS/ ( nS + nS* ) , and division of an S* cell with probability pS* = nS*/ ( nS + nS* ) ., In other words , we assumed that the spatial arrangement of cells in the 2D basal layer can be ignored ( the opposite end of the spectrum—a spatially clustered population of infected cells—is discussed below ) ., Since the relative size of the infected population compared to the entire basal layer is small throughout the infection , nS ≫ nS* , we can approximate pS ≈ 1 and pS* ≈ 0 . As outlined in section 2 in S1 Text , it follows that the S* cell dynamics reduce to a subcritical branching process ,, S * → S * + S * at rate λ r , ∅ at rate λ r + μ ., ( 5 ), The probability of survival to time t for this process is , according to results in 32 ,, ℙ n S * ( t ) > 0 = 1 1 + λ r + μ μ e μ t - 1 ., ( 6 ), In particular , addition of the immune capacity transforms the ∼ 1/t decay in ( 3 ) into an exponential decay ., Next , we used the longitudinal HPV data from the REACH study to infer the model parameters via maximum likelihood estimation ( MLE ) ., Thereby , we faced the issue of non-identifiability of the model , a common problem in statistical inference ., To understand where these issues arise , we first consider the probability density function f for the persistence time of the infection ( see section 3 in S1 Text for its derivation ), f ( t ) = n 0 ( λ r ) n 0 t n 0 - 1 1 + λ r t n 0 + 1 , μ = 0 , n 0 A n 0 μ e μ t e μ t - 1 n 0 - 1 1 + A e μ t - 1 n 0 + 1 , μ > 0 , ( 7 ), where A ≡ ( λr + μ ) /μ , and n0 is the initial number of infected stem cells ., From ( 7 ) we see that the values of λ and r cannot by inferred individually , and the best we can do is infer their product , α ≡ λr ., Even though there are no further apparent identifiability issues , we found that for n0 large enough , the density ( 7 ) only depends on the ratio α/n0 ( see section 4 in S1 Text ) ., As a consequence , α and n0 cannot be inferred individually , and we perform the inference over μ and n0 for fixed values of α , across a prior range of biologically meaningful values α ∈ 0 . 01 , 0 . 25 d−1 ( see section 5 in S1 Text for a justification of this range ) ., In addition to the identifiability issues , the MLE required the derivation of a non-standard likelihood function that takes into account the different combinations of data types: infections were either present at the time of the first visit ( prevalent infections ) , or they were initiated after the first visit ( incident infections ) ; some individuals were lost to follow-up before clearing the virus ( right-censoring ) , and both the time of initiation and the time of clearance were only determined up to the between-visit intervals ( interval-censoring ) ., The derivation of the corresponding likelihood function is found in section 3 in S1 Text ., A final comment regarding parameter inference concerns the interpretation of negative test results ., In fact , it has been shown that longitudinal HPV studies bear a significant amount of misclassifications due to short-term variation 37 , and that apparently cleared infections can reappear after variable amounts of time 38 , 39 ., The time before reappearance of seemingly cleared infections could be interpreted as a latency period during which the infection temporarily regresses to subdetection levels ., However , molecular evidence for this latency mechanism has so far only been established in animal models 40 ., Therefore , we decided to interpret the first of two consecutive negative test results as the time of clearance of the infection ., The inference results are summarized in Fig . 3A-B ., In what follows , the maximum likelihood estimates are denoted by a hat ( ^ ) on the parameter name , and subscripts ( − ) and ( + ) are used to refer to the HIV-negative and HIV-positive cohorts , respectively ., As explained above , the number of initially infected cells n0 is a linear function of α , which varies over the prior range 0 . 01 , 0 . 25 ., The inferred ranges for the initial number of infected cells are n̂0 , − ( α ) ∈ 5 , 80 in the HIV-negative cohort , and n̂0 , + ( α ) ∈ 5 , 120 in the HIV-positive cohort , see Fig . 3A ., Across the prior range of α , the inferred number of initially infected cells is slightly higher ( but of the same order of magnitude ) in HIV-positive compared to HIV-negative individuals: n̂0 , + ( α ) >n̂0 , − ( α ) , for all α ., To our knowledge , there is no experimental data that would allow us to assess the validity of these model predictions ., Regarding the immune capacity μ , we find a stark difference between the cohorts: the estimated capacity μ̂− in the HIV-negative cohort ( μ̂−=1 . 4⋅10−3d−1 ) is two orders of magnitude larger than the estimated capacity μ̂+ in the HIV-positive cohort ( μ̂+=3⋅10−3d−1 ) , see Fig . 3B ., In particular , the estimates μ̂+ and μ̂− are constant over the prior range of α ., Using the inferred parameter values μ̂+ and μ̂− for the immune capacity , and the inferred ranges n̂0 , − ( α ) ∈ and n̂0 , + ( α ) ∈ for the number of initial cells , we then derived the parametric clearance time distributions according to ( 7 ) , see Fig . 3C ., Since the clearance time distributions were found to be insensitive to α over the prior range ( see section 6 in Text SI ) , the distribution in Fig . 3C is only shown for an intermediate value of α = 0 . 14 ., Due to the reduced immune capacity in the HIV-positive cohort , its median clearance time is considerably larger ( 689 days ) than the median time in the HIV-negative cohort ( 340 days ) ., The main goal of this study was to assess the relative roles of stochastic cell dynamics and immune response in the process of HPV clearance ., Therefore , we compared the model-based persistence distributions for varying immune capacities μ ., As shown in Fig . 4A , the median time to clearance is a decreasing function of μ , and the distributions become more localized with increasing μ ., However , comparing the distributions for μ = 0 and μ/μ̂−=1 ( where μ=μ̂− is the estimated immune capacity of HIV-negative individuals ) , the contribution of the immune response appears to be small in comparison to the contribution of the stochastic cell dynamics ( compare the box plots for μ = 0 and μ/μ̂−=1 in Fig . 4A ) ., This is particularly clear when plotting the clearance probability as a function of time as shown in Fig . 4B ., In particular , comparing the ( μ/μ̂−=0 ) -curve with the ( μ/μ̂−=1 ) -curve after 2 years , the clearance probability without immune response ( 0 . 66 ) is only ∼ 17% smaller than the clearance probability with normal immune capacity ( 0 . 79 ) ., In other words , the stochastic dynamics contribute to as much as ∼ 83% of the viral clearing mechanism in healthy individuals , and the contribution from the immune system is comparatively small ., The subcritical branching process model above was derived under the assumption of a well-mixed basal layer where infected cells are surrounded primarily by susceptible cells ., In this situation , elimination of an infected cell prompts division of an S cell with high probability , justifying the approximation pS = 1 − pS* = 1 . As a consequence , the persistence distribution could be derived analytically ( 6 ) , rendering the model amenable to MLE inference ., To assess whether the ensuing results were an artifact of the well-mixing assumption , we developed the following alternative model that takes into account the spatial clustering of infected cells ., If we assume that the initial cell population is subject to tight clustering , then radial symmetry implies growth in the form of a radially expanding disk in the basal layer ., That is , all the infected cells are inside the disk , whereas the outside is populated only by uninfected cells ., Since the number of D* cells is roughly proportional to the number of S* cells ( see section 2 . 2 in Text SI ) , the disk radius is proportional to n S * ., Accordingly , whenever an infected cell in the interior of the disk is eliminated by a T-cell , the probability to trigger an S cell division is given by the ratio of disk circumference to disk area: p S = min { 1 / n S * , 1 } and pS* = 1 − pS ., Under these assumptions , the S* cell dynamics are now decoupled from the S cell dynamics , but they still depend on the D* cell dynamics , see also section 2 in S1 Text for details ., Since closed-form expressions for the clearance time distributions are out of reach for this model , even with the approximation , we resorted to simulations ., As in Fig . 4A for the well-mixed model , we investigated the impact of increasing immune capacity μ on the clearance time distribution in Fig . 5A ., We make the following observations ., First , time to clearance is generally longer in the branching process model: the three dotted horizontal lines correspond to the three quartiles for the ( μ/μ̂−=1 ) -distribution in Fig . 4 ., Only for the ( μ/μ̂−=8 ) -distribution , which corresponds to an 8-fold increase in immune capacity , are all three quartiles of the spatial model below the corresponding quartiles of the branching process model ., Second , the impact of the immune capacity on the clearance time for the clustered model is even weaker than in the well-mixed model ., Whereas the well-mixed model yields a decrease in median time to clearance for increasing μ , small μ values yield a slight increase in median clearance time for the spatial version ., This is due to the fact that , in contrast to the branching process model , elimination of an infected cell can trigger division of an S* cell ( with probability pS* > 0 ) , therefore compensating for the loss of the infected cell and delaying clearance ., The relative insensitivity of the persistence time distribution to μ is further illustrated in Fig . 5B , where we observe that the clearance probability is only slightly increased for small μ values ., Finally , since the prior estimates of several model parameters have a relatively large interval of uncertainty ( see section 5 in S1 Text ) , we performed a combined sensitivity analysis ., By means of a Monte-Carlo simulation ( with the parameters r , α , ρ and μ drawn from their prior ranges ) , we computed the corresponding persistence time distribution , and found that it did not substantially differ from the fixed parameter distribution ( see section 7 in S1 Text for details ) ., Clearance of anogenital and oropharyngeal HPV infections has primarily been attributed to a successful adaptive immune response ., To date , little attention has been paid to the potential role of homeostatic cell dynamics in clearing HPV infections ., In this study , we combined mechanistic mathematical models at the cellular level with epidemiological data at the population level to disentangle the respective roles of immune capacity and cell dynamics in the clearing mechanism ., Our results suggest that chance—in form of the stochastic dynamics of basal stem cells—plays a critical role in the elimination of HPV-infected cell clones ., In particular , we found that in individuals with normal immune capacity ( HIV-negative cohort ) , the immune response may contribute to less than 20% of the clearing task overall—the rest is taken care of by the random succession of symmetric and asymmetric stem cell divisions ., Furthermore , in immunocompromised individuals ( HIV-positive cohort ) the contribution of the immune response is likely to be negligible ., Based on our results , we may be able to shed new light onto questions currently debated in the literature ., First , in view of the high prevalence of HPV infections and the relatively small risk of persistent infections that eventually lead to malignant disease , the identification of predictive markers for persistence would be valuable 8 ., However , if stochasticity does indeed play a key role in viral clearance , and if the major difference between individuals who clear effectively and individuals who develop persistent infections is largely a matter of chance , then there may not be any predictive markers to discover ., Hence , we may want to rephrase the question , and ask if there is a way of modulating the cellular dynamics to achieve an increase in the clearance probability ., Our results suggests that by increasing either the probability of a symmetric division ( r ) or the proliferation frequency ( λ ) through a locally administered drug , time to clearance and risk of progression could be substantially reduced ., Second , the suggested clearing mechanism could provide an alternative explanation for the correlation between long-term use of combined oral contraceptives and increased risk of persistent infections and cervical cancer 41 ., Since estrogen stimulates 42 and progesterone inhibits 43 epithelial proliferation , it seems plausible that a decrease in cervical proliferation could be caused directly via increased progesterone levels , and indirectly via loss of the estrogenic mid-cycle peak ., The resulting decrease in proliferation ( smaller λ ) would imply an increase in time to clearance and a higher risk of progression to cancer ., While the same reasoning would imply an increased risk of cervical cancer in progestin-only users , the effect of progestin-only contraceptives on HPV persistence and cervical cancer development is less consistent in the literature 44 , 45 ., This highlights the need for future research into the influence of sex steroids on the natural history of oncogenic HPV infection ., Finally , the suggested model of chance-driven clearance is interesting in view of the ongoing debate about viral latency 46–48 ., To date , the existence of latent infections has been demonstrated in animal models , and it is assumed to occur in HPV infections as well ., The current theory of latency is based on the assumption that the virus stays present inside long-lived basal stem cells 48 ., But while the notion of such long-lived , asymmetrically dividing and slow-cycling stem cells is consistent with a theory of epithelial homeostasis developed in the 1970’s 49 , it is not aligned with the new paradigm that is based on fast-cycling stem cells that divide both asymmetrically and symmetrically 18 , 19 , 29 ., According to our model , which is based on this more recent theory of homeostasis , viral latency is again a stochastic phenomenon and occurs if the number of infected cells becomes very small ( latent period ) before growing back to a detectable size ., A more thorough discussion of the latency issue will be the subject of future work ., While population-level models of HPV transmission and progression are commonly used by epidemiologists and health economists , only few groups have developed mathematical models of HPV infection at the tissue level ., In two recent studies 50 , 51 , deterministic ( partial ) differential equation models were used to study evolutionary and ecological aspects of HPV infections and competition between coexisting HPV types ., To our knowledge , we are the first to develop a stochastic model of HPV infection that couples stem cell dynamics with viral infection and immune response ., In addition , the methods introduced here provide a useful tool in the parametric analysis of longitudinal data sets that contain both prevalent ( present at study begin ) and incident ( initiation after study begin ) infections , as well as right-censoring ( study exit before viral clearance ) and interval-censoring ( duration of infection only known up to an interval ) ., In fact , 70% of the individuals in the analyzed REACH data set had an unknown time of initiation , rendering conventional nonparametric approaches problematic ( see section 1 in S1 Text for details ) ., Thanks to the mechanistic models introduced and analyzed in this study , we were able to account for the unknown time lapse between infection initiation and study entry ., Finally , the approach employed in this study may prove useful in other situations ., In fact , mathematical models at the tissue-level are often difficult to parametrize because sample sizes in pathology studies are generally small and exhibit large between-patient variation ., By combining longitudinal population-level data with cell-level mechanistic models as done in this study , insights can be gained across the scales ., Every model comes with its limitations ., First , it is known that there can be time-lags between inoculation and productive infection 22 ., Since these lag times vary widely among individuals , and since we wanted to avoid adding to the complexity of the model , we set the incubation period to zero ., Second , since infected cells acquire a selective growth advantage only at later , symptomatic stages of the infection 31 , we assumed that the presence of viral DNA did not alter the proliferation rates of infected stem cells ., In addition , there is , to our knowledge , no experimental evidence regarding HPV-mediated modulation of symmetric and asymmetric division patterns in infected tissues ., Third , we assumed that the interactions between virus and immune system are independent of the specific HPV strains , and that there are no synergistic or competitive effects among co-infecting types , see also 51 ., Since we believe that adding these more subtle aspects would not change the main conclusion of the importance of stochasticity , we did not incorporate them into the current model ., However , we plan to address these issues in future work ., Fourth , a more realistic alternative to the clustered model version is provided by explicitly spatial models with lattice-based voter dynamics 52 , 53 ., Such a spatial model extension is subject of ongoing work ., Fifth , even though the stratified squamous epithelia at different anogenital and oropharyngeal sites affected by HPV are qualitatively similar , we parametrized our model for cervical infections , and our insights regarding the role of stochastic stem cell proliferation in viral clearance may not apply to other organs ., Finally , our model predicts extinction of infection with probability 1 due to the subcritical nature of the process ., This is not in contradiction with the observation that a small fraction of infections persist and progress ., In fact , progression from HPV infection to sustained neoplastic growth is associated with cellular changes triggered by the viral genome ., These transformations are themselves stochastic processes , and hence progression only takes place in the small group of individuals where the oncogenic transformation takes place before extinction of the infected population .

Introduction, Methods, Results, Discussion

Clearance of anogenital and oropharyngeal HPV infections is attributed primarily to a successful adaptive immune response ., To date , little attention has been paid to the potential role of stochastic cell dynamics in the time it takes to clear an HPV infection ., In this study , we combine mechanistic mathematical models at the cellular level with epidemiological data at the population level to disentangle the respective roles of immune capacity and cell dynamics in the clearing mechanism ., Our results suggest that chance—in form of the stochastic dynamics of basal stem cells—plays a critical role in the elimination of HPV-infected cell clones ., In particular , we find that in immunocompetent adolescents with cervical HPV infections , the immune response may contribute less than 20% to virus clearance—the rest is taken care of by the stochastic proliferation dynamics in the basal layer ., In HIV-negative individuals , the contribution of the immune response may be negligible .

Worldwide , 5% of all cancers are associated with the sexually transmitted human papillomavirus ( HPV ) ., The most common cancer types attributed to HPV are cervical and anal cancers , but HPV-related head and neck cancers are on the rise , too ., Even though the lifetime risk of infection with HPV is as high as 80% , most infections clear spontaneously within 1–2 years , and only a small fraction progress to cancer ., In order to identify who is at risk for HPV-related cancer , a better understanding of the underlying biology is of great importance ., While it is generally accepted that the immune system plays a key role in HPV clearance , we investigate here a mechanism which could be equally important: the stochastic division dynamics of stem cells in the infected tissues ., Combining mechanistic mathematical models at the cell-level with population-level data , we disentangle the contributions from immune system and cellular dynamics in the clearance process ., We find that cellular stochasticity may play an even more important role than the immune system ., Our findings shed new light onto open questions in HPV immunobiology , and may influence the way we vaccinate and screen individuals at risk of HPV-related cancers .

journal.pcbi.1005651

Possible roles of mechanical cell elimination intrinsic to growing tissues from the perspective of tissue growth efficiency and homeostasis

In 1975 , Morata and Ripoll analyzed the mosaic system of the Drosophila imaginal disc composed of wild type cells and mutant cells of ribosomal protein , and found that mutant cells underwent apoptosis and were eliminated from the tissue 1 ., This was the first report of cell competition resulting from local cell-cell interaction ., Subsequent work has shown that the competition phenomenon is widely present , not only in insects but also in vertebrates , and that the elimination of cells is realized through various processes such as cell death , phagocytosis , or live cell extrusion 2–4 ., The process has close connections with important biological events such as tumor formation and tissue size regulation ., Thus , it has attracted attention from a variety of fields 5 , 6 ., As potential mechanisms of cell competition , related molecules and/or signaling pathways have been identified 7 , 8 ., Moreover , recent reports have shown mechanical relevance as well as chemical or molecular mechanisms 7 , 9; for example , Bielmeier et al . found that cells with mutations in genes that determine cell fate were extruded from a tissue by a common mechanical process 10 ., In addition , de la Cova et al . reported that in the Drosophila imaginal disc , the effect of growth of clone did not reach beyond the AP compartment boundary 6 , suggesting that cell elimination is influenced by mechanical constraints ., Interestingly , recent live imaging studies have shown that even when a population is genetically homogeneous , a non-negligible number of cells are extruded from developing tissues ., For instance , it was reported that at pupal stages of Drosophila wing development , about 1000 cells are extruded when the number of cells constituting the wing tissue increases from 4000 to 8000 , i . e . 20% of newly born cells are eliminated 11 , 12 ., Similar live cell extrusion was observed around the midline of Notum closure 4 , 12 ., In addition to epithelial development , in a culture system using MDCK cells , when cell density was artificially increased , some cells were excluded until the original density was restored 13 ., In these cell elimination processes , there is no a priori program that selects which cells are lost , and these mechanisms were mostly explained in a mechanical context ., On the other hand , Clavería et al . found that in early mouse development , competition is based on differences in the expression level of the Myc gene 14 , identifying the existence of chemical signals which determine the relative merits between cells ., In this manner , the elimination of a portion of cells from a tissue or competition between cells is not necessarily due to a difference in genetic background ., By regarding cell elimination from genetically-homogeneous cell populations as a form of broad-sense cellular competition , the experimental observations described above can be classified by the following criteria ( Fig 1 ) ., The first criterion is whether the elimination is based on genetic differences or not , i . e . the focal cell population is “genetically-homogeneous” or “genetically-heterogeneous” ., These are completely exclusive ., Next , as a mechanism of elimination , the cases can be classified based on their mechanical relevance ., Of course , this classification is not completely exclusive; for instance , cell-cell mechanical interactions might trigger the upregulation of previously identified cell death signaling pathways , while in another case , cell-cell chemical interactions through membrane-bound and/or secreted molecules might induce changes in mechanical cellular properties that enable the easy extrusion of cells from a tissue ., Here , we used two kinds of tags , “mechanically-driven” and “non-mechanical” ( or cases in which the mechanical relevance is unclear; Fig 1 ) ., 1 , 2 , 4 , 6 , 10–28 In this study , we mainly focus on the mechanisms of mechanical cell elimination ( MCE ) from a genetically-homogeneous growing tissue for the following reasons ., First , this issue is related to tissue growth efficiency , in other words , “how often do cells newly-born by proliferation contribute to tissue growth” and “how can tissues grow efficiently with less energy waste due to elimination ? ”, Understanding the relationship between MCE and growth efficiency is important because tissues or individuals with higher growth efficiency have evolutionary advantages due to their faster growth ( i . e . , shorter time to reach their target sizes ) and/or higher survival probability under environments with limited energy resources ., Furthermore , as we will discuss later , cellular mechanical/growth parameters ( also called cellular traits ) generally show a distribution even if the genetic background of the cells is the same ., Such phenotypic variation could induce cell-cell competition and change the distribution of cellular traits with an increase in cell population size ., Since MCE in the presence of phenotypic variation can be regarded as a specific case of cell competition between populations of cells with different genetic backgrounds , examining what happens during the growth of such a mixed population of cells with different parameters will enhance our understanding of the mechanisms of cell competition ( especially in relation to mechanical aspects ) ., Secondly , it is likely that MCE is also related to tissue homeostasis ., As shown in the density recovery experiment described above 4 , 13 , if MCE is a kind of mechanical response to perturbations by extrinsic forces or cell division , it could function to maintain uniform cell density and/or stress distribution in a growing tissue ., As one possible mechanism to maintain ( local ) cell density , the density or stress-dependent regulation of cell proliferation was proposed 29–32 ., MCE could be another possible mechanism for cell density homeostasis ., In addition , in the presence of phenotypic variation , competition could lead to the homogenization of cellular phenotypes ., Lastly , from a more theoretical perspective , by focusing on the mechanical aspects , we can approach cell-elimination/competition independent of specific chemical signaling and gene regulation , the details of which are not well understood ., Since the mechanical and growth parameters of cells are basically identical in a population , the rate of elimination due to mechanical cell-cell interactions can be uniquely determined for each population with a given set of mechanical parameters ., The difference between the proliferation rate and elimination rate provides a net growth rate from which the fitness of the population can be quantified ., With this motivation , our aims are as follows ., The first one is to clarify the dependence of mechanical/growth parameters on the cell elimination rate or net growth rate ( fitness ) and to find common geometrical/mechanical determinants of the elimination-rate/fitness ., To achieve these aims , we introduce two quantities to define and measure fitness at the cellular and tissue levels ., Regarding each cell population with a certain growth/mechanical trait as a “species” , the time derivative of its logarithmic growth curve defines the cellular fitness of the species ., On the other hand , the tissue level fitness is defined as the average of cellular fitness between species weighted by the frequency of each species ., When the traits of all cells in a tissue are identical ( i . e . , a pure population ) , the cellular and tissue level fitness are equivalent ., In the presence of phenotypic variation , both cellular and tissue level fitness vary over time as a result of changes in the frequency distribution of cell types constituting the focal tissue through competition between them ., After defining the fitness , by performing numerical simulations with a vertex dynamics model which is used in many studies of the mechanics of epithelial tissues 31 , 33–36 , we examine the dependence of cell elimination rate or cellular fitness on mechanical/growth parameters of the cells ., In contrast to experimental studies , we can independently control each parameter in the model , which is the biggest advantage of simulation-based studies ., We show that the dependence could be summarized by how those parameters affected geometrical and stress heterogeneities within tissues , and that MCE functions to homogenize cell density and stress state within a tissue ., Based on the simulation results , the second aim is to propose possible feedback mechanisms in which mechanical parameters of each cell are regulated depending on its stress state to improve tissue growth efficiency and homeostasis ., These mechanisms could reduce the energy loss resulting from cell elimination , and homogenize cell density or tissue stress ., Since the energy required for growth is proportional to the number of cells produced , the difference in the elimination rate becomes a greater advantage as tissue size increases ., Interestingly , under the proposed feedback regulation , the geometrical and stress heterogeneities between cells were incompatible ., By controlling geometrical heterogeneity , the elimination rate could be reduced but tissue stress heterogeneity increased ( i . e . density homeostasis is achieved but stress homeostasis is impaired ) ., In contrast , controlling the stress heterogeneity increased both geometrical heterogeneity and the elimination rate ( i . e . stress homeostasis is achieved but density homeostasis is impaired ) ., Finally , we examine what happens through competition in a population where cells with different mechanical traits are mixed ., When daughter cells epigenetically inherit their parental traits ( the degree of inheritance was quantified as heritability ) , the trait distribution within the tissue drastically changes with tissue growth , resulting in an increase in fitness at the tissue level ., This clearly demonstrates that cell competition through MCE can improve tissue growth efficiency through the selection of mechanical cell traits , i . e . intra-tissue “evolution” ., Furthermore , through selection , cell density , stress within a tissue , and cellular phenotype are homogenized , which is another possible role for competition through MCE ., From a more theoretical perspective , we propose another differential equation model for competition dynamics that permits us to a calculate the approximate time evolution of tissue-level fitness and trait distribution ., The model is useful for predicting the outcome when tissue size grows much larger , e . g . , reaches a fully-developed size with ~106−107 cells , because direct simulations of a cell-based mechanical model require an immense amount of computation time ., Previous studies have introduced the concept of fitness to discuss the intensity of cell competition 8 , 37 , 38 ., The main focus was relative survivability , that is , which population survives when different populations with different genetic backgrounds are mixed ., In this study , we aim to reveal quantitative effects of cell competition through MCE on tissue growth dynamics ., To do so , we now propose another quantitative measure of fitness , which differs from relative survivability ., In experimental observations and in tissue growth simulations , the growth curve of a developing tissue , i . e . temporal changes in the total number of cells within a growing tissue , g ( t ) , can be locally approximated by an exponential function ( during a certain period ) 11 , 39 , 40 ., Thus , its exponent , representing net growth rate or growth efficiency , defines the fitness of a focal tissue at each time t:, ϕ T i s s u e ( t ) = d d t log g ( t ) ., ( 1 ), Assuming that growth dynamics is modeled by dg ( t ) /dt = ( μ ( t ) -m ( t ) ) g ( t ) , the fitness becomes simply μ ( t ) -m ( t ) , where μ ( t ) is the tissue growth rate through cell proliferation ., As stated before , in actuality , not all cells produced by division survive and contribute to the increase in tissue size ., Some cells are lost from the tissue by extrusion or apoptosis , the rate of which ( termed mortality ) is represented by m ( t ) ., In this study , the cell elimination rate is regarded as a key factor in determining cell mortality within growing tissues ., Rewriting the fitness as μ ( t ) ( 1-m ( t ) /μ ( t ) ) , the term ( 1-m ( t ) /μ ( t ) ) or m ( t ) /μ ( t ) indicates the energy efficiency ( the contribution ratio of produced cells to tissue growth ) or the loss of energy ( the waste of healthy cells ) ., In this way , tissue fitness is determined by both the growth rate and energy efficiency at each time ., As an ideal situation , when a focal tissue is composed of cells with exactly the same traits in mechanical and/or growth properties ( called a pure population ) , the fitness at the tissue level can also be regarded as the fitness of a cell with that focal trait , ϕCell = ϕTissue ., In this case , as shown later , the value of fitness is almost constant ( when the growth rate is sufficiently slow ) ., Using cell cycle T , the tissue growth rate μ is given by ( log 2 ) /T ., Regarding the mortality m , when defining the cell elimination rate as the ratio of the number of eliminated cells to that of newly-born cells by cell division per unit increment of tissue size: ε = Neliminated/Nproduced , the relationship m = - ( log ( 1-ε/2 ) ) /T holds ( see the Models section for details ) ., In the next three sections , we consider such a pure cell population to examine how and by what mechanisms cell mechanical/growth properties affect fitness , especially mortality through cell elimination ., Consider next a tissue that is composed of cells with different traits ( called a mixed population ) ., Here , the net growth rate at the tissue level and that at the cellular level for each population with each trait are different in general ., Denoting the cellular fitness with i-th trait by ϕCell , i , the tissue fitness is given by:, ϕ T i s s u e ( t ) = ∑ i f i ( t ) ϕ C e l l , i ( t ) ,, ( 2 ), ϕ C e l l , i ( t ) = d d t log g i ( t ) ,, ( 3 ), where fi is the frequency of cells with i-th trait in the entire tissue and gi ( t ) is the growth curve of the population with i-th trait ., Importantly , in a mixed population , cellular fitness for each trait is generally not constant but varies with time ., This is because the elimination rate of cells with a certain trait depends on the traits of its surrounding cells that mechanically interact with one another , and because the distribution of cellular traits throughout the entire tissue changes with time through selection based on the differences in cellular fitness ., Focusing on developmental advantages , fitness at the tissue level is more important than the cellular fitness ., In the remaining subsections , we show how mechanical feedback and competition between different traits improve tissue fitness or growth efficiency ., We started by systematically examining how the frequency of cell elimination depends on mechanical properties and the growth rules of epithelial cells using the vertex dynamics model ( Fig 2A , also see the Models section ) ., Specifically , we focused on tissue fluidity , cell division orientation , and proliferation rate ., In the vertex dynamics model , each cell shape is represented as a polygon formed by linking several vertices , and each vertex moves so as to decrease energy function U of the system ( see the Models section ) ., U includes two parameters Λ and Γ ( see the Models section for details ) ., Λ is the coefficient for tension acting on a cell’s edge; stronger cell-cell adhesion and/or weaker contractility of actomyosin fibers along the edge correspond to a smaller value of Λ ., The other parameter Γ is the coefficient for perimeter elasticity , the value of which is determined by the contractility of the actomyosin network over the apical surface of a cell 35 ., Each cell has a clock representing the cell cycle ., When the clock within a cell reaches a specific value T , the cell divides with an axis through its center and the clock is reset ( note that the cell cycle includes slight stochasticity to avoid the synchronization of divisions; see the Models section for details ) ., Regarding the division orientation , we modeled it as a random variable distributed around the shortest axis ., By changing a single parameter in the distribution , the randomness of division orientation can be controlled ( Fig 2B; also see the Models section ) ., As a consequence of push-pull dynamics between cells through their divisions , a cell whose area is below a certain threshold ( θT2 = 0 . 2 ) will be removed ( called T2 process; Fig 2C; also see the Models section ) ., As mentioned in the introduction , there have been some reports on MCE ., However , currently there is little known about whether a threshold in cell area for MCE exists and what the threshold value is ., The only exception is a study by Marinari et al . in which they showed that in the case of Drosophila notum development , cells whose area was less than ~25% of the initial area were eliminated 4 ., According to this report , we set θT2 = 0 . 2 ., To confirm the generality of our results , we also examined cases with different values for θT2 ( specifically , 0 . 05 , 0 . 1 , and 0 . 3 ) , and our results ( shown below ) did not change qualitatively ., In addition , as clarified in the later subsection , cell size is highly correlated with stress state , and thus our assumption on the criterion for MCE , the existence of a cell size threshold for MCE , also includes another criterion , the existence of a threshold for stress acting on a cell ., Tissue fluidity , i . e . the liquid-like behavior of a tissue , increases for smaller values of Λ and/or Γ 35 ., Intuitively , in those situations , each cell moves so as to maintain its apical area as near to the natural value ( a given constant , see the Models section ) as possible , leading to easier deformation and more frequent cell-cell rearrangements when forces due to tissue growth act on the cell ., In contrast , for larger values of those parameters , the force for isotropic shrinking increases , resulting in less fluidity ( see also the Models section ) ., In the vertex dynamics model , cell rearrangement is implemented by the reconnection of vertex networks called a T1-process ( Fig 2C ) , and thus the distance threshold for the reconnection ( θT1 ) also affects the tissue fluidity ., Whether or not the value of θT1 itself is a controllable parameter is unknown ., However , observations have shown that in some situations cell intercalation frequently occurs , and in other situations it rarely occurs and multicellular rosette structures are formed instead 41 , suggesting the existence of mechanisms that regulate the frequency of intercellular rearrangement ., The upper panels in Fig 3A show the dependences of cell elimination rate ε and ϕCell on the three parameters affecting the tissue fluidity ., These three parameters have the same tendency , although the degree of dependence on the T1-threshold θT1 is lower ., With an increase in tissue fluidity , the elimination rate decreases or the fitness increases monotonically ., In particular , the parameter dependence of ε can be approximated by a Gaussian-type function ( Fig 3A ) , which is useful in calculating the time evolution of cellular/tissue fitness within a mixed cell population as will be shown later ( see the final subsection ) ., For a fixed parameter , the cell elimination rate ε and the cellular fitness ϕCell are nearly constant during tissue growth as long as cell density is regarded as constant ( Fig 3B ) ., As described above , the randomness of cell division orientation was introduced as a tissue growth rule ( Fig 2B ) ., It was controlled by a single parameter , the variance of division orientation around the shortest axis of each cell ., Unexpectedly , the division orientation has a clear effect on the cell elimination rate ., When a cell divides along the shortest axis , the elimination rate decreases compared to situations in which division is randomly oriented ( Fig 3A ) ., In regards to the growth rate , as expected , the elimination rate becomes higher as it increases ( Fig 3A ) ., In actuality , this tendency has been observed in a biological system ., During development of the Drosophila notum , cell elimination occurred more frequently in the tissue of the mutant with the higher growth rate 4 ., As shown in the previous subsection , cell elimination naturally occurs as a consequence of tissue growth , and its rate depends on different mechanical/growth parameters ., In order to find common factors for determining the elimination rates , we next searched for quantities whose values change with the same tendency as the elimination rate when mechanical/growth parameters change ., Specifically , we focused on, ( i ) cell shape regularity , which was quantified by elliptical approximation ,, ( ii ) the frequency of cell rearrangement ( T1-process ) and, ( iii ) the variance in size between cells ., As shown in Fig 4A , only the variance in cell size has a high correlation with the elimination rate , indicating that cell size variance is the only geometrical determinant of the elimination rate ., This is reasonable because cell elimination , i . e . the T2-process in the vertex dynamics model , is determined by cell size ., However , it may be significant that the correlation with the remaining quantities ( T1-frequency and cell shape regularity ) is much lower ., This can be interpreted as follows ., Different mechanical and growth parameters affect the cell size variance ( and thus cell elimination ) in different ways ( Fig 4 ) ., For example , for higher tissue fluidity ( e . g . , smaller Λ/Γ or larger θT1 ) , the increase in T1-frequency reduces the cell size variance , leading to a decrease in the elimination rate ( left panels in Fig 4A and 4B ) ., In contrast , biased division-orientation along the shortest axis reduces the elimination rate even though the frequency of the T1-process is much lower ( middle panel in Fig 4B ) ., In this case , the increase in cell shape regularity instead of T1-frequency likely reduces the cell size variance ( middle panel in Fig 4C ) ., In regard to growth rate , as shown in Fig 4B , a higher growth rate decreases the T1-frequency and relative tissue fluidity is lower , which could cause an increase in the cell size variance and elimination rate ., A higher growth rate also increases the spatial heterogeneity of cell density ( Fig 5B ) ; the density becomes higher in more central regions of tissue as observed in the development of the Drosophila wing imaginal disc 42 , 43 ., As shown in Fig 5B , cell density has a clear positive correlation with size variance ., More central regions have higher density , which inhibits smooth cell rearrangement and thus leads to an increase in cell size variance and elimination ( right panel in Fig 5B ) ., Taken together , cell size variance is regulated in different ways and determines the cell elimination rate ., Since the inverse of the cell area corresponds to local cell density , the above result can also be interpreted as the spatial heterogeneity in cell density determines the cell elimination rate ., As expected , the heterogeneity of cell density around an elimination point ( defined as the CV of the inverse of cell size in the region including the elimination point ) decreases after its occurrence , demonstrating that cell elimination can recover the homogeneity in cell density ( Fig 5C and 5D ) ., Since stress in a tissue drives its deformation , we next evaluated the stress state acting on each cell in a growing tissue ., In this study , tissue is modeled not as a continuum but as a multicellular assembly , and thus Cauchy’s stress acting on each cell was evaluated by its microscopic and discrete representation ., Among different representations hitherto proposed 44 , 45 , we here adopted the two types of stress tensors used in recent papers on stress distribution in developing tissues 46 , 47 ( see σ ( A ) and σ ( B ) in Fig 6A , and the Models section for details ) ., The calculated tensors were characterized by the two scalars , stress magnitude σ1+σ2 and stress anisotropy σ1-σ2 , where σ1 and σ2 ( σ1>σ2 ) are the principal stresses ( Fig 6B ) ., Positive ( or negative ) values of σi represent a tensile ( or compressive ) stress ., Correlation analysis with cell geometry showed that the stress magnitude and stress anisotropy are strongly correlated ( >0 . 9 ) with cell size and cell shape anisotropy , respectively ( Fig 6C , top and middle ) ., This holds for cases with different mechanical/growth parameters and for either definition choice for stress tensor ( Eqs ( 25 ) and ( 27 ) in the Models section ) ., With regard to stress anisotropy , its direction ( defined as the direction of the maximum principal stress ) is perfectly consistent with the direction of cell shape anisotropy ( Fig 6C , bottom ) ., In this manner , in a pure population , cell geometry perfectly reflects the stress state acting on it ., As seen later , when growth/mechanical properties vary through mechanical feedback or when a tissue is composed of cells with different properties , the correlation between cell geometry and stress state decreases somewhat ( around 0 . 7 ) ., We showed in the previous section that the cell elimination rate is determined by the variance in cell size or cell density ., Thus , we can conclude that the spatial heterogeneity of stress magnitude , not that of stress anisotropy , is the main mechanical cause of cell elimination ., To avoid misunderstandings , we emphasize that the heterogeneity described here is not driven by the difference in mechanical/growth properties between cells ., Rather , in all simulations in Fig 6 , all cells had the same parameter values ( i . e . , a pure population ) ., The stress heterogeneity observed here is intrinsic to growing tissues ., To clarify the relationship between the heterogeneity of stress magnitude and tissue growth by cell division , we examined the change in stress states experienced by cells surrounding each dividing cell under different mechanical/growth rules ., In all cases , the stress magnitude always decreased ( became more compressed ) on average ., As expected , the average local stress change ,, E ( σ 1 + σ 2 ) A f t e r d i v i s i o n − ( σ 1 + σ 2 ) B e f o r e d i v i s i o n ,, ( 4 ), correlates well with the spatial heterogeneity in stress magnitude , and consequently with the cell elimination rate ( Fig 6D ) ., We also examined the change in local stress through the cell elimination event ., In contrast to the case of cell division , cell elimination caused a release in stress on average ., This suggests that MCE can be a mediator for homogenizing the tissue stress state ( i . e . , stress homeostasis ) as well as actomyosin activity ., This result is consistent with observations from previous experimental studies in which a potential role for cell elimination in the maintenance of homeostasis in epithelial tissues was shown 4 , 13 ., Similar to the case of cell division , cell elimination only affected stress magnitude , not stress anisotropy ., Fig 7 shows a summary of the results obtained with a pure population in the above ( 2nd-4th ) subsections ., When tissue grows , cell division induces surrounding tissue compression , which increases the spatial heterogeneity in stress magnitude and cell size/density ., The heterogeneity of cell size/density ( or stress magnitude ) is the common geometrical ( or mechanical ) trigger of MCE ., Once cell elimination occurs , the compression due to cell division is released and the variance in local cell density also decreases ., Thus , MCE functions as a mechanism for achieving density and stress homeostasis ., On the other hand , from the perspective of energetic efficiency , reducing MCE events and increasing the contribution of newly-born cells to tissue growth can achieve target size with less energy resources , which can be achieved by higher tissue fluidity , division along the shortest axis , and lower growth rate ., In the above sections , to clarify the effects of cellular mechanical/growth parameters on the elimination rate or the loss of energy in the fitness function ( see the first subsection ) , we assumed a pure population in which all cells had the same values for mechanical/growth parameters ., However , in actuality , the values of these parameters can change among cells even if all of them have an identical genetic background ., For example , cells can change their physical properties and/or growth rate through feedback depending on the stresses acting on them or mechanical environment ., In addition , due to various noise sources such as intrinsic fluctuations in gene expression levels and extrinsic environmental noise 48 , 49 , the parameter values can show a distribution among the cells ., Here we examine the possibility of improving tissue growth efficiency ( or tissue fitness ϕTissue ) and homeostasis through mechanical feedback; in the next section we will examine the results of competition in a population where cells with different mechanical parameters are mixed ., Density- or stress-dependent growth regulation is a type of feedback that has been discussed extensively 29–31 , although the molecular mechanism of mechano-sensing is not entirely clear ., As a plausible example , we first examine how this feedback would affect ϕTissue ., In particular , we modeled the clock of the cell cycle τα as a function of stress magnitude S = σ1+σ2:, d d t τ α ( t ) = { 0 ( S < S ¯ ) const ., ( S ≥ S ¯ ) ,, ( 5 ), where S ¯ is the mean stress magnitude before tissue growth starts ., As mentioned before , when the clock becomes larger than the threshold T , cell division occurs ., This stress-dependent growth regulation led to a large decrease in the elimination rate ( Fig 8A ) by preventing spatial heterogeneity in cell density , clearly demonstrating that this type of feedback can promote both tissue growth efficiency and homeostasis ., However , it was not necessarily efficient in the sense of developmental speed , as it took much more time to reach a certain tissue size compared to cases without feedback ( Fig 8B ) ., Developmental speed is an evolutionarily significant trait as well as growth efficiency and homeostasis ., Our results in the previous sections suggest another possible means by which mechanical feedback could improve tissue growth efficiency and homeostasis: it could decrease the elimination rate while maintaining the growth speed ., Here , we assume that cells can sense their own stress state or that of adjacent cells through their cytoskeletons or filopodia 50–52 and that , depending on the state , they can change local tissue fluidity by appropriately regulating their contractility and/or edge tension ( i . e . , Γ and Λ in the vertex dynamics model ) through a change in intracellular localization of actin and adhesive molecules ., As shown above , the elimination rate perfectly correlates with the variance in cell size or that of stress magnitude , and thus the proposed feedback mechanisms must be designed to decrease either of them ., Specifically , we considered the following feedback systems;, d d t χ α ( t ) = c ( S α − S 0 ) − d ( χ α ( t ) − χ 0 ), ( 6 ), or, d d t χ α ( t ) = c ( S α − S ¯ α ) − d ( χ α ( t ) − χ 0 ) ,, ( 7 ), where χα ( t ) is the parameter for apical contractility or cell-cell edge tension of cell α ( i . e . , χα ( t ) = Γα ( t ) or Λα ( t ) ) , and χ0 ( Γ0 or Λ0 ) is a cell-independent basal value ., The magnitude of c indicates the feedback strength and d is the parameter determining the timescale of restoration to the basal value ., When the value of c is positive , the feedback reduces cell size variance , whereas a negative value for c reduces the variance in stress magnitude ., In the mechanism given by Eq ( 6 ) , the apical contractility or cell-cell edge tension of a cell i

Introduction, Results, Discussion, Models

Cell competition is a phenomenon originally described as the competition between cell populations with different genetic backgrounds; losing cells with lower fitness are eliminated ., With the progress in identification of related molecules , some reports described the relevance of cell mechanics during elimination ., Furthermore , recent live imaging studies have shown that even in tissues composed of genetically identical cells , a non-negligible number of cells are eliminated during growth ., Thus , mechanical cell elimination ( MCE ) as a consequence of mechanical cellular interactions is an unavoidable event in growing tissues and a commonly observed phenomenon ., Here , we studied MCE in a genetically-homogeneous tissue from the perspective of tissue growth efficiency and homeostasis ., First , we propose two quantitative measures , cell and tissue fitness , to evaluate cellular competitiveness and tissue growth efficiency , respectively ., By mechanical tissue simulation in a pure population where all cells have the same mechanical traits , we clarified the dependence of cell elimination rate or cell fitness on different mechanical/growth parameters ., In particular , we found that geometrical ( specifically , cell size ) and mechanical ( stress magnitude ) heterogeneities are common determinants of the elimination rate ., Based on these results , we propose possible mechanical feedback mechanisms that could improve tissue growth efficiency and density/stress homeostasis ., Moreover , when cells with different mechanical traits are mixed ( e . g . , in the presence of phenotypic variation ) , we show that MCE could drive a drastic shift in cell trait distribution , thereby improving tissue growth efficiency through the selection of cellular traits , i . e . intra-tissue “evolution” ., Along with the improvement of growth efficiency , cell density , stress state , and phenotype ( mechanical traits ) were also shown to be homogenized through growth ., More theoretically , we propose a mathematical model that approximates cell competition dynamics , by which the time evolution of tissue fitness and cellular trait distribution can be predicted without directly simulating a cell-based mechanical model .

When genetically different cell populations are mixed , there is competition between cells such that losing cells are eliminated from a tissue ., Such cell elimination is also observed during normal development in genetically-homogeneous tissues ., In addition to the identification of key genes and molecular mechanisms related to these phenomena , the relevance of cell/tissue mechanics has been reported as a possible common mechanism of elimination ., Here , we examined these mechanisms and possible functions of mechanical cell elimination ( MCE ) from the perspective of tissue growth efficiency and homeostasis ., Using mechanical simulations of tissue growth processes , we identified key parameters of cellular mechanical/growth properties that determine elimination rates or cellular fitness ( defined as the difference between cell division and elimination rate ) ., Based on these results , we propose mechanical feedback mechanisms that could improve tissue growth efficiency and density/stress homeostasis ., Furthermore , when cells with different mechanical traits are mixed , we found that MCE could drive a drastic shift in cell trait distribution , thereby improving tissue growth efficiency through the selection of cellular traits ., With this , cell density , stress state , and phenotype were also shown to become homogenous ., Our results will permit the elucidation of the mechanisms of intrinsic tissue defense against abnormal cells by their elimination through mechanical cell-cell interactions .

death rates, medicine and health sciences, classical mechanics, mechanisms of signal transduction, demography, cell cycle and cell division, cell processes, condensed matter physics, population genetics, mechanical stress, anisotropy, physiological processes, homeostasis, materials science, pharmacology, population biology, tissue distribution, feedback regulation, pharmacokinetics, physics, people and places, biochemistry, signal transduction, cell biology, physiology, genetics, biology and life sciences, physical sciences, material properties, evolutionary biology

journal.pgen.1000628

The Limits of Individual Identification from Sample Allele Frequencies: Theory and Statistical Analysis

Homer et al . 1 showed that it was possible in some circumstances to identify whether a person with observed genotypes at multiple loci was part of a sample from which only estimated allele frequencies were known ., Such identification would be particularly useful in forensic science if the presence or absence of a persons DNA in a mixture of DNA could be established ., The authors also discussed the relevance of their findings when summary statistics such as allele frequencies were available in the public domain as part of genotype-phenotype studies , because it possibly could be established that individuals , or their close relatives , were part of a particular study ., As a result of the publication of Homer et al . , NIH and the Wellcome Trust added more restrictions to the access of such data to avoid potential identifiability ( http://grants . nih . gov/grants/gwas/data_sharing_policy_modifications_20080828 . pdf ) ., The approach taken by Homer et al . was to have two samples with estimated allele frequencies , here called the “test” and “reference” sample , and to ask whether an individual was ‘close to’ either of these samples , using a statistic that measured a distance to the sample ., The properties of the test statistic were not investigated theoretically ( although simulation studies were performed ) , and the difference between “sample” and “population” was not always clear ., In this note we take a best-case idealised setting in which there is a single population from which there is a test sample with allele frequencies at a number of loci and from which there is a single individual , called the proband , with full genotypes ., The question is whether the person was part of this test sample from which allele frequencies are available ., We use both likelihood and linear regression theory , which illustrate different approaches to the problem , to draw inference about the hypothesis that a proband was part of the test sample ., We show that the power of identification of a proband as part of a test sample is , approximately , proportional to the number of independent SNPs divided by the size of the sample from which the allele frequencies are available ., The power is reduced by a predictable magnitude if the frequencies in the population are themselves estimated imprecisely ., Properties of likelihood-ratios and regression test statistics and a comparison with the statistic used by Homer et al . were verified by simulation ., There are m independent SNP markers with a population frequency of pi for allele B at the ith SNP ., We assume Hardy-Weinberg equilibrium in the population , so that the genotype proportions for the ith SNP are ( 1−pi ) 2 , 2pi ( 1−pi ) and pi2 for genotypes AA , AB and BB , respectively ., We have estimated allele frequencies based upon a test sample of N unrelated individuals ., In the test sample of 2N alleles , ni is the number of B alleles at locus i ., In this study we assume that N is known and individuals are equally represented in computing ., Note that these conditions are unlikely to be fully met in forensic applications when the test sample may be a DNA pool and we consider the implications later ., The genotype for proband X at the ith SNP is gi , which can take values of 0 , 1 and 2 for genotypes AA , AB and BB , and the expectation of yi\u200a=\u200a½gi is the population frequency pi , i . e . E½gi\u200a=\u200api ., To simplify derivations , we shall first assume the population frequencies pi , are known ., More generally , we assume we have prior unbiased estimates of the allele frequencies from the same population from a different finite sample ( the “reference sample” ) of size N* , in which there are n*i B alleles at locus i ., As both the test and reference samples are drawn independently from the population , the best estimate of the frequency in the population is given by the pooled value , It is explained subsequently why this estimate , rather than say n*i/2N* , the estimate of the allele frequency from the reference sample , is used in the statistical analysis ., We show that the main results for the regression approach are based upon the expectation that the regression of the proband frequency , yi\u200a=\u200a½gi , on , each expressed as deviations from population frequencies , is distributed about unity for all loci if the proband was part of the test sample , and about zero otherwise ., Population allele frequencies on m markers were drawn from a uniform distribution with lower bound 0 . 05 and upper bound 0 . 95 ( i . e . , minor allele frequency ( MAF ) >0 . 05 ) ., For the ith SNP , a genotype score ( yi ) of a proband was simulated from a binomial distribution with probability pi and sample size 2 ., Allele frequencies in the reference and test samples were simulated from a binomial distribution with probability pi and sample size 2N* and 2N , respectively ., If the proband was part of the test sample then the test sample was simulated on N−1 individuals and the allele count from the proband was added to that from this sample to create a sample from N individuals ., Linear regression was performed as described previously , for a type-I error rate of 0 . 05 , and the Homer et al . 1 test statistic ( see Text S2 ) was also implemented ., 1000 simulations were performed for combinations of N\u200a=\u200a100 , 1000 , 10000 , N*\u200a=\u200a100 , 1000 , 10000 and ∞ and m\u200a=\u200a50 , 000 , when the proband was either part or not part of the test sample ., The results are shown in Table 1 ., The regression type-I error rates are well controlled when the hypotheses tested are true ., As predicted ( Text S2 ) , the type-I error rates for the Homer et al . test statistic are not well controlled ., In many cases the probability of rejecting the null hypothesis when it is true is close to zero ., Power to determine whether the proband is part of the test sample is good for test samples of 1000 if the reference sample size is large ., Inference from the regression and likelihood-ratio approach is similar , as expected ( Table S1 ) ., In our derivations we have assumed that all samples ( proband , reference and test ) are from the same population and that within the population there is random mating ., What if these assumptions are violated ?, If all samples are from the same population but there is deviation from HWE then the tests are somewhat biased because HWE is assumed in computing the likelihood and the variance of sample allele frequencies ., Population differences are more serious and can lead to the wrong inference ., There are a large number of possibilities because , in principle , the proband , reference and test samples can all come from different populations ., However , population differences between the reference and test sample can be tested explicitly using standard tests for differences in gene frequency ., There seems little point in testing whether a proband was part of a specific test sample when there is no reference sample from the same population ., Nevertheless , what can we predict if the reference population is not actually from the same population , but is used as if it is ?, Then both the likelihood statistics for the hypothesis ‘in’ and ‘out’ are inflated , by essentially the same amount , so the problem is not the divergence between the two populations , but bias in the test statistic ., If population frequencies are inappropriately or approximately estimated , the sample is more likely to be assigned as ‘in’ when it should not be ., The reference sample is of little value if the divergence between the populations , expressed as Wrights FST , approaches 1/ ( 2N ) ., Can we quantify the limits of identification in practical situations ?, This is hard , because there are ( at least ) three difficulties in addition to the theoretical sample m/N criterion: For these reasons we cannot set a simple limit to identification without reference to other parameters ( or speculation ) ., In the analysis we have not considered the possibility that the proband is not in the test sample , but is related to one or more persons who is ., For example if a relative with relationship R ( e . g . R\u200a=\u200a½ for full sibs ) is in the test sample , then the expectation of the regression coefficient is E ( b ) =\u200aR rather than 0 or 1 ., Similar calculations can be done if , for example , there are several relatives in the test or reference samples ., If many markers are used , a value of b of approximately one-half would raise suspicions that in fact a full sib , parent or child is in the test sample ., Lower , but non-zero values could be consequences of sampling or relationship ., The simulation results in Table 1 illustrate how sensitive the methods can be , and hence there seems a real possibility of identifying not just the proband but also his/her relatives ., A problem frequently met in forensic applications is whether a particular individuals DNA appears in a mixture obtained at a crime scene , for example ., In this case , it is usually unknown how many individuals DNA is present in the sample ( i . e . , N is unknown ) , equal representation cannot be assumed , and there may be allelic drop out in the sample , although Homer et al . 1 showed empirically that probands could be detected even if their contribution to the DNA pool was small ., We do not therefore consider the present results to be relevant for probabilistic inference in a forensic setting ., However , exclusion of a proband from a pooled DNA sample is possible if many markers are used , the actual N is small and frequencies of alleles from the pool are estimated accurately ., The likelihood framework is sensitive to genotyping errors in that false exclusions could occur , but the analysis could be adapted to model genotype counts with specified probability of errors or by assuming replacement sampling in computing P ( in ) ., The linear regression approach is likely to be robust to genotyping error ., In contrast to forensic applications , in the situation considered by Homer et al . in which the test sample is a database constructed using a specified number of individuals each with individual genotypes , and with the gene frequencies estimated as their average , our results support their conclusions ., Probands that were part of a test sample could be identified even for samples sizes of 1000 ., If , for example , there are both diseased case and healthy control samples in the association test , each assumed to be sampled from the same population , then it is possible to test whether an individual is present in either the case or control group using the analysis we have described , but using each sample in turn as the test sample ., Current genome-wide association studies ( and meta-analyses based upon multiple studies ) are conducted on large samples , often of the order of 10 , 000 or so , and in this case our results show that the power to identify a proband who was part of such a large sample when the reference sample is of similar size is only about one-half ( Table 1 ) assuming 50 , 000 independent loci , even under the ideal circumstances considered in this study .

Introduction, Methods, Results, Discussion

It was shown recently using experimental data that it is possible under certain conditions to determine whether a person with known genotypes at a number of markers was part of a sample from which only allele frequencies are known ., Using population genetic and statistical theory , we show that the power of such identification is , approximately , proportional to the number of independent SNPs divided by the size of the sample from which the allele frequencies are available ., We quantify the limits of identification and propose likelihood and regression analysis methods for the analysis of data ., We show that these methods have similar statistical properties and have more desirable properties , in terms of type-I error rate and statistical power , than test statistics suggested in the literature .

It was shown recently by Homer and colleagues that it may be possible to determine whether a person with known genotypes at a number of markers was part of a pool of DNA from which only frequencies of alleles at the markers are known ., In this study , we quantify how well such identification can work in practice ., The larger the size of the sample from which the allele frequencies are available , the more independent genetic markers are required to allow individual identification .

genetics and genomics/population genetics

journal.pntd.0006372

Risk factors for human acute leptospirosis in northern Tanzania

Leptospirosis is a zoonotic bacterial infection and is increasingly recognized as an important cause of fever in Africa 1 ., Leptospirosis was a leading cause of severe febrile illness in a study conducted in northern Tanzania during 2007–8 , where it was diagnosed in 8 . 8% of participants 2 ., The annual incidence of severe acute leptospirosis in northern Tanzania is high , but has fluctuated during surveillance over two time periods: from 75–102 cases per 100 , 000 people in 2007–08 to 11–18 cases per 100 , 000 people in 2012–14 , suggesting dynamic transmission patterns 3 ., An understanding of major animal reservoirs , sources , and modes of transmission to humans is required to inform leptospirosis control ., Animals infected by Leptospira may become carriers and excrete Leptospira in urine leading to environmental contamination ., Humans can be infected following direct exposure to the urine of infected animals or through contact with contaminated surface water or moist soil 5 ., Portals of entry include mucous membranes and broken skin 5 ., While the major reservoirs , sources of human infection , and modes of transmission of infection are established on a global scale , there is substantial variation by location reflecting the diverse ecology of Leptospira ., In many tropical countries , rodent species are considered the most important animal reservoir for human infection 4 ., As such , dominant risk factors for leptospirosis in many tropical countries include activities that expose individuals to rodent urine , such as living in urban slums , proximity to sewers , and exposure to flood waters 4 , 6 , 7 ., In Tanzania and most other African countries , the risks factors for human infection are not well characterized 1 , 4 , and there is some evidence that the risk factors may differ from other tropical countries ., In northern Tanzania there is evidence that leptospirosis is more common in rural areas where both livestock and rodents could be important sources of human infection 8 , and previous Leptospira exposure studies have identified livestock farmers as a high-risk group for Leptospira seropositivity 9 ., Serogroup reactivity patterns of acute human leptospirosis infections have also suggested that livestock may be reservoirs for human cases 8 , and studies of livestock have found high proportions that were seropositive or with leptospiruria 10–12 ., To inform leptospirosis control in Tanzania , we aimed to identify risk factors for acute leptospirosis and Leptospira seropositivity , and identify sources of human Leptospira infection ., We conducted a cross-sectional study at Kilimanjaro Christian Medical Centre ( KCMC ) , a 450-bed zonal referral hospital and , Mawenzi Regional Referral Hospital ( MRRH ) a 300-bed regional referral hospital , both in Moshi ., Moshi ( population ~180 , 000 ) is the administrative capital of the Kilimanjaro Region ( population ~1 . 6 million ) of Tanzania ., Moshi is situated at approximately 890 meters above sea level and has a tropical climate with rainy seasons from October through December , and March through May ., Agriculture in northern Tanzania includes smallholder systems involving mixed crop and livestock farming , as well as pastoralism ., We enrolled pediatric and adult patients presenting to KCMC and MRRH from February 2012 through May 2014 ., From Monday through Friday , we screened all patients in the adult medical ward at KCMC and the adult and pediatric medical wards at MRRH within 24 hours of admission , as well as patients presenting to the outpatient department at MRRH ., We enrolled consecutive eligible inpatients and every second eligible outpatient ., Patients were eligible to participate if they had an axillary temperature of >37 . 5°C or a tympanic , oral , or rectal temperature of ≥38 . 0°C at presentation ., Inpatients were also eligible if they reported a history of fever within the past 72 hours ., After obtaining informed consent , a trained study team member completed standardized clinical history and risk factor questionnaires ., The risk factor questionnaire included questions on socio-demographic characteristics , participant living environment , and daily activities performed over the past 30 days , focusing specifically on animal-related activities , exposure to surface water and to rodents ( S1 Text ) ., The questionnaire was designed to include established risk factors for leptospirosis from studies done in other settings 4 , 6 , 7 , 13–15 , and was piloted prior to use ., For participants who lived in the Kilimanjaro Region , study personnel visited participant households to record Global Positioning System ( GPS ) coordinates of participants’ dwellings ., Clinician diagnoses were recorded ., Participants were asked to return 4–6 weeks after enrollment for collection of a convalescent serum sample ., Blood was allowed to clot for between 30 and 60 minutes ., It was then centrifuged for 15 minutes at 1 , 126–1455 relative centrifugal force to separate serum ., Serum was stored at -80°C ., Serum specimens were batch shipped on dry ice from Moshi , Tanzania to Atlanta , GA , United States of America for testing ., Serology for leptospirosis was performed at the US Centers for Disease Control and Prevention using the standard microscopic agglutination test ( MAT ) with a panel of 20 Leptospira serovars belonging to 17 serogroups 16 ., These included: Australis ( represented by L . interrogans serovar Australis , L . interrogans serovar Bratislava ) , Autumnalis ( L . interrogans serovar Autumnalis ) , Ballum ( L . borgpetersenii serovar Ballum ) , Bataviae ( L . interrogans serovar Bataviae ) , Canicola ( L . interrogans serovar Canicola ) , Celledoni ( L . weilii serovar Celledoni ) , Cynopteri ( L . kirschneri serovar Cynopteri ) , Djasiman ( L . interrogans serovar Djasiman ) , Grippotyphosa ( L . interrogans serovar Grippotyphosa ) , Hebdomadis ( L . santarosai serovar Borincana ) , Icterohaemorrhagiae ( L . interrogans serovar Mankarso , L . interrogans Icterohaemorrhagiae ) , Javanica ( L . borgpetersenii serovar Javanica ) , Mini ( L . santarosai serovar Georgia ) , Pomona ( L . interrogans serovar Pomona ) , Pyrogenes ( L . interrogans serovar Pyrogenes , L . santarosai serovar Alexi ) , Sejroe ( L . interrogans serovar Wolffi ) , and Tarassovi ( L . borgpetersenii serovar Tarassovi ) ., MAT was performed beginning at a dilution of 1:100 , with subsequent two-fold dilutions ., Positive and negative controls were included with each run ., We defined leptospirosis cases as participants with either a four-fold rise in agglutinating antibody titers between acute and convalescent serum , or a single reciprocal titer of ≥800 17 ., Seropositivity was defined as a single positive reciprocal titer of ≥100 from either sample ., Controls were participants with negative titers on both acute and convalescent serum samples ., The predominant reactive serogroup for cases and seropositive participants was defined as the serogroup containing the serovar with the highest titer ., For each participant , village population density was calculated from the 2012 Tanzania Population and Housing Census 18 ., For the purpose of analysis , a priori zone classifications were applied to each village 19 ., Villages with a population density of 10 inhabitants/km2 were classified as urban; villages ≤15km distance from urban areas with a population density ≥3 and < 10 inhabitants/km2 were classified as peri-urban; and villages ≥15km distance from an urban area with a population density of <3 inhabitants/km2 19 ., Georeferenced mean annual rainfall and soil type data were obtained from the 2002 Kenya International Livestock Research Institute report 20 ., Land use data were obtained from the 2010 National Geomatics Center of China report 21 ., Daily rainfall data were obtained from the Tanzania Production Company ( TPC ) rainfall stations located near Moshi ., Patient history , questionnaire , and MAT data were entered using the Cardiff Teleform system ( Cardiff , Inc . , Vista , CA , USA ) into an Access database ( Microsoft Corporation , Redmond , WA , USA ) ., Geospatial data were managed using QGIS , version 2 . 8 . 3 ( Free Software Foundation , Boston , MA , USA ) ., Spatial scan statistics were calculated using a Bernoulli model to assess evidence of spatial clustering of cases using SatScan version 9 . 0 ( www . satscan . org ) 22 ., All other analyses were performed using Stata , version 13 . 1 ( StataCorp , College Station , TX , USA ) ., This study was conducted in accordance with the Declaration of Helsinki ., It was approved by the KCMC Research Ethics Committee ( #295 ) , the Tanzania National Institute for Medical Research National Ethics Coordinating Committee ( NIMR1HQ/R . 8cNo1 . 11/283 ) , Duke University Medical Center Institutional Review Board ( IRB#Pro00016134 ) , and the University of Otago Human Ethics Committee ( Health ) ( H15/055 ) ., Written informed consent was obtained from all participants or their guardians ., Of 15 , 305 patients admitted and 30 , 413 presenting to the outpatient department , 2 , 962 met eligibility criteria and 1 , 416 ( 47 . 8% ) were enrolled ., Of 1 , 293 participants who completed the risk factor questionnaire and had serum tested , 24 ( 1 . 9% ) met the study criteria for acute leptospirosis , 252 ( 19 . 5% ) were seropositive , and 592 ( 45 . 8% ) were classified as controls ( Fig 1 ) ., The remaining 449 ( 34 . 7% ) were seronegative but provided only a single serum sample and so were excluded from analysis ., The frequency with which participants were predominantly reactive to different serogroups is shown in Table, 1 . Participant characteristics are shown in Table, 2 . Clinicians did not diagnose leptospirosis in any study participant ., Four ( 25 . 0% ) of 16 leptospirosis cases with discharge diagnoses recorded were diagnosed with malaria despite negative blood parasite examinations ., Bivariable logistic regression of individual risk factors are included in S2 Table ., There was a strong association between behaviors involving a single livestock species ., For example having cleaned cattle waste was associated with having fed cattle with an OR 324 . 1 ( 95% confidence intervals 96 . 6–1087 . 0 ) ., There was some association between behaviors involving different livestock species ., For example having cleaned cattle waste was associated with having cleaned goat waste with an OR 28 . 8 , 95% confidence interval 12 . 0–69 . 1 ., There was a small magnitude association between rodent contact variables and livestock related variables ., For example owning cattle was not associated with seeing rodents frequently in the house , compound or fields , and had a low magnitude association with seeing rodents in the kitchen or food store ( OR 1 . 5 , 95 confidence intervals 1 . 1–2 . 1 ) ., Results for the logistic regression analysis of individual behaviors are shown in Table, 3 . On bivariable regression , variables associated with acute leptospirosis included working in rice fields ( OR 14 . 6 , 95% confidence intervals ( CI ) 2 . 9–59 . 5 ) ; cleaning up cattle waste ( OR 4 . 3 , CI 1 . 2–12 . 9 ) ; feeding cattle ( OR 3 . 9 , CI 1 . 3–10 . 3 ) and working as a farmer ( OR 3 . 3 , CI 1 . 3–8 . 2 ) ., Nine ( 42 . 9% ) of 21 experts ( three livestock field officers , four veterinarians , and two zoonotic disease epidemiologists provided internally consistent multiple pairwise rankings of the relative exposure to livestock urine from the behaviors listed in Table, 4 . Four ( 100 . 0% ) of four experts ( one water engineer , one water and sanitation epidemiologist , and two zoonotic disease epidemiologists ) provided consistent multiple pairwise rankings of the relative exposure to surface water ., Three ( 75 . 0% ) of four experts ( one rodent ecologist , one veterinarian , and one zoonotic disease epidemiologist ) provided consistent multiple pairwise rankings of the relative exposure to rodent urine ., The individual behaviors evaluated for each exposure scale and the geometric means of the weights assigned to each are listed in Table, 4 . The results of pairwise comparisons , and calculated weights for each behavior are presented in S3 Table , S4 Table , and S5 Table ., The distributions of participants’ exposure scores on each scale are shown in Fig, 2 . Overall , 534 ( 69 . 3% ) of participants had no evidence of exposure to cattle urine , 563 ( 73 . 0% ) had no exposure to goat urine , 241 ( 31 . 2% ) had no exposure to rodent urine , and 262 ( 34 . 0% ) had no exposure to surface water ., There was limited correlation between cattle urine exposure and both goat urine exposure ( r2 = 0 . 21 ) and pig urine exposure ( r2 = 0 . 04 ) ., In addition there was little correlation between livestock urine exposure scores and rodent urine exposure ( for example , cattle urine exposure and rodent urine exposure , r2 = 0 . 04 ) , livestock exposure scores and surface water exposure ( for example cattle urine and surface water ( r2 = 0 . 02 ) , and between rodent urine exposure and surface water exposure ( r2 = 0 . 02 ) ., All exposure scales had a linear relationship with log odds of acute leptospirosis Our bivariable logistic regression ( Table 5 ) found that increasing exposure to cattle urine ( OR 2 . 3 , CI 1 . 1–4 . 7 ) and exposure to rodents ( OR 1 . 7 , CI 1 . 1–2 . 8 ) were both associated with increased odds of acute leptospirosis ., In multivariable logistic regression ( Table 5 ) , no exposure scale was independently associated with leptospirosis ., As shown in S6 Table , there were no significant interactions ., The largest variance inflation factor was 1 . 33 ., GPS co-ordinates were available for houses of 649 ( 84 . 2% ) participants ., No two or more participants lived at the same household ., Land use designation could be determined from participant’s self-reported village of residence for an additional 79 ( 10 . 2% ) participants ., There was no evidence of clustering in the spatial distribution of cases ., Results of the bivariable logistic regression analysis of geo-referenced variables and rainfall , and acute leptospirosis are shown in Table, 6 . There were no statistically significant associations ., Results of the logistic regression of individual risk factors for Leptospira seropositivity are listed in Table, 7 . Working in rice fields ( OR 3 . 6 , 95% CI 1 . 5–9 . 0 ) ; slaughtering goats ( OR 2 . 3 , 95% CI 1 . 0–4 . 8 ) , working as a farmer ( OR 1 . 8 , 95% CI 1 . 3–2 . 5 ) , and frequently seeing rodents in the kitchen ( OR 1 . 5 , 95% CI 1 . 1–2 . 1 ) were significant risk factors ( p < 0 . 05 ) on bivariable regression ., We fitted an initial multivariable model using the risk factors shown in Table, 8 . As shown in S6 Table , we did not identify any significant interactions between variables ., In our final multivariable model , working as a farmer ( OR 1 . 6 , CI 1 . 1–2 . 3 ) , working in the rice fields ( OR 2 . 7 CI 1 . 0–7 . 2 ) , or seeing rodents in the kitchen ≥ once per week ( OR 1 . 5 , CI 1 . 0–2 . 1 ) were all independent risk factors for Leptospira seropositivity ., Walking barefoot ( OR 0 . 7 , CI 0 . 5–0 . 9 ) and owning dogs ( OR 0 . 6 , CI 0 . 4–1 . 0 ) were associated with reduced odds of Leptospira seropositivity ., The logistic regression models of the exposure scales and Leptospira seropositivity are shown in Table, 9 . Increasing exposure to rodent urine ( OR1 . 2 , CI 1 . 0–1 . 5 ) was associated with Leptospira seropositivity on bivariable logistic regression , but not on multivariable regression ., Results of the bivariable logistic regression analysis of rainfall and Leptospira seropositivity are shown in Table, 10 . There was an inverse association with mean annual rainfall >1 , 600mm per year ( OR 0 . 56 , 95% CI 0 . 33–0 . 93 ) ., We fitted an initial multivariable model using household elevation , mean annual rainfall , maximum daily rainfall in the preceding 30 days , and total rainfall in the preceding 30 days ., The final model contained elevation ( OR 0 . 99 per 10m , CI 0 . 98–1 . 0 , p = 0 . 06 ) , and total rainfall in the preceding 30 days ( OR 1 . 2 per 100mm , CI 0 . 95–1 . 5 , p = 0 . 13 ) but neither association was statistically significant ., An analysis of the risk factors for seropositivity against Leptospira serogroup Icterohaemorrhagiae is included as S6 Table ., We identified multiple associations between exposure to cattle and acute leptospirosis , suggesting that cattle may be important sources of human leptospirosis in northern Tanzania ., We also identified work in rice fields as an important risk factor for human leptospirosis ., These findings must be interpreted with caution , as they were based on a small number of cases , and were present in only bivariable regression ., Despite this , our findings have implications for the control and prevention of leptospirosis in Tanzania ., On bivariable regression , exposure to cattle was associated with acute human leptospirosis both when we evaluated individual behaviors and scales of cumulative exposure to cattle urine ., These findings support other data from northern Tanzania that indicate that livestock may be an important source of human leptospirosis 31 ., Among cattle slaughtered for meat in the Moshi area , 7 . 6% of cattle tested were carrying pathogenic Leptospira spp ., in their kidneys 31 ., Furthermore , seroreactivity against serogroups Australis and Sejroe , the two dominant serogroups among human cases in our study , was also frequently observed among cattle slaughtered for meat in the Moshi area in 2014 12 ., Our findings are also consistent with studies examining risk factors for Leptospira seropositivity in Africa ., Leptospira seropositivity was common among abattoir workers in Kenya and Tanzania 11 , 27 ., In rural Uganda , livestock skinning was reported as a risk factor for seroreactivity and human seropositivity to livestock-associated Leptospira serovars was common 28 ., In a global context , cattle have also been identified as a key risk factor in other rural livestock-farming communities in Central America and South Asia 14 , 15 , suggesting that strategies to reduce either livestock leptospirosis or transmission of leptospirosis from livestock to humans may be important global public health interventions ., Rodent exposure is an important risk factor for leptospirosis in the tropics , particularly in urban areas of Asia and South America 4 , 29 , 30 ., In our study , an increasing score on the exposure to rodent urine scale was associated with acute leptospirosis in bivariable regression ., However , the only individual component of the scale for which we found an association on bivariable regression was smallholder farming ., Since smallholder farming may involve substantial exposure to both livestock and rodents , and other rodent related variables were not associated with leptospirosis the role of rodents in this association is uncertain ., We also found that frequently sighting rodents in the kitchen or food store was associated with Leptospira seropositivity ., Rodents could transmit leptospirosis to humans , or act as a reservoir that transmit Leptospira to livestock ., However , recent work in the Kilimanjaro Region found no evidence of Leptospira urinary shedding , or renal infection among 393 wild rodents 31 Although practiced by few participants , we found an association between working in rice fields , and both acute leptospirosis and Leptospira seropositivity ., In some areas of northern Tanzania rice farming is practiced intensively , and there are active efforts to increase irrigated , continuously flooded rice farming across Tanzania 32 ., In Asia rice farming is an established risk factor for leptospirosis ., In Asia humans are infected through prolonged contact with water that may be contaminated by infected animal hosts 4 , 29 ., Further work is needed to evaluate possible sources of contamination of rice paddies in Tanzania and promote personal protective measures among rice farmers ., We did not find associations between acute leptospirosis and rainfall , or environmental risk factors around the home ., The small number of cases available for analysis , and the relative lack of resolution of geo-referenced data meant that this result must be interpreted with caution ., The lack of association with heavy rainfall differs from findings of studies from other locations 33 , 34 ., We found that seropositivity was associated with lower elevation and lower rainfall ., While we did not have household level slope data , the topography of the study area includes steeply sloping terrain on the flanks of Mount Kilimanjaro that may not favor surface water accumulation ., The lack of association between leptospirosis and home location may indicate that the workplace is an important site for infection 9 , 11 ., Future studies should collect data regarding workplace location ., Clinicians did not diagnose leptospirosis during the study period , and over-diagnosis of malaria was common ., At the time of our study , there were no locally available , accurate diagnostic tests for leptospirosis ., In addition , despite the high incidence in the region , clinician awareness of leptospirosis and other zoonotic diseases remains low 35 ., This highlights the need for clinician education and evaluations in Africa of inexpensive point-of-care diagnostic tests ., We found that risk factors and the pattern of predominant reactive serogroups among leptospirosis cases was markedly different from those in seropositive individuals , for whom the febrile illness concurrent with enrollment was unlikely to be leptospirosis ., In particular , reactivity to serogroup Icterohaemorrhagiae was common among seropositive participants , but there were few acute cases associated with this serogroup ., These results may indicate that a serovar from the Icterohaemorrhagiae serogroup was circulating in this region 36 , causing only mild disease not requiring tertiary medical care ., Elsewhere , a difference in severity of disease has been linked to variability of infecting Leptospira species 37 , Alternatively , the presence of Icterohaemorrhagiae seropositivity but absence of acute cases could indicate historic circulation of this serogroup that has since declined ., Other results suggest that leptospirosis has a dynamic epidemiology in this area with the emergence and decline of specific serovars over time 3 ., Cross reactivity between serogroups , and non-specific reactivity are other possible explanations 38 ., Our study had several limitations ., First , the prevalence of acute leptospirosis was lower than anticipated 8 , potentially curtailing our ability to detect important associations ., Conversely , associations of individual activities and leptospirosis identified by this study were sometimes based on only a few cases and should be interpreted with caution , especially given the multiple statistical tests ., In addition , changes in leptospirosis incidence in the study area might also reflect changes in predominant sources and modes of transmission over time 3 ., Second , the associations for acute leptospirosis were seen only on bivariable analysis , and these associations may be due to confounding from unobserved behaviors ., Due to the complex interconnection between individual behaviours , we also consider that confounding may influence the multivariable logistic regression model of individual behaviours and Leptospira seropositivity ., For example , the inverse association of walking barefoot and leptospirosis is puzzling , and we think it is likely to be influenced by an association with some protective factor , despite not identifying such an association among the behaviors we investigated ., Diagnostic test limitations may have also introduced classification errors of participant cases or controls into our analysis ., Leptospirosis is notoriously difficult to diagnose , particularly in the acute stages of illness and all currently available diagnostic tests for leptospirosis , including MAT 39 , are imperfect ., The sensitivity of MAT on paired serum samples is approximately 80% and the specificity close to 100% 40 ., Specifically , not all participants with leptospirosis will seroconvert 40 , and it is not possible to differentiate between historic and recent infection based on a single high titer 41 ., We chose MAT for our case definitions since MAT on paired serum samples , while imperfect , remains the reference standard 40 ., Furthermore , culture , nucleic acid amplification and point-of-care IgM serology lack sensitivity in our setting 12 , 42 , 43 , and reports from other settings have been mixed 39 , 44–46 ., Our MAT panel comprising 20 serovars covered the major Leptospira serogroups that cause human disease , and all those within which African isolates are grouped 1 ., We did not use locally isolated serovars and this may have influenced identification of cases ., However , studies on the use of local isolates in MAT reference panels have shown that they do not necessarily perform better than other serovars from the same serogroup 47 , 48 ., Our analysis of acute leptospirosis was limited to cases across all serogroups ., We acknowledge that risk factors may vary by infecting serovar , and pan-serogroup analyses may mask important associations ., We developed scales for use in our analyses for dimension reduction due to the unanticipated low number of cases ., We suggest that cumulative exposure scales may have a future role in assessing sources of acute leptospirosis , as they allow assessment of cumulative exposure that may be important in assessing individual risk of disease ., The analytic hierarchy process was an appropriate method of creating these scales , as it is an effective tool for quantifying multi-dimensional qualitative knowledge 24 ., While we acknowledge that there is scope to improve our cumulative exposure scales , our scales that quantify expert opinion offer more biologically plausible groupings than statistical methods of dimension reduction ., Key areas for future development of cumulative exposure scales are to validate them across multiple groups of experts , and to formally compare their effectiveness against purely statistical dimension reduction ., Since our questionnaire sought exposures over a 30 day period , recall bias may have influenced our findings ., Finally , we enrolled only 47 . 1% of eligible patients ., We found no bias towards particular ethnic or occupational groups ., However , we cannot rule out the possibility that the enrollment pattern influenced our results ., Despite these limitations , the consistency of the association of the livestock related variables strengthens our confidence in the interpretation of their role in transmitting leptospirosis to people in our region ., Our results have implications for control of leptospirosis ., Transmission of leptospirosis within rice fields , and from livestock to people is amenable to control through personal protective equipment for those performing high risk activities 49 ., In addition , Leptospira vaccines are available for use in livestock against some Leptospira serovars ., In some countries such vaccines have contributed to successful control of leptospirosis 49 ., However , before a vaccination program is considered it is essential to understand reservoir structure and predominant infecting serovars ., Our study identifies associations between cattle contact and work in rice fields with acute leptospirosis ., Our findings suggest that cattle may be a source of human leptospirosis in northern Tanzania ., Further work is needed to determine if these findings are stable over time , and to investigate the link by isolating infecting serovars from humans and animal hosts ., The development of local MAT capacity , or use of nucleic acid amplification or point-of-care IgM tests that have sufficiently high sensitivity would enable real-time diagnosis and allow testing of potential animal hosts living in proximity to humans with acute leptospirosis ., Nonetheless , our findings suggest that control of Leptospira infection in livestock could play a role in preventing human leptospirosis in Africa .

Introduction, Methods, Results, Discussion

Leptospirosis is a major cause of febrile illness in Africa but little is known about risk factors for human infection ., We conducted a cross-sectional study to investigate risk factors for acute leptospirosis and Leptospira seropositivity among patients with fever attending referral hospitals in northern Tanzania ., We enrolled patients with fever from two referral hospitals in Moshi , Tanzania , 2012–2014 , and performed Leptospira microscopic agglutination testing on acute and convalescent serum ., Cases of acute leptospirosis were participants with a four-fold rise in antibody titers , or a single reciprocal titer ≥800 ., Seropositive participants required a single titer ≥100 , and controls had titers <100 in both acute and convalescent samples ., We administered a questionnaire to assess risk behaviors over the preceding 30 days ., We created cumulative scales of exposure to livestock urine , rodents , and surface water , and calculated odds ratios ( OR ) for individual behaviors and for cumulative exposure variables ., We identified 24 acute cases , 252 seropositive participants , and 592 controls ., Rice farming ( OR 14 . 6 ) , cleaning cattle waste ( OR 4 . 3 ) , feeding cattle ( OR 3 . 9 ) , farm work ( OR 3 . 3 ) , and an increasing cattle urine exposure score ( OR 1 . 2 per point ) were associated with acute leptospirosis ., In our population , exposure to cattle and rice farming were risk factors for acute leptospirosis ., Although further data is needed , these results suggest that cattle may be an important source of human leptospirosis ., Further investigation is needed to explore the potential for control of livestock Leptospira infection to reduce human disease .

Leptospirosis is an under-recognized but important cause of febrile illness and death in Africa ., The bacteria that cause leptospirosis have their usual life cycle in animals; humans are infected as accidental hosts ., There is considerable variation between countries as to which reservoir animals and human activities are important for transmission of leptospirosis to humans ., In many tropical countries flooding and rodents are the dominant sources of human infection ., However , in Africa it is unknown which sources of leptospirosis are most responsible for human infection and what behaviors put people at risk for infection We performed a prospective cross-sectional study , to identify risk factors for acute leptospirosis and sources of human infection ., We identified contact with cattle and work in rice fields as risk factors for acute leptospirosis ., Our findings indicate that cattle may be an important source for human leptospirosis , and therefore control of leptospirosis in livestock may help prevent leptospirosis in people ., Further work is needed to isolate Leptospira from humans and livestock ., Rice farming was an uncommon activity in our study , but strongly associated with acute leptospirosis ., Efforts are warranted to prevent infection in rice farmers living in Africa .

livestock, medicine and health sciences, surface water, leptospira, body fluids, pathology and laboratory medicine, ruminants, pathogens, tropical diseases, microbiology, vertebrates, animals, mammals, urine, bacterial diseases, rice, experimental organism systems, neglected tropical diseases, plants, bacteria, bacterial pathogens, research and analysis methods, hydrology, infectious diseases, zoonoses, grasses, medical microbiology, microbial pathogens, goats, leptospirosis, agriculture, rodents, eukaryota, plant and algal models, anatomy, physiology, earth sciences, biology and life sciences, amniotes, organisms

journal.pgen.1008179

Suppressor mutations in ribosomal proteins and FliY restore Bacillus subtilis swarming motility in the absence of EF-P

Translation elongation factor P ( EF-P ) has been shown to alleviate ribosome pausing at consecutive proline residues ( XPPX motifs ) in Bacteria and Eukaryotes 1–3 ., While EF-P is widely distributed and often required for rapid growth , the reason it is highly conserved is unknown 4–6 ., In Escherichia coli , the manner in which EF-P promotes growth is thought to be pleiotropic by enhancing translation of multiple proteins that contain XPPX motifs 7 ., Systems-level approaches , however , show that not all XPPX motifs induce ribosome pausing in the absence of EF-P , and that even fewer of those pauses result in decreased protein expression 8–10 ., Thus , EF-P pleiotropy may be limited ., Consistent with limited pleiotropy , the phenotypes of an efp mutant in E . coli are conditional , and are suppressed when translation rates are reduced 11 ., Finally , apparent pleiotropy is organism-specific as growth defects in Bacillus subtilis efp mutants are negligible , even under conditions of high translation 12 ., Instead , EF-P in B . subtilis is specifically required for swarming motility 12 , 13 ., Swarming motility is a flagellar-mediated form of movement on surfaces and often requires an increase in flagellar number relative to swimming in liquid 14–18 ., Increasing flagellar number is complicated as flagella are trans-envelope nanomachines that require hierarchical assembly of dozens of subunits in precise stoichiometry 19 , 20 ., At the core of each flagellum is a type III secretion apparatus and early-class secretion is activated after the flagellar base plate and C-ring rotor are fully assembled 21–24 ., Early class secreted products span the cell envelope to form an axle-like rod and universal joint-like hook 25–27 ., Once the hook is polymerized to a certain length , the secretion specificity changes , the late-class sigma factor σD is activated , and late-class flagellar proteins are exported to assemble the filament 28–30 ., Mutants that decrease the efficiency of flagellar expression or assembly abolish swarming motility and can do so at any step in the hierarchy 24 , 31 ., The mechanism by which EF-P specifically activates swarming motility in B . subtilis is unknown ., Here we show that B . subtilis EF-P functions in a manner similar to that reported in other organisms and alleviates ribosome pausing at a subset of XPPX motifs ., Flagellar assembly requires translation of a large number of proteins , and efp mutants were found to have a reduced number of flagella ., Cells lacking EF-P were defective in hook completion due to translational pausing at one particular XPPX motif within the basal body component FliY ., FliY in turn was necessary to activate early-class secretion ., EF-P structurally resembles a tRNA and while it is thought to promote translation entropically , the mechanism is poorly understood 32 ., Genetic analysis reported here further indicates that mutations in a wide variety of conserved genes related to the ribosome suppress the absence of EF-P , which may aid in the understanding of the EF-P mechanism ., The reason that EF-P is required for swarming motility is unknown ., Cells mutated for the master activator of flagellar biosynthesis SwrA lack swarming motility due to reduced transcription of the fla/che flagellar operon and a proportional reduction in flagellar number ( Fig 1A ) 31 , 33 ., To determine if cells mutated for efp also had reduced flagellar number , a functional variant of the filament protein Hag that could be labeled with a fluorescent dye ( hagT209C ) was introduced into various genetic backgrounds 34 ., Qualitatively , the efp mutant appeared to have fewer filaments than wild type and more closely resembled cells mutated for the master activator of flagellar biosynthesis SwrA ( Fig 2A ) ., As swarming motility requires an elevated number of flagella per cell , we infer that the decrease in flagellar number likely accounts for the swarming defect observed upon mutation of efp ., Flagella are assembled in a stepwise fashion , with the basal body assembled first , followed by the rod-hook , and finally the filament ., Thus , a decrease in flagellar filament number could result from a decrease in either the number of hooks or basal bodies ., To determine if mutation of efp affected hook and/or basal body number , functional variants of the hook protein FlgE ( FlgET123C ) or basal body C-ring subunit FliM ( FliM-GFP ) were introduced into various genetic backgrounds , and fluorescent puncta were quantified with 3D structured illumination microscopy 31 , 35 ., Mutation of efp resulted in a decrease in hook number compared to wild type , but the number of basal bodies remained the same ( Fig 2A–2C ) ., By contrast and consistent with previous reports , mutation of swrA resulted in a decrease in both basal body and hook numbers 24 , 31 ( Fig 2A–2C ) ., We conclude that EF-P is required for completion of a step in flagellar assembly between incorporation of FliM into the C-ring and completion of the hook ., A defect in hook completion prevents export of the anti-sigma factor FlgM , resulting in a decrease in expression directed by RNA polymerase and the sigma factor σD ( Fig 1A ) 30 , 36 ., To determine whether the efp mutant was defective in σD-dependent gene expression , a σD-dependent transcriptional reporter in which the promoter of flagellin Phag fused to β-galactosidase ( Phag-lacZ ) was inserted at an ectopic location in various genetic backgrounds 33 , 37 ., Cells mutated for either swrA or efp showed a decrease in expression from the Phag promoter , and expression was partially restored to either swrA or efp mutants by mutation of flgM ( Fig 3 ) ., SwrA and EF-P appeared to enhance Phag-lacZ expression by different pathways , however , as an efp swrA double mutant synergized to decrease promoter activity ( Fig 3 ) ., Further , expression in an efp swrA flgM triple mutant remained low relative to either the swrA flgM or the efp flgM double mutants ( Fig 3 ) ., We conclude that EF-P promotes hook completion and σD-dependent gene expression by a mechanism unrelated to SwrA activation of the Pfla/che promoter ., One way that EF-P could promote flagellar biosynthesis is if it alleviated ribosome pausing as it does in a number of other organisms 1–3 ., To determine whether EF-P in B . subtilis alleviates ribosome pausing , the ribosome pause sites of wild type and an efp mutant were compared ., In brief , mRNA fragments protected by ribosome footprinting were purified , subjected to Illumina sequencing , and the codons in the ribosomal P-site were identified using the 3’ end assignment method 10 ., Each codon in the genome was then assigned a pause score , defined as the number of reads that mapped to a particular position divided by the average read density for the corresponding gene ( S3 Table ) ., Approximately 250 codons in 180 genes had a pause score that was at least 10-fold higher in the efp mutant compared to WT ( S4 Table ) ., In the absence of EF-P , proline codons were enriched in both the ribosome P-site and E-site , and the tripeptides encompassing the “-2” , “E” , “P” , and “A” sites showed an enrichment of pausing at PPX and XPP motifs ( Fig 4A–4C , S5 Table ) ., We conclude that in the absence of EF-P , B . subtilis ribosomes paused more frequently at XPPX motifs , consistent with that reported in other organisms ., The 180 genes that experienced increased ribosome pausing in the absence of EF-P were predicted to be diverse in function ( S6 Table ) ., Five genes with EF-P-alleviated pause sites appeared to be directly related to the flagellum: fliI ( encoding the secretion accessory protein FliI ) , fliF ( encoding the basal body base plate FliF ) , motB ( encoding the stator component MotB ) , sigD ( encoding the alternative sigma factor σD ) , and fliY ( encoding the C-ring component FliY ) , ( Fig 1B , S4 Table ) 38 ., FliF , σD , and FliY are all required for hook completion and their translational impairment might be consistent with the efp mutant phenotype but it wasn’t clear which , or how many , of the sites were directly responsible 24 , 35 ., Moreover , EF-P-alleviated pause sites were observed in several essential genes , even though mutation of efp did not substantially reduce growth rate ( S4 Table ) 12 , 39 ., Thus , as ribosome pauses were found in at least 5 genes known to be involved in motility ( not including genes of unknown function ) , and the location of EF-P-alleviated translational pausing was not necessarily predictive of phenotype , we concluded that ribosome profiling alone was insufficient to identify the hook-promoting EF-P target ., As an alternative approach to determine how EF-P increased hook number , spontaneous suppressors were isolated that restored swarming motility to an efp mutant ., Cells mutated for efp were initially incapable of migrating from the site of inoculation on swarming motility agar ( Fig 5A and 5B ) , but after prolonged incubation , second-site mutations that suppressed the need for EF-P emerged from the central colony as motile flares ., Twenty-four suppressors of efp ( soe ) mutations were independently isolated and each suppressor resulted in a partial restoration of swarming motility ., The location of each suppressor mutation in the genome was identified using a combination of SPP1-mediated transduction linkage mapping and whole genome sequencing ., The mutations were organized into 6 different classes based on their chromosomal location ( Table 1 ) ., Translation elongation factor P ( EF-P ) is conserved in all domains of life and has been shown to alleviate ribosome pausing at a subset of sequences encoding tandem proline residues ( XPPX motifs ) 8 , 10 , 58 ., In many organisms , EF-P is required for growth , presumably because it enhances translation of one or more XPPX-containing essential proteins , the ValS aminoacyl-tRNA synthetase in particular 4–6 , 59 ., In B . subtilis however , efp mutants have negligible growth defects and instead are specifically incapable of a flagellar-mediated surface motility called swarming 12 , 13 , 39 ., We show here that B . subtilis EF-P alleviates ribosome pausing at XPPX motifs in a manner nearly indistinguishable from other organisms ., We further attribute the efp mutant swarming defect to a decrease in flagellar number at the level of flagellar hook biosynthesis , and we analyze spontaneous suppressor mutants that restore swarming motility ., Many of the suppressors were in ribosome subunits or ribosome-associated factors and were likely compensatory ., One suppressor mutant , however , was in the motility target of EF-P and changed a ribosome pause-inducing SPP motif to APP in the flagellar C-ring component , FliY ., FliY is homologous to the protein FliN found in the flagellar C-ring of other bacteria , and the efp mutant flagellar assembly defect is consistent with FliY being a motility-related EF-P target 24 , 60 ., A fliY deletion does not perfectly phenocopy mutation of efp , as FliY is necessary for flagellar C-ring assembly and all forms of flagellar motility , whereas the efp mutant has wild type basal body numbers and can swim but not swarm ( Fig 2A ) 13 ., Moreover , the fliY mutant lacks flagellar filaments whereas the efp mutant does not , perhaps because FliY , like FliN , may be a docking point for the late class flagellar secretion protein FliH ( Fig 2 ) 61 , 62 ., The absence of EF-P instead increases ribosome pausing and decreases FliY copy number , thereby reducing the frequency of flagella that complete basal body assembly and activate early-class type III secretion 24 ., Why EF-P is needed to specifically relieve translational pausing of FliY is unclear ., The need for EF-P may be unavoidable as the SPP motif falls within a highly conserved sequence of residues that are nearly invariant ., The FliYsoe allele in otherwise wild type cells , however , exhibited nearly wild type levels of swarming motility suggesting that an EF-P-independent variant is indeed tolerated ( Fig 1C , S1F Fig ) ., Alternatively , EF-P pausing relief may play a regulatory role ., While EF-P in B . subtilis is constitutively expressed , it is post-translationally modified by 5-aminopentanolylation which is predicted to be built through the sequential maturation of at least 3 EF-P modification intermediates 63 ., Moreover , previous work has shown that the modification state of B . subtilis EF-P alters its activity and therefore may represent a method of regulating EF-P function 39 , 63 ., We note that while FliN of E . coli does not encode an XPPX motif , translational pausing in the absence of EF-P is nonetheless conserved at a series of four consecutive valine residues , perhaps indirectly due to increased ribosome pausing in ValS and a concomitant decrease in tRNAs charged with valine ( S4 Fig ) 10 ., Suppression of the efp mutant swarming defect could be achieved through mutation of 7 additional loci , many of which are broadly conserved and could be readily related to the translational machinery ( S2 Fig , S7 Table ) ., The location of these additional suppressors may provide insight into the mechanism by which EF-P promotes translation in diverse organisms ., Homologs of YeeI are highly conserved and poorly studied , but one YeeI homolog in humans , TACO1 , has been implicated in activating the translation of Cox1 , which contains 4 XPPX motifs 41 ., YdiF is a broadly conserved member of the ABC-F family of ATPases which comprises many proteins known to interact with the ribosome such as EF-3 in Eukaryotes and EttA in E . coli 43 ., YacO is homologous to RlmB in E . coli , a highly conserved protein that methylates the 23S rRNA guanosine G2251 within the ribosomal peptidyltransferase domain 64 ., Rae1 has been recently shown to act as a ribosomal A-site endoribonuclease , and it was hypothesized that ribosome stalling may increase its access to its mRNA substrate and thereby increase its activity 42 ., Finally , S3 and S10 are components of the small subunit of the ribosome itself: S3 is involved in mRNA processivity and S10 is involved in binding to the P-site tRNA 48 , 50 ., Further , the residue altered by soe24 ( S10M88R ) has been implicated in the direct interaction with the last protein identified by efp suppressor analysis , NusG 49 ., NusG couples transcription and translation in E . coli by binding both RNA polymerase and the leading ribosome on the transcript to promote transcriptional elongation 47 , 49 ., In B . subtilis , however , NusG is thought to do the opposite and promote transcriptional pausing 65 , 66 ., In E . coli , NusG also binds to the ribosome but whether it does so in B . subtilis and how the nusGsoe allele suppresses the efp swarming defect is unclear 47 , 49 ., NusGsoe appears to be a gain-of-function allele that does not increase FliY protein levels but rather increases the expression of σD-dependent late-class flagellar genes , including the flagellar filament ( Fig 3 , Fig 8 ) ., The increase in σD -dependent gene expression , however , is likely an indirect effect of suppression as artificial activation of σD was insufficient to restore swarming to the efp mutant ( S1E Fig ) ., While the mechanism by which NusGN21S suppresses the efp mutant swarming defect is unknown , it appears to operate in parallel to the alleviation of ribosome pausing in FliY , as the nusGsoe and fliYsoe alleles synergized to enhance swarming in the efp mutant background ( Fig 5H ) ., The majority of flagellar genes including both fliY and sigD are encoded on what is thought to be a single transcript from the 27kb 32 gene fla/che operon ( Fig 1 ) 67–70 ., Perhaps NusG is somehow involved in the expression of long transcripts ., Ultimately , EF-P alleviates ribosome pausing at some but not all XPPX motifs , and the context that causes a particular primary sequence to trigger stalling is unclear ., For example , cells fail to swarm when ribosomes pause at an SPP motif in the fliY transcript and swarming is restored by substitution to a APP motif , another site that also experiences strong pausing elsewhere in the genome ., Moreover , even in situations where ribosome pausing is severe , there may or may not be phenotypic consequences ., For example , ribosomes also pause at and accumulate upstream of a PPP motif in the valS transcript but little to no growth defect is observed , and unlike the case in E . coli , pauses at valine residues are not enriched in B . subtilis ( S5 Fig , Compare Fig 3B to S4D Fig ) ., Thus , one cannot predict whether ribosomes pause at particular motifs by bioinformatics , and it may be difficult to predict the phenotypes of efp mutants simply from ribosome profiling data sets ., Our work supports previous observations in E . coli that the phenotypic effect of EF-P may be most significant for pauses in proteins for which relative stoichiometry is important ., For example , EF-P-alleviated pausing has been shown to be important for the maintenance of subunit ratio for the F1F0 ATPase 8 , 71 ., Moreover , EF-P relieves translational pausing within CadC , a transcriptional activator that is antagonized by direct interaction with LysP 3 ., Thus translational pausing creates a stoichiometric imbalance and results in constitutive antagonism of CadC and deactivation of the CadC transcriptional target 3 ., Here we provide evidence that EF-P supports synthesis of the protein FliY , which when in stoichiometric deficiency limits the cells ability to complete flagellar basal body biosynthesis , increase flagellar number , and perform swarming motility ., We broadly speculate that biological systems which depend on stoichiometry may be particularly sensitive to translational pausing and thus display enhanced phenotypic dependency on EF-P ., B . subtilis and E . coli strains were grown in lysogeny broth ( LB ) ( 10 g tryptone , 5 g yeast extract , 5 g NaCl per L ) or on LB plates fortified with 1 . 5% Bacto agar at 37°C ., When appropriate , antibiotics were included at the following concentrations: 100 μg/ml ampicillin , 10 μg/ml tetracycline , 100 μg/ml spectinomycin , 5 μg/ml chloramphenicol , 5 μg/ml kanamycin , and 1 μg/ml erythromycin plus 25 μg/ml lincomycin ( mls ) ., Isopropyl β-D-thiogalactopyranoside ( IPTG , Sigma ) was added to the medium at the indicated concentration when appropriate ., Strain construction and suppressor isolation details are described in the S1 Text ., Strains used in this study are listed in Table 2 , plasmids are listed in S9 Table , and primers are listed in S10 Table ., For quantitative swarm assays , strains were grown to mid log phase ( OD600 0 . 3–1 . 0 ) concentrated to an OD600 of 10 in PBS pH 7 . 4 ( 0 . 8% NaCl , 0 . 02% KCl , 100 mM Na2HPO4 , and 17 . 5 mM KH2PO4 ) plus 0 . 5% India ink ., LB plates fortified with 0 . 65% agar were dried for 10 min open-faced in a laminar flow hood and subsequently inoculated by spotting 10 uL cell resuspensions onto the center of the plate ., Plates were dried an additional 10 min open-faced in a laminar flow hood and then incubated at 37°C in a humid chamber ., Swarm radius was measured along the same axis every 30 minutes ., Images of swarm plates were obtained by toothpick-inoculating a colony into the center of an LB plate fortified with 0 . 65% agar ., Plates were dried open-faced in a laminar flow hood for 12 min and incubated at 37°C in a humid chamber for 16 hrs ., Images were taken using a BioRad Gel Doc ., Fluorescence micrographs were generated with a Nikon 80i microscope along with a phase contrast objective Nikon Plan Apo 100X and an Excite 120 metal halide lamp ., FM4-64 was visualized with a C-FL HYQ Texas Red Filter Cube ( excitation filter 532–587 nm , barrier filter >590 nm ) ., GFP and Alexa Fluor 488 were visualized using a C-FL HYQ FITC Filter Cube ( FITC , excitation filter 460–500 nm , barrier filter 515–550 nm ) ., Images were captured with a Photometrics Coolsnap HQ2 camera in black and white and subsequently false colored and superimposed using Metamorph image software ., For fluorescent microscopy of flagellar filaments and hooks , 1 . 0 ml of broth culture was harvested at mid-log phase , resuspended in 50 μl of PBS buffer containing 5μg/ml Alexa Fluor 488 C5 maleimide ( Molecular Probes ) , incubated for 2 min at room temperature , and washed once in 1 . 0 ml of PBS buffer ., The suspension was pelleted , resuspended in 30 μl of PBS buffer containing 5 μg/ml FM 4–64 ( Invitrogen T13320 ) , and incubated for 2 min at room temperature ., The cells were pelleted , resuspeneded in 30 μl PBS buffer , and were observed by spotting 5 μl of suspension on a microscope slide and immobilized with a poly-L-lysine-treated glass coverslip ., For fluorescent microscopy of flagellar basal bodies , 1 . 0 ml of broth culture was harvested at mid-log phase , resuspended in 30 μl of PBS buffer containing 5 μg/ml FM 4–64 , and incubated for 2 min at room temperature ., The cells were pelleted , resuspeneded in 30 μl PBS buffer , and were observed by spotting 5 μl of suspension on a microscope slide and immobilized with a poly-L-lysine-treated glass coverslip ., For super-resolution microscopy , the OMX 3D-SIM Super-Resolution system with a 1 . 42-numerical-aperture ( NA ) Olympus 60X oil objective was used ., FM4-64 was observed using laser line 561 and emission filter 609 nm to 654 nm , and GFP ( along with Alexa Fluor 488 ) was observed using laser line 488 nm and emission filter 500 nm to 550 nm ., Images were captured using PCO Edge 5 . 5 sCMOS cameras , processed using SoftWorx imaging software , and analyzed using Imaris software ., Strains were grown in LB at 37°C to OD600 0 . 7–1 . 0 and 1 mL was harvested by centrifugation at 18 , 000 xg ., The pellet was resuspended in 1 mL Z-buffer ( 40 mM NaH2PO4 , 60 mM Na2HPO4 , 10 mM KCl , 1 mM MgSO4 , and 38 mM 2-mercaptoethanol ) , 200 μg lysozyme was added , and cells were lysed at 30°C for 15 min ., To obtain optical density readings within the linear range , each lysate was appropriately diluted to a final volume of 500 μL in Z-buffer ., The reaction was started by the addition of 100 μL start buffer ( 4 mg/mL ortho-Nitrophenyl-β-galactoside in Z-buffer ) , and incubated at 30°C ., The reaction was stopped by the addition of 250 μL 1M Na2CO3 and the OD420 of the mixture was measured ., The β-galactosidase-specific activity was calculated according to the equation ( OD420 * Dilution factor * 1000 ) / ( time * OD600 ) ., Average β-galactosidase activity and the standard deviations for all experiments can be found in S10 Table ., The expression constructs for His-SUMO-FliY ( pDP288 ) and His-SUMO-FliG ( pKB43 ) were introduced into E . coli Rosetta gami II cells and grown at 37°C in Terrific broth ( 12 g tryptone , 24 g yeast extract , 4 ml glycerol , 2 . 31 g monobasic potassium phosphate and 12 . 54 g dibasic potassium phosphate per liter ) to mid-log phase ., 1 mM IPTG was then added and the culture was grown overnight at 16°C ., Cells were pelleted , resuspended in lysis buffer ( 50 mM Na2HPO4 and 300 mM NaCl ) and lysed using an Avestin EmulsiFlex-C3 at approximately 15 , 000 psi ., Cell debris was pelleted by centrifugation at 31 , 000 xg for 30 min and Ni-nitrolotriacetic acid resin ( Novagen ) was added to the clarified supernatant ., The resin-lysate mixture was incubated at 4°C for 3 hrs ., The resin was applied to a 1-cm separation column ( Bio-Rad ) , washed twice with 10 mL lysis buffer , and once with 10 mL wash buffer ( 50 mM Na2HPO4 , 300 mM NaCl , and 30 mM imidazole ) Protein was eluted with lysis buffer containing 100 mM imidazole ., To cleave the His-SUMO tag from the purified protein , ubiquitin ligase/protease was added and the reaction was incubated at room temperature for 3 hrs ., To remove remaining uncleaved protein or free His-SUMO from the cleavage reaction , Ni-nitrolotriacetic acid resin ( Novagen ) was added and incubated at 4°C for 1 h ., The resin was pelleted by centrifugation and the supernatant , containing untagged FliY or FliG , was dialyzed into PBS pH 7 . 4 plus 10% glycerol and stored at -20°C ., One milligram of purified FliY protein was sent to Cocalico Biologicals Inc . for serial injection into a rabbit host for antibody generation ., Anti-FliY serum was mixed with FliY-conjugated Affi-Gel-10 resin ( Bio-Rad 1536099 ) and incubated overnight at 4°C ., The resin was packed onto a 1-cm column ( Bio-Rad ) and then washed with 100 mM glycine ( pH 2 . 5 ) to release the antibody and immediately neutralized with 2M Tris base ., The purification of the antibody was verified by SDS-PAGE ., Purified anti-FliY antibody was dialyzed into PBS–50% glycerol and stored at -20°C ., Ribosome profiling libraries were prepared as described previously with minor modifications 75 ., 300 mL LB exponential phase cultures ( OD600 0 . 3–0 . 4 ) grown at 37°C were subjected to rapid filtration and subsequently flash frozen in liquid nitrogen ., Cells were lysed in 650 μL lysis buffer ( 10 mM MgCl2 , 100 mM NH4Cl , 5 mM CaCl2 , 20 mM Tris pH 8 . 0 , 0 . 1% NP-40 , 0 . 4% Triton X-100 , 0 . 1 units/μL RNase free DNase I ( Invitrogen AM2222 ) , 0 . 5 units/μL Superase-In ( Invitrogen AM2696 ) ) using a Spex 6875 freezer mill set to 10 cycles of 2 min runs at 15 cps separated by 2 min rests ., Following lysis , 25 A260 units of lysate were digested with 1500 units of S7 micrococcal nuclease ( Roche 10107921001 ) for 1 hr at room temp after which the reaction was quenched by the addition of EGTA to a final concentration of 6 mM ., The digested lysate was then applied to a 10%-50% sucrose gradient and centrifuged in a Ti-40 rotor at 35 , 000 rpm for 2 . 5 hrs at 4°C ., 700 μL of fractions containing 70S ribosomes were denatured in 1% SDS and extracted once with an equal volume of 75°C acid phenol , once with an equal volume of room temp acid phenol , and RNA was precipitated with isopropanol ., The precipitant was resuspended in 12 μL H2O and 25 μg RNA was mixed with 2X loading dye ( 10 mM EDTA , 30 μg/mL bromophenol blue , and 98% formamide ) and resolved on a 15% polyacrylamide TBE Urea gel ., After staining the gel in SYBR Gold ( Sigma S11494 ) for 3 min , products between ~15 and 40 bp were excised using the 10 bp O’range ladder as the standard ( Thermo Scientific SM1313 ) and subsequently gel extracted ., RNA was resuspended in H2O and the 3’ ends were dephosphorylated with T4 poly-nucleotide kinase ( Lucigen 30061–1 ) at 37°C for 1 hr ., RNA was precipitated in isopropanol and ligated to 1 μL 1 μg/μL Linker 1 ( IDT /5rApp/CTGTAGGCACCATCAAT/3ddC/ ) with T4 RNA Ligase 2 , truncated ( NEB M0242S ) in a 50 μL reaction at 25°C for 2 . 5 hrs ., Products were precipitated , mixed in 2X loading dye and resolved on a 15% polyacrylamide TBE Urea gel ., After staining the gel in SYBR Gold for 3 min , products between 30 and 100 bp were excised using the 10 bp O’range ladder as the standard and subsequently gel extracted ., Isolated RNA was then reverse transcribed using Superscript III ( Invitrogen 18080044 ) and 2 μL 1 . 25 μM reverse transcription primer ( IDT 5′- ( Phos ) -AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGC- ( SpC18 ) -CACTCA- ( SpC18 ) -TTCAGACGTGTGCTCTTCCGATCTATTGATGGTGC CTACAG-3′ ) in a 20 μL reaction for 30 min at 48C ., RNA was subsequently hydrolyzed by the addition of 2 . 2 μL 1N NaOH and incubation at 98C for 20 min ., Reverse transcription products were resolved on a 10% polyacrylamide TBE urea gel , the gel was stained in SYBR Gold for 3 min , and cDNA products were gel extracted ., After resuspension in H2O , cDNA products were circularized using CircLigase ( Epicentre CL4111K ) in a 20 μL reaction volume at 60°C for 1 hr and subsequently heat-inactivated at 80°C for 10 min ., Circularized products were precipitated in isopropanol , resuspended in H2O , and used as a template for 20 μL PCR reactions using Phusion Polymerase ( NEB M0530S ) with forward library primer ( IDT 5′-AATGATACGGCGACCACCGAGATCTACAC-3′ ) and Indexed reverse library primer ( IDT 5′-CAAGCAGAAGACGGCATACGAGATNNNNNNNNGACTGGAGTTCAGACGTGTGCTCTTCCG-3′ ) where NNNNNNNN represents the barcode sequence unique to each library ., After 6–10 cycles , two PCR reactions per sample with no apparent duplexed products after resolution of 2 μL on an 8% polyacrylamide TBE urea gel were pooled and DNA was purified with a QIAquick kit ( Qiagen 28106 ) and eluted in 20 μL H2O ., Libraries were sequenced using the Illumina NextSeq 500 platform in a single-end flow cell at the Indiana University Center for Genomics and Bioinformatics ., Total RNA was extracted from the same cell lysates used to create ribosome profiling libraries ., Following lysis , 2 . 5 A260 units were diluted in 700 μL H2O and denatured in 1% SDS ., RNA was extracted once with an equal volume of hot acid phenol , once with an equal volume of room temp acid phenol , and RNA was precipitated with isopropanol ., Precipitant was resuspended in H2O and 10 μg RNA was DNAse treated using 4 units of RNase free DNase I ( Invitrogen AM2222 ) at 37°C for 30 min in a 20 μL reaction volume ., RNA was precipitated in isopropanol , resuspended in H2O and libraries were prepared by the Indiana University Center for Genomics and Bioinformatics using the ScriptSeq RNA-library kit ( Illumina SSV21124 ) ., Libraries were sequenced using the Illumina NextSeq 500 platform in a single-end flow cell at the Indiana University Center for Genomics and Bioinformatics ., NGSutils v 0 . 5 . 9 was used to remove sequencing adapters ( CTGTAGGCACCATCAAT ) and filter out any reads shorter than 25 bp ., Fastx v 0 . 0 . 13 was subsequently used to remove the first base from each read and resulting reads were aligned to the NCIB 3610 genome ( NZ_CP020102 . 1 ) using Bowtie v 1 . 1 . 2 ., Using the 3primeassignment . pl script ( S1 File ) , 1 , 750 , 000–3 , 550 , 000 reads per sample that uniquely aligned to the genome were assigned to a single position corresponding to the 15th nucleotide from the 3’ end according to the 3’ assignment method described previously 10 ., Only genes with an average read density greater than 0 . 1 ( defined as the number of mapped reads divided by the number of codons ) in all samples were analyzed further ( S11 Table ) ., For each sample , the pausescore . pl script ( S1 File ) was used to determine the pause score for each codon in the filtered list of genes defined as the number of reads assigned to that position divided by the average read density of that gene ., The first and last 6 codons of each gene were excluded from this analysis ., The pause scores for all codons calculated in this way can be found in S3 Table ., Average pause scores for all 8 , 000 potential tripeptides were determined in one of two ways–either with the tripeptide centered on the P-site or the E-site ., In both methods only the pause score for the P-site codon was used to determine the average ., The average pause scores for all tripeptides calculated in this way can be found in S5 Table ., Weighted sequence logos were generated by compiling all sequences in which the P-site codon had a pause score of 10 or greater and visualized using the WebLogo 3 online tool at http://weblogo . threeplusone . com/ ., Clustered orthologous group assignment was performed with the DIAMOND mapping mode of eggNOG version 4 . 5 76 ., E . coli ribosome profiling datasets ( SRX823699 , SRX823700 , SRX823701 , and SRX823703 ) published by Woolstenhulme et al . , 2015 were downloaded from the Sequence Read Archive and analyzed as described above using the MG1655 genome ( NC_000913 . 3 ) as a reference ., RNA-sequencing analysis was performed using the default parameters of the RSEM ( v 1 . 3 . 0 ) calculate expression function and the NCIB 3610 genome ( NZ_CP020102 . 1 ) as a reference 77 ., The transcripts per kilobase million ( TPM ) reported in the RSEM output were used to generate Fig 8 and S8 Table ., A local database of 2554 genomes were annotated with the Pfam library using the software hmmer v 3 . 1b2 and an E-value threshold of 1e-10 78 , 79 ., Proteins that contained both a CheC and FliMN_C domain were considered to be Fl

Introduction, Results, Discussion, Methods

Translation elongation factor P ( EF-P ) alleviates ribosome pausing at a subset of motifs encoding consecutive proline residues , and is required for growth in many organisms ., Here we show that Bacillus subtilis EF-P also alleviates ribosome pausing at sequences encoding tandem prolines and ribosomes paused within several essential genes without a corresponding growth defect in an efp mutant ., The B . subtilis efp mutant is instead impaired for flagellar biosynthesis which results in the abrogation of a form of motility called swarming ., We isolate swarming suppressors of efp and identify mutations in 8 genes that suppressed the efp mutant swarming defect , many of which encode conserved ribosomal proteins or ribosome-associated factors ., One mutation abolished a translational pause site within the flagellar C-ring component FliY to increase flagellar number and restore swarming motility in the absence of EF-P ., Our data support a model wherein EF-P-alleviation of ribosome pausing may be particularly important for macromolecular assemblies like the flagellum that require precise protein stoichiometries .

Translation elongation factor P ( EF-P ) is a highly conserved protein that alleviates ribosome pausing at consecutive proline residues ., Unlike most organisms , EF-P in the bacterium Bacillus subtilis is not required for growth but is instead required for a flagellar-mediated form of motility called swarming ., By mapping spontaneous suppressors , we identify 7 broadly distributed ribosome-associated factors that , when mutated , allow swarming in the absence of EF-P , the location of which may provide mechanistic insight ., Moreover , we show that EF-P enhances flagellar biosynthesis by alleviating ribosome pausing within a single flagellar structural component FliY and we implicate the RNA polymerase pausing factor NusG in long operon expression ., Finally , we extend ribosome profiling analysis in the absence of EF-P to gram-positive bacteria .

cell motility, medicine and health sciences, pathology and laboratory medicine, insertion mutation, pathogens, bacillus, microbiology, flagellar motility, mutation, prokaryotic models, experimental organism systems, nonsense mutation, sequence motif analysis, frameshift mutation, cellular structures and organelles, bacteria, bacterial pathogens, research and analysis methods, sequence analysis, animal studies, bioinformatics, medical microbiology, microbial pathogens, ribosomes, pathogen motility, biochemistry, cell biology, virulence factors, database and informatics methods, bacillus subtilis, genetics, biology and life sciences, organisms

journal.pgen.1005861

Carriage of λ Latent Virus Is Costly for Its Bacterial Host due to Frequent Reactivation in Monoxenic Mouse Intestine

Bacterial viruses , called bacteriophages or phages , are present in all bacterial communities and have profound impact on bacteria either by killing them or by mediating horizontal gene transfer through lysogeny ., Lysogeny refers to the ability of temperate phages , as opposed to virulent ones , to repress their lytic multiplication after infection and stably segregate with the bacteria ., In most cases , the repressed phage , or prophage , is integrated into the bacterial chromosome , but it can also replicate as an extrachromosomal element in the bacterium ., Nearly all bacterial genomes contain one or multiple prophages , which can constitute up to 14% of the genome for Escherichia coli strains 1 ., Active prophages can be induced , i . e . switch back to lytic multiplication in response to a signal such as DNA damage and subsequent SOS response ( reviewed in 2 ) ., Induction rates are usually too low to result in a cost to their host , and prophages were generally found to have positive impacts on lysogenic bacteria 3–6 ., The benefits of lysogeny can result from three distinct mechanisms:, ( i ) lysogenic conversion , by which phages bring useful bacterial accessory traits 4 , 7;, ( ii ) immunity , i . e . protection against other phages , as the prophage protects its carrier bacterium against the same , and sometimes other , phages 8; and, ( iii ) allelopathy , by releasing infectious virions that are able to kill susceptible bacterial competitors ., While induction results in the death of the lysogen , it can provide a competitive advantage for the remaining lysogenic population ., A large number of major bacterial toxins , such as the diphtheria , Panton-Valentine , cholera , Shiga- or scarlatin toxins are encoded on temperate phage genomes ( reviewed in 7 ) ., However , pathogenicity does not always increase bacterial fitness in a human host , suggesting that some pathogenic traits can be coincidental ( reviewed in 9 ) ., To our knowledge , except for Staphylococcus aureus , only a small proportion of prophages were demonstrated to carry beneficial traits for their bacterial host , such as improvement of the colonization of body surfaces—like intestine 10 , 11 , nasopharynx 12 , or skin 13—or resistance to protozoa grazing 14 , 15 ., The allelopathic character of temperate phages has been demonstrated by in vitro experiments and mathematical modelling 16 , 17 , but also recently during insect infection 18 ., However , very few data exist concerning the impact of prophages on the fitness of their hosts in the most densely populated bacterial ecosystem , the intestine of mammals ., Metagenomic studies have shown that gut bacteria harbor many temperate phages 19 , but whether carrying a prophage is generally costly or advantageous for its host has been rarely investigated in the intestinal environment 20 , 21 ., A well documented case of beneficial interaction is the filamentous temperate phage of Vibrio cholerae VPIΦ , which encodes factors essential for bacterial adherence and intestine colonization 10 , 11 ., E . coli prophages carrying Shiga toxin stx genes are known to be active in the intestine , but their excision and lysogenization rates were not quantified 22 , 23 ., Another study demonstrated that a prophage of an Enteroccocus faecalis strain provided a 1 . 5-fold growth advantage after 24 hours of mouse gut colonization 24 , but the mechanisms involved were not entirely explored , nor the impact of prophage presence after the first day of colonization ., The costs or benefits of lysogeny in the gastrointestinal tract cannot be inferred from in vitro studies , since the parameters that rule phage-bacteria interactions vary greatly with the environment , bacterial physiology and medium structure ., For example , the lysogenization rate of phage λ , i . e . the proportion of infected E . coli bacteria that are lysogenized upon infection , varies from 10−3 when infecting cells in optimal growth conditions , to 0 . 5 when infecting starved cells 25 ., This rate also varies with temperature and multiplicity of infection 26 ., Three other main interaction parameters can be distinguished:, ( i ) the induction rate ,, ( ii ) the adsorption rate onto the bacterial host , i . e . affinity of the phage for its receptors , a parameter that greatly depends on ionic conditions 27 , and, ( iii ) the multiplication rate within the host ., Up to now , none of these parameters has been determined for a temperate phage in the gut environment ., Yet , characterizing temperate phage activity is essential to estimate their impact on lysogenic bacteria , and to evaluate the extent of the horizontal gene transfer they mediate in this environment ., This point is of paramount importance because temperate phages are major actors of bacterial genome evolution , and as such they participate to the emergence of new pathogenic strains ., Moreover they are suspected to be important disseminators of antibiotic resistance genes 28 ., The extreme complexity of the gut microbiota prevents any exhaustive characterization of all the virus-host systems it hosts ., It is thus necessary to first characterize specific virus–host systems in a controlled microbiota to bridge the existing gap between in vitro studies and the functional characterization of natural gut microbial communities ., We used monoxenic mice , i . e . mice associated with a single bacterial species , to perform competition experiments between two isogenic E . coli strains , one carrying the λ prophage and the other devoid of it ., These experiments , supported by a mathematical model consisting of five ordinary differential equations , allowed disentangling the different components of the impact of the prophage on bacterial reproductive fitness ., We obtained quantitative estimations of the main parameters driving phage-bacteria interactions in monoxenic mouse intestine ., Moreover , we demonstrate that efficient phage spreading enabled rare events of phage-mediated gene capture by homologous recombination , and transmission to new bacteria ., To characterize phage-bacteria interactions in the mouse digestive tract , we colonized germ-free mice with two isogenic E . coli MG1655 strains , except for antibiotic resistance markers and the presence of the λ prophage ( λble phage confering phleomycin resistance to the lysogen ) ., Populations of free phage ( V ) , bacteria from the lysogenic lineage ( L ) , from the susceptible lineage ( S ) and newly lysogenized by λ ( SL ) were quantified in mouse feces for one week , based on their differential antibiotic resistance levels ., During the first day of colonization , phage propagation , via free phage production and lysogenization of the susceptible bacteria , was highly efficient: after 24 hours of colonization , an average 73% of the bacteria from the initially susceptible lineage were either killed or lysogenized , and a transient increase in free phage had occurred ( Fig 1A ) ., To determine what fraction of free phage was produced by multiplication on susceptible bacteria , as opposed to free phage produced by spontaneous induction in lysogens , the same experiment was repeated with lamB derivatives of the two strains , devoid of the phage receptor and resistant to phage infection ( Fig 1B ) ., In such conditions all free phages result from the spontaneous induction of prophages in lysogenic bacteria ., At the peak of free phage production , the free phage over lysogen ratio was 20-fold lower than in the experiment with wt strains ( Fig 1C ) , indicating that the transient increase of free phage observed with these strains resulted from multiplication on susceptible bacteria , and not from a transient increase in induction rate ., By comparison , when the same S and L strains were co-cultured in vitro , in standard rich LB medium , phage propagation was almost undetectable ( Fig 1D ) , in line with previously published results 29 ., This absence of propagation was due to low Mg2+ concentration in LB , drastically limiting λ adsorption ( 27 and Fig 1D and 1E ) ., Addition of maltose did not improve phage propagation , suggesting that LamB expression in LB is sufficient for phage infection 30 ., In mice , phage propagation stopped before the complete lysogenization of the S lineage ., To test whether this resulted from changes in gut bacteria impairing infection , mice were monocolonized with the susceptible strain S only , and bacteria from feces were tested for affinity to λ ( Fig 2A ) ., After one day , λ adsorption rate on bacteria from mouse feces was measured at 3 . 10−7 ml h-1 , which is similar to the value measured at day 0 in vitro ., Therefore the phage receptor LamB is highly expressed in the mouse gut , and favorable ionic conditions allow for efficient binding ., Later on however , the adsorption rate diminished continuously , suggesting a decrease in LamB expression ( Fig 2A ) ., To investigate this phenomenon further , we determined the susceptibility to λ of S clones isolated from mouse feces two days after colonization ., Nine out of the twelve clones tested turned out to be genetically resistant to λ , and were moreover unable to use maltose , as revealed by their inability to grow on minimal medium containing maltose as the unique energy source ., In subsequent colonization experiments , we quantified the increase in maltose-deficient bacteria ( Mal- ) by using maltose agar plates containing a tetrazolium dye that turned red in Mal- colonies ( Fig 2B ) ., Mal- bacteria were selected in the S and L lineages ( Fig 2C ) ., A similar rise in Mal- bacteria occurred in mice monocolonized with the phage-free strain S , demonstrating unambiguously that their selection is not caused by λ ( Fig 2C ) ., Mal- and λ resistance phenotypes , as well as previously published results 31 , guided our identification of mutations in the malT gene ., MalT is the transcriptional activator of the maltose regulon ., It notably controls expression of the λ receptor LamB ., All six resistant clones studied carried a mutation in malT , among which three led to a truncated protein ( S1 Fig ) , which explains that the selected mutations prevent phage infection ., The reason for the selection of these mutants might be linked to the LamB-induced envelope stress associated with osmoregulation 30 since bacteria in the gastrointestinal lumen are continuously exposed to osmotic stress ( reviewed in 32 ) ., They are specific to monoxenic mice , as malT mutations are not selected for in the MG1655 E . coli strain when colonizing mice with a conventional microbiota 33 ., The rise of malT mutants was nevertheless sufficiently delayed to permit observation phage infection of the majority of S bacteria during the first two days ., In order to provide quantitative estimations of the parameters governing phage-bacteria interactions , we developed a mathematical model representing the dynamics of the different microbial populations in this model ecosystem ( Fig 3 ) ., The model is based on the one in 17 and adapted to take into account our experimental settings ., It consists of five coupled differential equations , representing time evolution of five population densities: S ( susceptibles ) , L ( lysogens ) , SL ( newly-lysogenized susceptibles ) , V ( free phage ) , as well as latent bacteria Q in which the phage undergoes lytic multiplication ., Invasion of malT mutants is not included in the model ., S1 Text gives a detailed description of the main modeling assumptions behind its construction , as well as a mathematical analysis of its dynamical behavior ., A careful examination of the effect of the eight model parameters onto the dynamics enabled the quantitative estimation of six of them from our experimental datasets ( Table 1 ) ., With these estimated values , numerical simulations of the model ( Fig 3C ) are in good agreement with experimental observations on the first two days , before invasion of malT mutants , suggesting it captures most of the relevant information contained in our data ., The main discrepancy observed is in the initial velocity of the temporal evolutions , faster in the model than in experimental data ., This might result either from incorrect estimation of some parameters , or from the neglect of a phenomenon not taken into account in the model , such as the binding of free phage on some intestinal components ., Such binding would result in a “loss” of phage that would slow the dynamics , as exemplified by the effect of reduced burst size ( S2 Fig ) ., Upon infection of a susceptible bacterium , λ goes to lysogenization with a high probability around 19% , leading to a very rapid rise of SL bacteria both in data and in numerical simulations ( Fig 4A ) ., The remaining infected susceptibles were lysed , resulting in an increase of the L lineage relative to the S one , independently of the initial L/S ratios ( Fig 4B ) ., LamB deletion abolished the competitive advantage of the L lineage ( Fig 4B , dotted line ) , confirming that the advantage of lysogens only stemmed from the lysis of susceptible competitors , and not from the presence of putative bacterial fitness genes in the λ genome that would improve growth in mice ., However , the gain of the L lineage over the S one is limited by the lysogenization of susceptible , independently from the rise of λ resistant mutants ., Its final value , as predicted by the model , seems to be directly proportional to the inverse of g at population equilibrium ( Fig 4C ) ., Interestingly , other parameters governing phage-bacteria interaction have very modest impact on the final gain of the L lineage ( S3 Fig and S1 Text ) ., Prophage induction rate in the mouse gastrointestinal tract was estimated to be 1 . 6% , several orders of magnitude higher than usually assumed ., This high induction leads to a slight but systematic decrease of lysogens ( L and SL lineages ) in mice when bacteria are resistant to infection , either because of malT or lamB mutations ( Figs 1A and 1B and 5A ) ., By contrast , in vitro , induction rate is 3 x 10−4 ( Table 2 ) , and competitions under conditions that did not permit phage infection resulted in a stable proportion over time of lysogenic ( L and SL lineages ) and non-lysogenic S bacteria ( Figs 1D and S3A ) ., In mouse , model-based estimation of the induction rate was derived from latent Q cell counts in mouse colonized with lamB strains ., Because of the small data set available ( one experiment with three mice ) , the confidence interval is relatively large ( 0 . 6%-3 . 6% , see Table 1 and S1 Text ) ., In order to strengthen the estimation , we also computed the induction rate from the relative fitness of L compared to S lamB lineages ( Material & Methods ) ., The value found ( 1 . 7% ± 0 . 5% ) was very close to that estimated by the model ., To examine experimentally the impact of high induction rate , we used a non-inducible λ prophage , λcIind- , which has a mutation in the repressor of the lytic cycle , CI , preventing its RecA activated auto-cleavage upon DNA damage ., As expected , in standard in vitro conditions , the cIind- mutation decreased the induction rate 1 , 000-fold ( Table 2 ) ., In the mouse gastrointestinal tract , the mutation abolished the decrease in proportion of lysogens ( Fig 5B ) , demonstrating unambiguously that high prophage induction explains the disadvantage of lysogens ., Moreover , in a lamB genetic background , S and λcIind- lysogenic strains presented no reproductive fitness differences over 9 days ( S4B Fig ) , validating the model hypothesis that in the absence of lysis and induction , the presence of the prophage makes no difference in growth rate ., Interestingly , λcIind- experiments also validated the absence of a rarity threshold to phage multiplication in the mouse gut: since phage amplify on susceptible bacteria , even a very low initial number of phage can lead to killing of a significant part of S lineage ( Fig 5B and 5C ) ., The switch from lysogenic to lytic cycle requires CI autocleavage , catalyzed by RecA nucleofilament formed by DNA damage 36 , 37 ., In the λcIind- lysogens , RecA mediated CI autocleavage is prevented , and the few phage produced are CI low expression mutants 36 ., Indeed , free phage isolated from feces of mice colonized with λcIind- lysogens formed clearer plaques than λ wild-type , which suggest they have a lower lysogenization rate ., Sequencing of the cI gene from 9 phages isolated at day 2 either from free phage in feces or from SL bacteria revealed they all had a point mutation in the -35 box of CI promoter , PRM ( G->T , -33 relative to the cI start of transcription ) ., Interestingly , this PRM mutation was previously shown to enable λ prophage induction in the absence of SOS activation , by decreasing by 80% intracellular CI levels , leading to much higher switching rates from the lysogenic to the lytic states 36 ., Indeed , these PRM mutants , named λcI* , had an induction rate 50 , 000-fold higher than that of the ancestral λcIind- phage and 300-fold higher than the wild-type ( Table 2 ) ., Measurement of induction rate from 12 other SL bacteria revealed they were all lysogenized by λcI* ., The high induction of this virulent mutant enabled its propagation during the first days of colonization ., However , in agreement with evolutionary epidemiology theory , that predicts that selection for virulence decreases with the pool of susceptible hosts 38 , the virulent λcI* mutant was counter selected later on in the prophage form , due to killing of its host through induction ( Fig 6 ) ., We next investigated whether this high phage activity allowed for gene exchange between the phage and bacterial genomes ., We have previously reported that λ captures bacterial genes by homologous recombination during the lytic cycle , at frequencies ranging between 10−4 and 10−6 depending on the extent of homology between the DNA segments ( Fig 7 and 20 ) ., In our experimental system , recombination can lead to the incorporation of the chloramphenicol resistance gene ( cat ) of the L strain into the phage genome , since L bacteria have the cat gene in a chromosomal region of partial homology with λ ( 88% identity ) ., We investigated the occurrence of this phenomenon in mice ., Recombinant phages can be detected in their lysogenic form as they confer chloramphenicol resistance to the bacteria they are integrated in ., On days 1 and 2 , recombinant prophages were detected in all mice , at frequencies around 5 . 10−8 relative to the number of new lysogens ( SL ) ., PCR analysis confirmed that the cat gene was placed at the expected position in the λ prophage ., No recombinants were detected with a λ phage deleted of its main recombination gene , bet ( or redβ ) , indicating the importance of phage recombination function for gene acquisition ., Complete genome sequencing of thousands of gut bacteria has shown that most harbor prophages , yet their impact on strain fitness in the gastrointestinal tract has rarely been investigated ., Colonization experiments , supported by a mathematical model of phage/bacteria interactions , show that the advantage of λ lysogeny in monoxenic mice gut is valid only when susceptible bacteria are present; a situation that might be only occasional in the gut microbiota ., Indeed , it is supposed that only one or two E . coli strains cohabit at the same time in the human gastrointestinal tract 39 , 40 , and moreover , to our knowledge λ phage infects only a small proportion of E . coli strains ., In the absence of susceptible competitors , the prophage was costly for its host , due to frequent induction caused by DNA damage ., Prophages were generally shown to positively impact their host fitness , and our study is , to our knowledge , the first demonstration that a prophage can be detrimental to bacteria in the gastrointestinal tract ., The level of phage λ induction observed in monoxenic mice was remarkable: 1 to 2% of lysogenic bacteria were lysed per generation , which is almost two orders of magnitude higher than in standard laboratory conditions , in which induction was too low to constitute a measurable fitness cost ( Figs 1C and S4 ) ., This result is reinforced by another study showing that the induction rate of 933W lambdoïd prophage is higher in the mouse gastrointestinal tract than in vitro , and constant over time 41 ., However , the reporter assay used did not permit direct estimation of the induction rate and the associated cost for the bacteria 41 ., We observed that the λ repressor mutation CIind- , which abolishes CI auto-cleavage , dramatically decreased prophage induction in the intestine ., Many reports over a long period of time have proven unambiguously that for such cleavage to occur , a RecA nucleoprotein filament ( also called “activated RecA” ) must catalyze the reaction 36 , 42 , 43 , so lambda prophage induction reflects DNA damage ., Since RecA nucleoprotein filament also triggers the general SOS response 44 , our results indicates that this response is activated in 1 to 2% of bacteria in the intestine of monoxenic mice ., DNA damage sensing is not only responsible for the induction of most prophages 2 , but it also triggers activation of other mobile genetic elements such as integrative and conjugative elements or ICEs 45 , transposons 46 and integrons 47 ., Moreover , the large number of defective prophages in E . coli genomes , i . e . prophages incapable of independent induction or particle formation 48–51 , suggests a regular selection in favor of bacteria having lost or inactivated these prophages , possibly in response to their frequent induction in the intestine ., Interestingly , at least in the simplified gut environment used in this study , induction cost is not compensated by the putative adaptative genes lom , bor and rex described in the λ genome 52 , 53 ., The low induction rate λcIind- phage used in this study was on the contrary beneficial to its host , since it confers no induction cost while still enabling efficient killing of competitors through amplification on susceptibles ., However , phage mutants with higher induction are strongly selected for when susceptible bacteria are present 38 , as exemplified in our study by the selection of the λcI* virulent phage ( Fig 6 ) ., Interestingly , all E . coli lambdoïd phages tested have comparable levels of induction 54 , close to that of lambda , suggesting they have all evolved toward the same optimal induction rate for propagation ., Alternatively , it was proposed that optimal induction rates evolved to benefit the bacterial host–and thereby prophage vertical transmission- by a “bacterial altruism” mechanism 55 ., Indeed , in the case of Shiga-toxin carrying prophages , prophage induction leads to the release of toxins killing bacterial protozoan predators , benefiting the bacterial host even in the absence of susceptible competitors ., The level of phage λ lysogenization unraveled was also remarkable: we estimated that in monoxenic mice gastrointestinal tract the lysogenization rate is close to 20% ., By comparison , in vitro , lysogenization rate is close to 0 . 1% in bacteria growing rapidly 54 , but almost 50% in starved bacteria 25 , 56 ., This lysogenization rate estimated in mouse is therefore much higher than generally measured or assumed 16 , 17 , 57 , 58 ., Since the final gain of the original phage carrier on the susceptible strain is inversely proportional to the lysogenization rate ( our results and 57 ) , in the gastrointestinal tract high lysogenization results in a smaller gain of the original phage carrier than previously described ., Theory predicts that high lysogenization optimizes phage reproduction in an environment where the density of susceptible hosts is low or variable 58 , 59 ., Although the gut microbiota is the densest bacterial community on earth , it includes hundreds of different species and thousands of bacterial strains , possibly making highly specific phage infection relatively rare ., Low phage susceptibility seems to be the conclusion of a large-scale study of phage-bacteria interactions in a gnotobiotic mouse model 60: in mice raised with a simplified microbiota composed of 15 strains belonging to dominant human species , only two were attacked by a cocktail of thousands of different phages isolated from a human gut microbiota ., Moreover , a higher proportion of temperate phages was found in gut viromes than in other environments 19 , 61 ., Altogether , these data support our results , suggesting that in the gastrointestinal tract the lysogenic life cycle of phages is favoured compared to lytic multiplication ., Temperate phages being major actors of horizontal gene transfer in bacteria , a concern emerged recently regarding their role in the propagation of antibiotic resistance genes 62 ., Indeed , some phage particles are vectors of antibiotic resistance genes 28 ., Most of the time , gene transfer occurs by generalized transduction , the erroneous encapsidation of bacterial DNA ., Such errors are rare: for instance , the proportion of E . coli phage P1 capsids leading to the production of an antibiotic-resistant clone is between 10−5 and 10−6 63 , 64 ., The incorporation of a bacterial gene into phage genome , and afterwards transfers by lysogenization is a much rarer event , that we could detect only when the gene was located in a defective prophage sharing homology with λ 20 ., In the present study , we estimated the frequency of such gene capture ( cat gene , conferring chloramphenicol resistance ) by λ in mice to be 10−8 ., Interestingly , up to now very few cases of phages encoding resistance genes have been reported 65–67 , suggesting that even if the lysogenization rate in the intestine is very high , the risk of antibiotic resistance spread mediated by temperate phage is low ., Here we found that in monoxenic mouse gastrointestinal tract , lysogeny initially benefits its host during competitions with susceptible bacteria , in line with previous studies in other environments 16–18 ., The mathematical model highlighted that the benefit of the original lysogenic strain depends critically on the lysogenization and induction rates: the lower these parameters , the higher the benefit ., We also show that in monoxenic mice gastrointestinal tract , DNA damage leads to high prophage induction , which results in a significant cost for the lysogen ., Provided that DNA damage observed in monoxenic mice gut also occurs in conventional animals , since all E . coli lambdoïd phages tested have comparable levels of induction 54 -and since most E . coli prophages are lambdoïd 68- our results might prove to be general ., Due to the highly specific phage-bacteria interactions , we hypothesize that the absence of bacteria susceptible to a particular phage in the gastrointestinal tract might regularly occur , and that on the long term , the parasitic aspect of at least some active prophages prevails ., All bacterial strains are described in S1 Table ., All strains were constructed by modifying the MG1655 ΔfliC ΔompF strain ., This strain was used because ompB mutations are rapidly and systematically selected in the MG1655 strain in the mouse gut as a result of their effects on flagellin ( FliC ) repression and of decreased membrane permeability via repression of the major porin OmpF 69 ., As ompB mutants also display a reduced expression level of LamB 31 , a maltoporin used by phage λ for infection , we used a ΔompF ΔfliC strain in which no ompB mutations were selected 69 ., The stfR::cat mutation was introduced in this strain by phage P1 transduction from the MD19 strain described in 20 ., The ΔlamB strains were constructed by phage P1 transduction of the lamB::KanR cassette from the Keio collection strain JW3996 70 ., In the MD56 and MD74 strains , the KanR cassette was excised as described in 71 ., The λ receptor being absent in ΔlamB strains , λ prophage was introduced by transformation with Urλble purified DNA ., The λble phage strain used in this study was constructed by insertion of the phleomycin resistance gene ble into the Urλ strain of λ , as described in 20 ., The λcIind- mutant contains a mutation ( A111T ) in the RecA cleavage site: the alanine in position 111 is replaced by a threonine 72 ., This mutation was introduced by recombineering with the oligonucleotide AT111 ( GTAAAGGTTCTAAGCTCAGGTGAGAACATgCCgGttTGgACATGAGAAAAAACAGGGTACTCATACC ) ., Small letters represent changes in the DNA sequence ., Several neutral differences were added to the one necessary for the amino acid change in order to avoid recognition by MutS ., Recombineering was performed in the HME57 strain 73 , which carried plasmid pKD46 71 , and lysogenized with λble ., The strain was co-transformed with two oligonucleotides , AT111 and Court’s lab oligonucleotide 144 , conferring it the ability to use galactose 74 ., After transformation , colonies were isolated on M9 minimal galactose plates ., 96 Gal+ clones were screened for the absence of spontaneous phage induction , by scoring the absence of infectious phage particles in culture supernatants ., 1 out of 96 clones had the expected mutation , which was confirmed by sequencing of the cI gene ., The λcIind- phage was next introduced into the MG1655 ΔfliC ΔompF stfR::cat strain by P1 transduction and selection on phleomycin plates ., Germ-free C3H/HeN mice were bred at the germ-free animal facilities of the INRA Micalis Institute , Anaxem ., Mice were reared in isolators and fed ad libitum on a commercial diet sterilized by gamma irradiation ( 40 kGy ) and supplied with autoclaved tap water ., For colonization experiments , 8 week-old germ-free female mice were gavaged with 106 bacteria from the chosen strain , or the appropriate mixture of the two strains , in 0 . 1 mL of M9 minimal medium ., The cassettes used to differentiate strains during competition confer resistance to chloramphenicol or to kanamycin ., Their expression is known to have no significant cost during E . coli intestinal colonization , so no inversion of markers was performed 31 , 75 ., Bacterial and phage populations in feces were monitored by colony forming unit ( CFU ) and plaque forming unit ( PFU ) counts in freshly harvested individual fecal samples , as described below ., Feces were homogenized in a 10-fold volume of sterile water before dilution in LB and plating on LBA plates with the appropriate antibiotics ., PFUs were enumerated in the supernatant of suspended feces centrifuged 3 minutes at 12 , 000 g ., All procedures were carried out in accordance with the European guidelines for the care and use of laboratory animals ., The project received the agreement of the local DDPP ( n° A48-195 ) and from the local ethic committee for animal experimentation , the Comethea ( n° 13–05 ) ., After serial dilutions , bacterial populations were determined by plating on selective antibiotic-LB agar plates ( 1 . 5% agar ) ., Antibiotics were used at the following concentrations: kanamycin ( 50 μg/mL ) , chloramphenicol ( 20 μg/mL ) , and phleomycin ( 5 μg/ml ) ., PFUs were determined by spotting 10 μl of serial dilutions of diluted feces on a lawn of the indicator bacteria in top agar ( 0 . 4% agar , 10 mM MgSO4 ) ., The indicator bacterial culture was fresh MD5 culture grown in LB containing 0 . 2% maltose ., Latent bacteria were counted similarly but after elimination of free phage by centrifugation ., CFUs and PFUs were counted after 12–16 hours of incubation at 37°C ., The ability to use maltose was monitored in tetrazolium maltose ( TM ) indicator plates ., Mal+ and Mal- clones respectively form white and red colonies on these plates ., The TM medium was composed of tryptone ( 10 g/L ) , yeast extract ( 1 g/L ) , NaCl ( 5g/L ) , agar ( 16g/L ) , maltose ( 5 g/L ) and tetrazolium dye ( 50 mg/L , Sigma ) ., The technique was essentially that of Hendrix 76 , with minor modifications ., Adsorbing bacteria from feces were prepared as described for enumeration ., A control culture was grown at 37°C with shaking in LB +

Introduction, Results, Discussion, Materials and Methods

Temperate phages , the bacterial viruses able to enter in a dormant prophage state in bacterial genomes , are present in the majority of bacterial strains for which the genome sequence is available ., Although these prophages are generally considered to increase their hosts’ fitness by bringing beneficial genes , studies demonstrating such effects in ecologically relevant environments are relatively limited to few bacterial species ., Here , we investigated the impact of prophage carriage in the gastrointestinal tract of monoxenic mice ., Combined with mathematical modelling , these experimental results provided a quantitative estimation of key parameters governing phage-bacteria interactions within this model ecosystem ., We used wild-type and mutant strains of the best known host/phage pair , Escherichia coli and phage λ ., Unexpectedly , λ prophage caused a significant fitness cost for its carrier , due to an induction rate 50-fold higher than in vitro , with 1 to 2% of the prophage being induced ., However , when prophage carriers were in competition with isogenic phage susceptible bacteria , the prophage indirectly benefited its carrier by killing competitors: infection of susceptible bacteria led to phage lytic development in about 80% of cases ., The remaining infected bacteria were lysogenized , resulting overall in the rapid lysogenization of the susceptible lineage ., Moreover , our setup enabled to demonstrate that rare events of phage gene capture by homologous recombination occurred in the intestine of monoxenic mice ., To our knowledge , this study constitutes the first quantitative characterization of temperate phage-bacteria interactions in a simplified gut environment ., The high prophage induction rate detected reveals DNA damage-mediated SOS response in monoxenic mouse intestine ., We propose that the mammalian gut , the most densely populated bacterial ecosystem on earth , might foster bacterial evolution through high temperate phage activity .

Dormant bacterial viruses , or prophages , are found in the genomes of almost all bacteria , but their impact on bacterial host fitness is largely unknown ., Through experiments in mice , supported by a mathematical model , we quantified the activity of Escherichia coli prophage λ in monoxenic mouse gut , as well as its impact on its carrier bacteria ., λ carriage negatively impacted its hosts due to frequent reactivation , but indirectly benefited its host by killing susceptible bacterial competitors ., The high prophage activity unraveled in this study reflects a constant rate of SOS response , resulting from DNA damage in monoxenic mouse intestine ., Our results should motivate researchers to take the presence of prophages into account when studying the action of specific bacteria in the gastrointestinal tract of mammals .

bacteriology, organismal evolution, medicine and health sciences, gut bacteria, bacteriophages, microbiology, vertebrates, mice, animals, mammals, viruses, animal models, dna damage, model organisms, microbial evolution, bacterial genetics, dna, microbial genetics, bacteria, digestive system, research and analysis methods, mouse models, gastrointestinal tract, biochemistry, rodents, anatomy, nucleic acids, genetics, biology and life sciences, evolutionary biology, bacterial evolution, organisms

journal.pcbi.1004846

Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data

Human malaria is caused by infection with Plasmodium falciparum and four other species of parasites , accounting for around 600 , 000 deaths and 100–250M febrile episodes annually 1 ., The global burden of malaria is declining , partly due to an increase in donor funding and scaled up distribution of vector control and effective medicines 1 , 2 ., Many countries have set the goal of eliminating malaria in the coming decades , which involves stopping transmission , emptying the parasite reservoir , and then managing imported malaria and potential outbreaks 3 ., To continue this progress , methods of prioritizing particular areas for targeting control efforts have been proposed 4 , 5 , requiring an understanding of the spatial patterns of transmission ., Since mosquitoes transmit the pathogen , and since the underlying distributions of mosquitoes and humans are highly heterogeneous 6 , so is the intensity of transmission 4 , 7 ., Heterogeneity in transmission is observed throughout on the road to elimination , providing an opportunity for spatial targeting of control 4 , 7 ., This heterogeneity in malaria transmission is driven by ecological and social factors such as Anopheline mosquito density , land use and agricultural practices , bed net use , wealth and education , access to and utilization of healthcare , and urbanization 8 , 9 , and these factors drive heterogeneity across all spatial scales , from very local 5 to national and global landscapes 10 ., Because of these spatially heterogeneous processes that drive transmission , malaria tends to persist in “foci” , or localized areas of self-sustaining transmission 5 ., Malaria burden can also be relatively high outside of foci , as human and mosquitoes transport parasites 4 , 11 ., Because they are the ultimate sources of local parasites , foci drive the spatial distribution of endemic malaria , and effective targeting requires their identification and understanding how human populations interact with them 12 ., Some statistical algorithms and ad hoc methods target foci for control 5 , 13 and quantify change in their spatial extents 13 ., Despite this , no formal mathematical or mechanistic definition of a malaria focus exists , limiting practical application and ability to explain and predict changes in focal extent over time ., One possible quantitative definition regards it as a demographic source or an area where reproductive success is , on average , high enough for some population to persist and export individuals 14 ., This definition is well-established , as spatial heterogeneity in demographic success and population dynamics has been explored in detail in ecological literature 14 , 15 ., In this context , sources are self-sustaining areas of high demographic success , or areas where birth rates exceed death rates and excess individuals are exported 14 ., Sinks , on the other hand , are areas where populations require a constant flow of immigrants to persist 14 ., For malaria parasites and other pathogens , demographic success is defined by reproductive numbers 16 ., In this case , a source is where an infection in one host tends to be propagated to more than one host and excess parasites tend to be exported to other areas 11 , 17 , 18 ., For malaria , the basic reproductive number R0 has been both a useful threshold criterion for endemicity and a basis for setting intervention coverage targets ., If R0 > 1 , malaria is expected to persist locally because each case causes more than one case , whereas pathogen transmission is not sustained over time if R0 < 116 ., The conventional definition of R0 describes transmission in some particular place disregarding malaria importation and the spatial configuration of malaria transmission 19 ., In fact , malaria often persists in areas where R0 < 1 , however , due to human and mosquito movement 20 , termed “non-endemic transmission” 21 , 22 ., This non-endemic transmission is driven by both importation via incoming migrants 23 , and by residents infected with parasites during travel 23 , 24 ., As humans move more frequently and further , more areas outside of foci receive parasites via mosquito and human movement ., These extra-focal areas may then harbor a nonzero fraction of people infected with malaria , or a nonzero parasite rate ( Fig 1 ) ., Extra-focal areas with infected people are ultimately demographic sinks of parasites , as parasite death rates exceed birth rates locally ( represented by a local R0 < 1 ) but those parasite populations are sustained through immigration 14 ., If burden is assumed to be stable spatially and temporally , ignoring parasite mobility leads to the incorrect conclusion that all malaria-endemic areas would have self-sustaining parasite populations 25 ., Micro-epidemiological 26 and malaria metapopulation models 20 can be used to identify which of these areas are actually transmission foci ( i . e . sources , when paired with extra-focal human movement ) by quantifying migration and modelling transmission within and among populations ., By applying these methods to define transmission foci , novel methods for spatial targeting of control can accurately quantify R0 and identify areas with sustainable transmission ( Fig 2 ) ., These methods can then improve efficiency in achieving important policy goals such as regional malaria elimination by guiding control targeting efforts ., Here , we demonstrate use of a method for identifying putative sources and sinks of malaria transmission in a multipatch setting at steady state with respect to prevalence ( or parasite rate ) ., Using malaria prevalence estimates with a call record dataset from mobile phones , we calculate the transmission patterns necessary to yield observed patterns of prevalence , assuming burden was not changing over time ., We discuss this method’s utility for informing future elimination efforts in countries where malaria burden is generally low but stable , as transitioning towards elimination in these settings means concentrating activities on identifying and attacking transmission foci 3 ., As we analyze the presented model at equilibrium , it is not appropriate in areas where burden is changing through time ., In this case study , we use prevalence estimates from the pre-elimination phase in Namibia 10 with 2010 mobile phone call record data to identify pre-elimination transmission foci ., Because the survey data informing the prevalence estimates for Namibia used originated largely from pre-2000 10 , we assume these estimates represent a period when burden was relatively stable ( matching the steady state assumption required by our method ) ., Overall control was relatively limited during this time as Global Fund-supported efforts began in the early 2000s 27 , and case numbers did not drop significantly until after 2004 1 , 27 ., Stability of burden cannot be confirmed with certainty without more historical data , however , as national-level incidence data were not available from before 2001 1 ., As the output maps show historical transmission foci , practical application of the presented results is limited , though these historical foci allow us to compare against more recent malaria burden estimates to examine possible change in the spatial patterns of transmission over time ., We compare the predicted transmission foci map with an incidence map reflecting burden in 2009 26 to assess how transmission patterns may have changed between these time periods ., We also use this analysis to assess the possible importance of internationally imported cases into non-focal areas on overall case distribution ., To demonstrate how this modeling framework can guide elimination planning , we used our model to identify transmission foci and quantify parasite flow throughout Namibia using parasite rate estimates ., We compared the results of steady state analyses of this multipatch model with similar analysis of a classical model without human mobility ., Table 1 contains the values of various malaria metric parameters used for both models , and are constant throughout all analyses ., The parameters used originate from African vectors in The Gambia 28 , 29 , laboratory trials with Anopheles gambiae mosquitoes30 , 31 , the most common malaria vector in Africa , and epidemiological studies 32 , 33 ., Figures do not show areas initially estimated to have zero parasite rate , as neither model predicted any areas with zero PR to have R0 , i > 1 when analyzed at equilibrium ., Further , while the multipatch model provides an estimate of R0 , i for patches where parasite rate is zero at equilibrium , the classical model without movement can only estimate that R0 , i < 1 without providing an exact estimate 25 ., Human movement is a key driver of spatiotemporal vector-borne disease dynamics , as pathogen exportation out of malaria foci sustains transmission across much larger spatial extents 21 , 22 ., People move often and for a variety of reasons 35 , including local routines 36 , short-term labor-related movement , and long-term migration , and in moving they disperse parasites typically further than mosquitoes 11 ., Targeting the areas responsible for malaria persistence across landscapes , then , requires a quantitative framework that integrates human movement information and disease burden data 4 , 37 ., The presented method identifies transmission foci in settings with temporally static and generally low levels of malaria burden , using human mobility in a modeling framework rooted in ecological literature ., Generally , the results produced by this study can help inform targeting efforts when elimination efforts begin in countries where burden is unchanging over time ., We demonstrate use of this method in a case study with pre-elimination prevalence estimates and mobile phone data from Namibia , generating historical estimates of transmission capacity and highly resolved maps of malaria dynamics ., Importantly , vectorial capacity and basic reproductive number estimates generated using this method are in the context of the transmission setting represented by the prevalence data ., If a country is undergoing active malaria control , then the basic reproductive number calculated represents Rc , or the basic reproductive number given the current level of control 38 ., This method then can provide guidelines for interventions in addition to the baseline represented by the input data ., In this case study , we used prevalence estimates informed largely by pre-2000 surveys , causing our maps of transmission foci to reflect historical transmission patterns ., Namibia has been experiencing dramatic declines in malaria burden over the past decade 27 and therefore no longer meets the steady state assumption required ., While data were not available to confirm that malaria burden was stable pre-2000 , overall burden during 2000–2004 was stable relative to the following decade , as active control efforts were limited 1 , 27 ., Though our results therefore do not represent current transmission foci , they are a useful case study for areas considering pursuing elimination , and also allow us to examine changes in focal extent over time in Namibia by comparing with more recent burden data ., Our method predicts that several areas that appeared to sustain endemic transmission during the pre-elimination phase actually had unsustainable levels of transmission ( Figs 4 and 5 ) ., This highlights the importance of accounting for movement , as this suggests parasite populations were maintained in some areas by non-endemic transmission alone , while similar steady-state analyses using classical models must conclude that these areas have local R0 > 1 ., The transmission landscape is also more heterogeneous when taking into account human mobility ( Figs 4 and 5 ) , agreeing with recent research that malaria is highly dynamic across space and time 4 , 12 , 39 ., Areas where R0 > 1 are potentially important targets for malaria elimination planning in stable pre-elimination countries , as reducing focal transmission to locally unsustainable levels will cause the parasite population to propagate at below-replacement levels system wide 20 ., Because this case study generates historical transmission foci maps , we compared the predicted spatial patterns of pre-elimination foci with 2009 incidence to determine how transmission patterns may have changed over time ., Fig 6 juxtaposes incidence in 2009 from 26 with pre-elimination transmission foci ., Districts overlapping foci had higher incidence than those outside foci ( 12 . 5 vs 8 . 7 ) , suggesting that transmission in 2009 was higher in pre-elimination focal areas ( Fig 6 ) ., Overall , however , the spatial pattern of burden in 2009 differed with predicted pre-elimination foci , and there were numerous extra-focal areas where 2009 incidence was high , such as in eastern Namibia ., High incidence in these extra-focal areas could be caused by changes in transmission capacity patterns driven by climatic changes or heterogeneous application of control effort ., Perhaps highlighted by its presence near national borders , high extra-focal incidence could also be driven by international importation 22 , causing these extra-focal areas to be importation hotspots ., The possibility of extra-focal importation hotspots near-elimination agrees with recent research on cross-border parasite mobility , which suggests that parasite importation generates significant amounts of onward transmission in areas with very low malaria endemicity 23 , 24 ., Future passively detected health facility-level case data could refine the relationship between transmission foci , case distribution , and international human movement , as malaria control programs are increasingly recording travel history information when reporting cases ., Populations in importation hotspots would be expected to have disproportionately high rates of travel , and malaria infected individuals in these areas will be more likely to have traveled to areas with higher transmission rates ., Though we applied this method to pre-elimination Namibia , this framework may be useful elsewhere if relevant population movement data are available and if the steady state assumption is satisfied ., The prevalence estimates used in our Namibia case study are available globally 1 , 10 , and while mobile phone data are not always available to parameterize movement , short-term movement can also be estimated using data informing other typologies of movement , including travel history surveys and census data 11 ., These datasets should originate from the same period and should be very recent to be most useful operationally ., In our case study , prevalence and movement data originated from different years ( pre-elimination vs . 2010 ) ., Though this discrepancy is not ideal , recent studies suggest movement patterns are generally stable and predictable across temporal scales , potentially suggestive of temporal regularity in movement patterns 40 , 41 ., These studies also suggest that though these types of movement are fundamentally different from those captured by mobile phone data , they likely exhibit similar patterns 40 ., On the other hand , accurate and current entomological and parasitological parameters are often difficult to obtain , and in this study , estimates for these parameters originated from various entomological studies ., We also assumed human recovery rates were homogeneous across all patches , though this parameter varies in reality due to differences in treatment-seeking behavior and health system coverage 42 ., Though these estimates likely do not accurately reflect current malaria dynamics in Namibia , we found that model results are robust to uncertainty in entomological and parasitological parameters ( see Methods ) ., Varying recovery rates regionally based on recent studies on treatment-seeking behavior 42 also suggests limited effect on focal extent , as only 7 patches ( or 2% of the 401 total patches ) changed relative to the focus-defining criterion R0 = 1 ., Further research will be necessary to extend utility of these analyses across transmission settings , particularly for countries with rapidly changing burden , as the presented analysis applies only to countries with stable and low levels of disease burden ., In these countries , this framework could help target elimination campaigns on focal areas of transmission if elimination efforts are not currently ongoing ., In the presented case study , we assume the transmission setting reflected in the prevalence surface used represents a period of relative stability , though pre-2000 burden data would be needed to confirm 1 ., Dynamic prevalence is likely the rule rather than the exception , however , as countries with active elimination campaigns are often experiencing similar declines in transmission ., Transmission is highly seasonal in many malaria endemic countries ( including Namibia ) , as well ., We did not incorporate seasonality because the Malaria Atlas Project model provided an average annual prevalence , rather than season-specific estimates 10 ., This framework therefore will not be directly applicable to much of the globe until future work relaxes the steady state assumption and can account for seasonally and annually dynamic prevalence ., This framework would also be applicable to more areas if extended to use other metrics of malaria burden , as prevalence surveys become rarer and more uncertain near-elimination due to needing impractically large sample sizes ., In such situations , country programs rely more heavily on clinical incidence measured at facilities 43 , which could be used as an alternative source of disease burden information ., As these data are a spatially heterogeneous subset of actual disease burden , using these data to reflect overall burden must account for factors such as test positivity rates and health facility catchment sizes , similar to the model used to estimate incidence in 2009 26 , 42 ., Importantly , recent research has better-defined the relationship between clinical incidence and prevalence , a useful step towards using clinical incidence data in this framework 44 ., Despite these challenges applying this framework in near-elimination settings , it represents an important step towards understanding and mapping transmission foci on dynamic landscapes ., The spatiotemporal dynamics of malaria are critical for elimination efforts , as transmission is known to exhibit spatial and temporal heterogeneity across multiple scales , and humans transport parasites between areas 20 , 21 ., Prioritizing parasite sources requires a targeting algorithm that incorporates human movement and transmission heterogeneity 4 ., This method accounts for these factors to produce maps of vectorial capacity and R0 that are operationally useful for targeting malaria control and elimination efforts ., Outputs from this modeling framework can improve the outcomes of malaria control efforts and set a foundation for future targeting efforts across the spectrum of transmission for regional malaria elimination ., This project was approved by Ethics and Research Governance of the University of Southampton ( submission #7696 ) ., A basic version of the Ross-Macdonald model 45 has recently been updated to describe the spatial dynamics of malaria in a metapopulation 20 ., The homogeneous version of this model forms the basis for the classical model without human mobility used in this manuscript:, dXdt=mabY ( 1−X ) −rX, ( 1 ), dYdt=acX ( e−μτ−Y ) −μY, ( 2 ), Here X and Y are the proportions of infected humans and vectors respectively ., The parameters r and μ respectively represent the recovery rate of infected humans , and the death rate of infectious mosquitoes ., The parameter m is the ratio of the total number of mosquitoes divided by the total number of humans , and both values are assumed constant through time ., The parameter a is the rate at which mosquitoes bite humans , and b and c are the probabilities of a successfully disease-transmitting bite by an infectious mosquito on a susceptible human , and by an infectious human to a susceptible mosquito , respectively ., The parameter τ is the incubation period in mosquitoes ., The behavior of this model is well-understood , and essentially depends on the value of the basic reproductive number R0 , which relates the number of secondary cases expected from a single case in a completely naïve population:, R0=ma2bce−μτrμ, ( 3 ), Note that R0 can be factored as follows:, R0= ( abμ ) ( mae−μτr ), ( 4 ), From this factorization , we can interpret R0 as the product of the expected number of humans infected by a single infectious mosquito over its lifetime , and the expected number of infectious mosquitoes that arise from a single infectious human over their infectious period ., If R0 < 1 , then all solutions converge to the zero steady state , and malaria parasites are cleared from both mosquitoes and humans ., If R0 > 1 , then there is a unique stable endemic steady state to which all non-zero solutions converge ., A related malaria transmission measure is vectorial capacity , introduced in 25 as:, C=ma2e−μτμ= ( aμ ) ( mae−μτ ), ( 5 ), From the factorization , we can interpret C as the maximal possible rate at which a single infectious mosquito generates secondary infectious mosquitoes ., R0 and C are related via the formula:, R0=bcrC, ( 6 ), The multipatch version of this model is an n patch model in which a Ross-Macdonald model of the form Eqs ( 1 ) and ( 2 ) characterizes each patch , but various patches have different model parameters , indicated by subscripts i = 1 , … , n ., Each isolated patch model has its corresponding local basic reproduction number R0 , i and local vectorial capacity Ci , and these quantities are defined as in Eqs ( 3 ) and ( 5 ) ., The multipatch model incorporates human movement by including the proportion of nights pi , j that human residents of patch i spend in each patch j ( where pi , i is the proportion of nights residents of i spend at home ) ., This movement model more closely resembles short-term movements away from the home than permanent or long-term movements 20 ., These short-term movements are of increasing interest for understanding malaria , as significant importation of malaria can occur due to residents visiting areas with malaria , or through visitors from malaria endemic areas 22–24 ., Only humans move in this model , both for simplicity and because we applied the model at larger spatial scales ( e . g . patches larger than 50 km2 ) where vector movement is comparatively less important 11 ., Since humans move , the reservoir of infectious humans that mosquitoes bite is not limited to a patch’s residents ., We refer to this effective reservoir of infectious humans in patch i as κi ., κi can be calculated as a weighted sum of Xj’s , or the proportion of infectious visitors from j , weighted by the proportion of time they spend in patch i ( i . e . pj , i ) and the sizes of the different patches , Hj ., κi is then given by the expression, κi=∑jpjiXjHj∑jpjiHj, ( 7 ), Incorporation of these spatially heterogeneous effects yields the model:, dXidt=∑jpi , jmjajbjYj ( 1−Xi ) −rXi, ( 8 ), dYidt=aiciκi ( e−μiτi−Yi ) −μiYi, ( 9 ), The proportion of infectious mosquitoes at any time is related to the proportion of the effective local human population that is infectious κi ., We solve eq ( 9 ) for the quasi-equilibrium proportion of infectious mosquitoes as before:, Yi=ciaiκiμi+ciaiκie−μiτi, and substitute this expression into eq ( 8 ) ., This leads to the dynamics that describe the local transmission process in patch i:, dXidt=∑j=1Npijmjaj2bjcje−μjτjκjajcjκj+μj ( 1−Xi ) −riXi, ( 10 ), Using the multipatch model , we estimate local transmission ( i . e . Ci and R0 , i ) in a metapopulation that is assumed to be closed to immigration from outside the defined set of patches , and is assumed to be at its steady state with endemic malaria ., In principle , the local transmission measures ., Ci and R0 , i could be calculated from their respective definitions Eqs ( 3 ) and ( 5 ) , but this requires that various model parameters in the patches are known ., Here we will show how they can be determined from steady state measurements of the multipatch model , and certain patch parameter combinations ., The positive steady state of eq ( 10 ) can be written as:, ∑j=1Npijbjcjkjajcjkjμj+1Cj=riXi1−Xi , i=1 , … , n, ( 11 ), or in matrix form as AC = g ( X ) , where, g ( X ) = ( g1 ( X ) ⋮gn ( X ) ) with , gi ( X ) =riXi1−Xi , C= ( C1⋮Cn ) ,, ( 12 ), and, A=Pdiag ( f ( X ) ) ,, ( 13 ), where P is the connectivity matrix having pij as its ( ij ) th entry , and where, f ( X ) = ( f1 ( X ) ⋮fn ( X ) ) fi ( X ) =biciκiaiciμiκi+1 ., ( 14 ), Therefore , if A is invertible , we can calculate the vector of local transmission capacities:, C=A−1g ( X ), ( 15 ), Note that to calculate C with this approach , we need the values of the following parameters , and parameter combinations: After calculating all values of Ci , the corresponding values of R0 , i can be determined from the relationship R0 , i = ( bici/ri ) Ci ., We note that no additional parameters are needed in this case since bici and ri were needed in the calculation of the vector of local transmission capacities ., Alternatively , the local reproduction numbers R0 , i can be calculated directly by reformulating the steady state expression as:, ∑j=1Npijrjkjajcjkjμj+1R0 , j=riXi1−Xi , i=1 , … , n ,, ( 16 ), and a similar matrix inversion leads to the desired result ., In this case , we need the values of Xi , pij , ri , aici/μi , and Hi , which are the same as in the calculation of the local transmission capacities , except bici is no longer needed ., If we make the assumption that the recovery rates ri in all patches are equal , then they cancel out in the above equations , and are no longer needed to calculate local reproduction numbers ., Testing reasonable ranges for the necessary entomological parameters a , μi , and c ( 0 . 03–1 , 0 . 1–0 . 9 , and 0 . 1–0 . 9 , respectively ) , we found that while absolute values of R0 estimates changed slightly , no patches changed relative to the focus-defining criterion R0 = 1 ., We tested the effect of using recovery rates adjusted by probabilities of seeking treatment from 26 , aggregated to region , and applied to all patches within a particular region ., We made the conservative estimate that if an individual sought treatment for malaria , they were only infectious for one day , while individuals that didn’t seek treatment took the entire period to recover ., The effective recovery rate in a given patch was then r ( 1−t ) +t2 , where t is the proportion of people who sought treatment when feverish ., While most patches changed slightly in absolute R0 estimate , only 7 changed relative to the criterion R0 = 1 under these conditions ., We also assigned recovery rates randomly to patches bounded by the estimates provided by 42 ( bounding t by the region-level minimum and maximum estimates of 27 . 1% and 58 . 3% ) to represent an extreme case of heterogeneity in treatment-seeking behavior ., Across 1000 random assignments the number of patches that changed relative to R0 = 1 was at most 45 , or 11% of all patches ., To quantify R0 , i using prevalence estimates , or estimates reflecting the proportion of people infected with malaria ( Xi in our model ) , we used a gridded prevalence surface estimated from parasite rate surveys 10 ., This gridded surface is freely available at http://www . map . ox . ac . uk/ ., This continuous parasite rate surface was created using a collection of historical Plasmodium falciparum parasite rate ( PfPR ) surveys and remotely sensed across the globe such that surveys near a given point were weighted by distance to inform prevalence at that point ., This predicted surface was validated by comparing predicted with observed prevalence in randomly selected surveys and by comparing classification accuracy based on low , intermediate , and high endemicity classes ( defined as <5% , 5–40% , and >40% PfPR , respectively ) ., Validation statistics were calculated using a hold-out subset of the surveys with a model specifically fitted without these held-out data ., Overall , there was a global mean error of only -0 . 56% and 79 . 5% of surveys were classified as the correct endemicity class , suggesting that this surface is generally accurately predicting parasite rate ., In Namibia , the prevalence surveys used originated from 1985 and 1990 , while data for neighboring Botswana included 1997 surveys and Angola included 2006 and 2007 surveys ., Because most of the data that would inform prevalence across Namibia then originate from before 2000 , we assume that this surface represents a picture of prevalence throughout Namibia before elimination efforts were scaled up in 2004 ., Each patch was a Voronoi polygon around a settlement centroid defined as a combination of urbanized areas , yielding 402 total patches ., The population within each patch Hi was calculated using WorldPop population estimates from 2010 ( freely available from http://www . worldpop . org ) ., We then defined prevalence in each patch as the mean PR for each polygon ., In this study , we compare the predicted map of transmission foci with more recent incidence estimates from 26 , which are presented in this manuscript as-is ., These incidence estimates were modeled at the constituency level ( second level administrative unit; 108 total ) and modeled incidence using routinely collected health management information systems ( HMIS ) data ., Because these estimates only reflect cases that presented at health facilities , this incidence map used a model of treatment seeking behavior to adjust observed malaria cases based on test positivity rates and health facility utilization and define health facility catchment populations ., The catchment population model was calibrated using a malaria indicator survey from 2009 in Namibia ., The final model was the best of several models tested compared using deviance information criterion and the conditional predictive ordinate ., The authors tested overall predictive performance of the final model by calculating a Pearson correlation coefficient for the best model using a hold-out set , which was calculated to be 0 . 56 ., These incidence estimates represent a highly transient near-elimination period , as malaria burden has declined steadily since 2004 27 ., We pair the pre-elimination prevalence surface with individual-level movement patterns obtained from a mobile phone dataset from Namibia , originating from 2010 to map historical transmission foci ., Though the time period of the movement and prevalence datasets , recent studies suggest that human movement is typically regular and predictable across temporal scales 40 and recently-acquired mobile phone data from Namibia ( 2011–2014 ) suggest that movement patterns have been broadly regular seasonally and stable across years ., Because of this , and because of Namibia’s political stability since independence in 1990 , we assume that these 2010 mobile phone data likely reflect movement patterns similar to those that would have been observed in 2000 ., From October 2010 to September 2011 , a total of 9 billion communications from 1 . 19 million unique SIM cards were identified in the dataset , representing 85% of the estimated 1 . 4 million adult ( aged over 15 years old ) population of Namibia United Nations , “Volume I: Comprehensive Tables . ” ., We obtained these data through written agreements between Mobile Telecommunications Limited ( MTC ) , the NVDCP , and the Clinton Health Access Initiative ( CHAI ) ., These data are owned by MTC , who provided permission for publication of this manuscript given its use of these data ., A map of cell tower coverage can be found online at http://www . mtc . com . na/coverage , and S1 Fig shows population density estimates throughout Namibia obtai

Introduction, Results, Discussion, Methods

Humans move frequently and tend to carry parasites among areas with endemic malaria and into areas where local transmission is unsustainable ., Human-mediated parasite mobility can thus sustain parasite populations in areas where they would otherwise be absent ., Data describing human mobility and malaria epidemiology can help classify landscapes into parasite demographic sources and sinks , ecological concepts that have parallels in malaria control discussions of transmission foci ., By linking transmission to parasite flow , it is possible to stratify landscapes for malaria control and elimination , as sources are disproportionately important to the regional persistence of malaria parasites ., Here , we identify putative malaria sources and sinks for pre-elimination Namibia using malaria parasite rate ( PR ) maps and call data records from mobile phones , using a steady-state analysis of a malaria transmission model to infer where infections most likely occurred ., We also examined how the landscape of transmission and burden changed from the pre-elimination setting by comparing the location and extent of predicted pre-elimination transmission foci with modeled incidence for 2009 ., This comparison suggests that while transmission was spatially focal pre-elimination , the spatial distribution of cases changed as burden declined ., The changing spatial distribution of burden could be due to importation , with cases focused around importation hotspots , or due to heterogeneous application of elimination effort ., While this framework is an important step towards understanding progressive changes in malaria distribution and the role of subnational transmission dynamics in a policy-relevant way , future work should account for international parasite movement , utilize real time surveillance data , and relax the steady state assumption required by the presented model .

For countries considering pursuing malaria elimination , understanding where malaria transmission occurs is crucial for intervention planning ., By identifying the areas that act as sources of malaria parasites , elimination programs can target efforts to end local transmission and achieve nationwide elimination ., Mapping parasite sources requires a modeling framework that integrates malaria burden and human movement information , however , as human mobility facilitates parasite spread and drives source-sink disease dynamics ., In this study , we present a mathematical model that can be used to identify areas with self-sustaining malaria transmission when analyzed at equilibrium ., We demonstrate how this method can inform elimination planning for countries with stable low transmission using data from Namibia ., The maps of sources and sinks created using this method can be used to direct policy and target areas with self-sustaining malaria transmission in countries with stable transmission ., Finally , we compare the predicted extent of transmission foci with more recent maps of incidence , to determine whether local transmission likely retreated into focal areas and the potential importance of importation .

invertebrates, medicine and health sciences, namibia, engineering and technology, tropical diseases, geographical locations, social sciences, human mobility, parasitic diseases, parasitic protozoans, animals, cell phones, protozoans, insect vectors, africa, human geography, public and occupational health, geography, malarial parasites, epidemiology, disease vectors, insects, communication equipment, arthropoda, people and places, mosquitoes, equipment, earth sciences, biology and life sciences, malaria, organisms

journal.pcbi.1005919

Local motion adaptation enhances the representation of spatial structure at EMD arrays

Spatial vision is a fundamental challenge for animals moving in cluttered environments , and there is no exception for flying insects ., Because of their small brains insects have to rely on parsimonious principles to compute spatial information about their environment ., Possessing eyes that are close together , binocular spatial vision is no option in the spatial range that is behaviorally relevant for flight control ., Alternatively , optic flow , i . e . the displacement of projections of surrounding objects on the retina during an animal’s locomotion , may provide the information needed about the surrounding depth structure ., However , optic flow cues only provide depth information during translational self-motion , i . e . self-motion with the gaze direction kept constant over time ., During pure rotations the retinal images of surrounding objects are displaced with the same angular velocity irrespective of distance 1 ., Insects , such as flies and bees , shape their flight into rapid saccadic turns of head and body and translational segments where the gaze is largely kept constant 1–6 ., This behavioral strategy ‘purifies’ the translational flow by separating it from the rotational one and potentially serves the function of simplifying the computation of depth information ., Optic flow is not readily available at the input level of the visual system ., Rather , motion detectors are required to compute optic flow information from the spatiotemporal retinal brightness changes induced during locomotion ., In the visual systems of insects retinal intensity changes are encoded in membrane-potential changes by arrays of photoreceptors ., The photoreceptor responses are band-pass filtered in the first visual neuropile , the lamina ., The output of lamina cells is then used to compute local motion in the next neuropile , the medulla ( e . g . 7 ) ., Several variants of a particular model of motion detection , the correlation-type elementary motion detector ( EMD ) , have been suggested to account for the functional properties of the insect motion detection circuit 8–10 ., As a common feature of all these model variants , motion is detected by correlating the non-delayed signal originating from one retinal input with a temporally delayed signal originating from a neighboring input ., This model can successfully explain not only a wide range of electrophysiological data on the large-field motion sensitive lobula plate tangential cells ( LPTCs ) , which spatially pool over arrays of EMDs , but also motion-induced behavior such as optomotor following ( review: 9 , 11 ) ., With genetic tools , more and more details about the neuronal basis of the motion detector circuits are being unraveled 12–20 ., It has been shown in modeling studies that signals represented at the output of EMD arrays correlate well with the contrast-weighted nearness during behaviorally shaped translational self-motion 21 , 22 ., Like photoreceptors , which adaptively encode light intensities , the neuronal circuits for motion detection are adaptive to motion ., Adaptation is a general feature of neurons encoding information about the environment and allows to encode physical parameters that can vary over several decades by neurons with a limited operating range ., Moreover , adaptive coding can also reduce redundancies in the sensory input , enhance changes in the signals , and may support energy efficiency of the neural computations 23–25 ., Since local motion detectors are difficult to access in electrophysiological experiments , most experimental evidence for adaptation of the motion detection pathway was obtained in LPTCs that are post-synaptic to the local motion detection circuits 26–32 ., One major adaptive feature observed in LPTCs is the reduction of the cell responses during constant-velocity motion with retained or even enhanced sensitivity to brief velocity changes 26 , 27 ., This adaptive feature has been concluded to be generated , to a large extent , pre-synaptically to the LPTCs by a local retinotopic mechanism , although the exact location of this mechanism is still an open question 26 ., Cluttered environments cause fluctuations in velocity across the retina under natural flight conditions , especially during translational flight at a constant velocity because of discontinuities in the depth structure of the surroundings ., Therefore , we hypothesize that motion adaptation may enhance the representation of spatial information at the level of arrays of motion detectors ., Following the same idea , Liang et al . 30 simulated the optic flow experienced by a free-flying fly in a box covered with photographs of a meadow scenery and a black cylinder positioned close to the loop-shaped flight trajectory ., By repeatedly presenting this behaviorally generated optic flow to a fly , while recording from an LPTC , the consequences of motion adaptation for representing the cylinder in the neural response could be analyzed ., Whereas responses to the walls of the flight arena were reduced by adaptation , the responses to the cylinder remained large 30 ., Hence , the wide-field motion sensitive neuron became more sensitive to a nearby object relative to its background as a consequence of adaptation ., In the present study , this hypothesis was systematically tested and validated by model simulations ., First , we developed an adaptive model of the visual motion pathway of insects that captures benchmark features of motion adaptation as analyzed in previous electrophysiological studies on LPTCs 26–28 ., Our adaptive EMD model is based on an adaptation mechanism similar to the mechanisms previously proposed for light adaptation by photoreceptors 22 , here however , operating on the output of EMDs and with much larger time constants ., Based on this adaptive model of the visual motion pathway , our intention was to understand how motion adaptation affects the signal representation at the output of arrays of motion detectors and , in particular , the representation of the spatial layout of the environment during translational self-motion in 3D environments ., With simulations of an insect model translating in both simple virtual and naturally cluttered 3D environments , we show that by reducing the response to background motion and maintaining large responses to nearby objects , motion adaptation can make nearby objects more salient ., The conclusion that motion adaptation facilitates the segregation of nearby objects from their background during translational flight was further validated by taking the natural flight dynamics of insects into account ., The adaptive model of the fly visual motion pathway was first tested with visual stimuli that were used in previous electrophysiological studies on fly LPTCs 26–28 ., The characteristic responses of the LPTCs were used as a benchmark to adjust the model parameters of our adaptive model ., When presenting a sine-wave grating moving at a constant velocity superimposed by short-velocity increments as in Kurtz et al . 27 , both model and LPTC responses decayed over time ( Fig 2A ) ., However , the short response increments induced by the increments in velocity were not reduced , but even slightly increased over time ( Fig 2A ) ., Both model and cell responses revealed similar adaptive features when the constant-velocity motion was superimposed by velocity decrements: While the overall response amplitude considerably decreased , the response decrements evoked by the velocity decrements were even enhanced over time ( Fig 2B ) ., A characteristic feature of both biological and model motion detectors is the bell-shaped steady-state velocity tuning: i . e . the motion detector response increases with velocity up to a certain velocity and then decreases again if the velocity further increases ., Similar adaptive features as just described for the rising phase of the velocity-response characteristic ( Fig 2A and 2B ) were observed when the constant background velocity was on the downward-sloping side of the bell-shaped velocity-response characteristic ( Fig 2C ) as well at its optimum ( Fig 2D ) ., Note that , in Fig 2C transient velocity increments evoked transient response decrements , while in Fig 2D transient velocity decrements evoked fluctuations around background response level ., In conclusion , while motion adaptation leads to a reduction of motion-induced responses on a slow timescale , it enhances the relative sensitivity of both LPTCs and the adaptive model of the visual motion pathway to velocity transients under a wide range of stimulus conditions ., Since peripheral brightness adaptation implemented in our model , which is in a steady state already within several hundreds of milliseconds 22 , it does not much contribute to the described adaptive decay of the background activity and the enhancement of transient response on a timescale of several seconds as is characteristic of motion adaptation ., In order to systematically assess under which conditions the relative sensitivity to velocity increments ( Fig 3A and 3B ) and decrements ( Fig 3C and 3D ) is enhanced by adaptation we used the same stimulus scheme as in Fig 2 and systematically varied the velocity and the brightness contrast of the grating ( Fig 3B and 3D ) ., The sensitivity to each velocity transient was quantified by calculating the response contrast between the response to the velocity transient and the response to the constant background velocity:, C r e s p = | R b g - R p k | R b g + R p k ( 5 ), In Eq ( 5 ) Cresp is the response contrast; Rbg represents the LPTC model response to the constant background velocity calculated as the average response over 200 ms before the transient response ( Fig 3A and 3C , color-coded in green ) ; and Rpk is the peak response within the transient response range ( Fig 3A and 3C , color-coded in red ) ., To assess whether the response contrast was enhanced with adaptation we subtracted the response contrast to the first transient from the last one and used this value as an enhancement score ( Fig 3B and 3D , color code in black square frames ) ., A positive enhancement score ( Fig 3B and 3D , warm colors ) indicates an enhancement of response contrast to velocity transients , whereas a negative score ( Fig 3B and 3D , cold colors ) indicates an attenuation of the response contrast ., An enhancement of response contrast to velocity decrements ( Fig 3B ) as well as increments ( Fig 3D ) is evident under most examined stimulus conditions of brightness contrast and velocity as revealed by the dominantly warm-colored heat maps ., As a consequence of brightness adaptation in the peripheral visual system , this performance was maintained even if the overall pattern brightness was increased by up to 8 decades ( Fig 3B and 3D smaller plots ) ., We tested the model with another type of stimulus as used in a previous electrophysiological study on motion adaptation ., As Harris et al . 28 tested the adaptive performance of fly LPTCs , we tested how the response to a velocity step of our adaptive model was affected by adaptation stimuli moving in the preferred direction ( PD ) , the null direction ( ND ) , as well as orthogonal to these directions ( Fig 4A–4C ) ., The LPTC model response resembled that of LPTCs in the following qualitative features: The responses after adaptation were considerably smaller than the reference responses before adaptation irrespective of the direction of motion during adaptation ( Fig 4A , 4B and 4C ) ., Even if orthogonal pattern motion was used for adaptation , the adaptive effect was present , although both model and LPTCs almost did not respond to the adaptation stimulus ( Fig 4C ) ., In the electrophysiological recordings the initial part of the test phase after PD adaptation was less depolarized for a short time interval than that after ND adaptation ( see Figure 2a in 28 ) ., This was not the case in the corresponding model response ( Fig 4A and 4B ) ., The observed difference between model and experimental data is mainly due to the after-hyperpolarization , which occurs at the LPTC level after a strong depolarization of the cell ., Since our present study focuses on the impact of motion adaptation on the EMD-level , the after-hyperpolarization generated in the postsynaptic LPTC has not been taken into account ., Harris et al . 28 further assessed the modulation of contrast gain by motion adaptation ( see Figure 2a in 28 ) by systematically varying the brightness contrast of the reference and test stimulus and comparing response amplitudes before and after motion adaptation ., Part of the response characteristics revealed in their study can be explained by our model , such as the rightward-shift of the contrast-gain curve after motion adaptation ( Fig 4D ) ., Our model successfully accounts for the reduction of the contrast gain after both PD adaptation and ND adaptation ., However , our model does not explain two other response characteristics described by Harris et al . 28 , namely the after-hyperpolarization following PD motion adaptation and the corresponding reduction of the output range of the cell ( Fig 4D ) ., Both response characteristics have been concluded to occur at the LPTC level 40 which is not covered by our current model ., The above model was used to investigate the impact of motion adaptation on the representation of spatial information at the level of arrays of motion detectors ., This was done by simulating the visual input as experienced during translational motion in both virtual 3D environments ( Figs 5 and 6 ) and cluttered natural 3D environments ( Fig 7 ) , employing pure translational motion ( Figs 5 , 6 and 7 ) or mimicking natural flight dynamics of flies ( Fig 8 ) ., Several previous modeling studies have been dedicated to explain motion adaptation in the fly visual pathway at the level of LPTCs 43–46 and to decompose the components of the mechanisms involved 28 ., Clifford and Ibbotson 45 explained the reduction of the cell response to constant-velocity motion , while maintaining or enhancing sensitivity to brief velocity changes 26 , 27 by adaptive changes of the EMD low-pass filter time constant by feedback control ., This time constant is specific for the motion detection circuit and , especially , for determining its velocity tuning ., In contrast , the adaptation mechanism proposed in the present study is a more general-purpose feed-forward adaptive model ., The computational principle underlying this mechanism can be used at different stages of the visual pathway to explain , after adjustment of the time constants to the particular functional needs , brightness adaptation of photoreceptors as well as motion adaptation of the motion detection circuits ., This simple adaptation mechanism does not only explain the enhancement of response contrast with motion adaptation for a wide range of test conditions ( Figs 2 and 3 ) ., It also explains that motion adaptation in fly LPTCs is to a large extent direction-independent ( Fig 4; 28 , 44 ) , and reproduces the reduction of contrast gain ( Fig 4; 28 ) ., We could not validate these features ( Figs 2 , 3 and, 4 ) by re-implementing and testing the model of Clifford 45 ( see 47 ) ., Harris et al . 28 analyzed the adaptive properties of LPTCs by confronting them with grating motion before and after a period of motion adaptation in PD , ND or in the orthogonal direction ., They attributed motion adaptation observed in LPTCs to three components: ( 1 ) a motion-dependent , but direction-independent contrast gain reduction , ( 2 ) a strong direction-selective after-hyperpolarization , and ( 3 ) an activity-dependent reduction of the response range ., Amongst these adaptive components , our model can account for the direction-independent contrast gain reduction ( Fig 4 ) ., The other two components of motion adaptation characterized by Harris et al . 28 are not covered by the present model ., This finding is in line with the conclusion that these components of motion adaptation have their origin post-synaptic to the EMDs at the LPTC level 40 ., As pointed out above , it has not been the goal of the present study to model LPTCs , but to study the impact of local motion adaptation on the signal representation of environmental information at the level of EMD arrays ., However , in principle , depending on the signal used to adapt each branch of the half- detector , this model can be adjusted to also account for direction-dependent motion adaptation ., Another modeling study on motion adaptation of LPTCs attempted to explain a different response feature of LPTCs , i . e . the shortening of the response transients induced by motion steps and motion impulses after adaptation 29 , 46 ., This feature might potentially further enhance the representation of discontinuities in the optic flow pattern by increasing the temporal resolution of motion detectors ., On the other hand , this model based on adapting time constants of filters in the cross-branches of the EMDs before the multiplication stage 46 cannot explain the adaptive benchmark features examined in this study ( own results based on a reimplementation of the model of 46; Supplementary S1 Fig ) ., Since motion adaptation in our model was realized at the output of the EMD half-detectors rather than by interfering with motion computation itself , this adaptive mechanism could also be applied at the output of other types of motion detector models such as recently published motion detector models combining preferred-direction enhancement and anti-preferred direction inhibition 10 ., It was already in the fifties of the last century that each stage of signal processing in nervous systems had been suggested to reduce redundancy in order to efficiently use the limited information capacity of neurons and to extract eventually ecologically relevant information 48 ., Given the limited coding capacity of all processing stages of a nervous system , it is expected for each layer of neurons to be adaptive , i . e . to be able to adjust its input-output relationship according to recent input history ., Examples from insect visual systems ( but restricted neither to the visual modality nor to insects 49–51 ) are brightness adaptation in photoreceptors and LMCs 52 , 53 , motion adaptation at the level of local motion detectors ( although measured in large-field motion sensitive cells , 26 , 27 , 29 , 43 , 54 ) , and wide-field motion adaptation at the level of LPTCs 28 , 40 ., It is generally assumed from the perspective of information theory that adaptive coding provides the advantage of an efficient use of the coding capacity of neural circuits by removing redundant ( i . e . unchanging or only slowly changing ) signals based on the recent input history 49 , 50 ., Redundancy reduction can increase information transmission 23 , 24 and save encoding energy 25 , 55 ., There have been several studies revealing adaptive features based on electrophysiological experiments on LPTCs using various system-analytical stimuli 26–29 , 43 , and a major component of the adaptive mechanisms is suggested to occur locally pre-synaptic to the LPTCs 26 , 29 , 43 ., However , how local motion adaptation affects signal representation in the responses of motion detector arrays during flight in the three dimensional world has by now only been analyzed experimentally in an indirect way at the level of LPTCs 30 , 31 , but due to methodological constraints not at the level of the array of their pre-synaptic local input elements ., With our adaptive model of the visual motion pathway , it was possible to analyze the impact of local motion adaptation on the signal representation at EMD arrays , at least by simulation approaches ., In this way , we found that , as a consequence of motion adaptation , the representation of foreground objects in an environment is much more salient at the EMD output than the EMD responses to the background clutter ( Figs 5–7 ) ., Consistent with the experimental results on LPTCs 31 , we could show that this segregation of foreground objects from background clutter is maintained , even if translational flights were interspersed with fast saccades , as are the characteristic of insect flight ( Fig 8 ) ., However , saccades interspersed between translational self-motion segments of the agent attenuate the enhancement of the response contrast between fore- and background and its distance-dependency ., This detrimental influence of saccades on representing spatial information by movement detectors may be counteracted by the experimentally established efference copy signals that were found to suppress saccade-driven visual motion responses 41 , 42 ., What is the functional significance of an enhancement of nearby contours at the movement detector output resulting from motion adaptation ?, This question cannot yet be answered , because not much is known about how the output of EMD arrays , apart from being LPTC input , is processed ., Furthermore , closed-loop control as is characteristic of most behaviors may add complexity to our understanding of the role of local motion adaptation ., If the enhancement measured in our model simulations is sufficient to substantially change the detectability of objects is hard to assess without making assumptions on the signal-to-noise situation in a real system and the structure of the following processing steps ., In principle , the information provided by motion detector arrays during self-motion may serve later-stage signal processing subserving a wide range of behavioral tasks , such as ( 1 ) optic flow-based spatial vision which is important for detecting objects 56 , collision avoidance 57 , 58 and landing 59 , 60 , ( 2 ) gaze stabilization during locomotion 2 , 3 , 61 , ( 3 ) flight speed control 57 , 62 and ( 4 ) visual odometry 63 , 64 ., The impact of motion adaptation on signal processing in these behavioral contexts is still not clear ., However , one potentially important aspect is that local motion adaptation at the EMD level is largely direction-independent ( 28; Fig 4 ) ., This feature could be functionally important in maintaining equal adaptive states and , thus , equal sensitivity of local motion detectors with different preferred directions ., If the sensitivity of differently aligned motion detectors is changed by an adaptive mechanism depending on the direction of motion , the population responses of such detectors would indicate different directions of local motion in response to a given motion direction-depending stimulus history ., Thus , direction-independent adaptation might be important in behavioral contexts where a correct representation of local motion direction is essential ., Although this model study is based on the electrophysiological data and flight data from blowflies , there is no reason why the adaptive model and the conclusions about how local motion adaptation enhances the segregation of foreground objects from their cluttered background in optic flow-based spatial vision should be restricted to flies ., Moreover , the model may also be useful for implementing artificial motion vision systems .

Introduction, Results, Discussion

Neuronal representation and extraction of spatial information are essential for behavioral control ., For flying insects , a plausible way to gain spatial information is to exploit distance-dependent optic flow that is generated during translational self-motion ., Optic flow is computed by arrays of local motion detectors retinotopically arranged in the second neuropile layer of the insect visual system ., These motion detectors have adaptive response characteristics , i . e . their responses to motion with a constant or only slowly changing velocity decrease , while their sensitivity to rapid velocity changes is maintained or even increases ., We analyzed by a modeling approach how motion adaptation affects signal representation at the output of arrays of motion detectors during simulated flight in artificial and natural 3D environments ., We focused on translational flight , because spatial information is only contained in the optic flow induced by translational locomotion ., Indeed , flies , bees and other insects segregate their flight into relatively long intersaccadic translational flight sections interspersed with brief and rapid saccadic turns , presumably to maximize periods of translation ( 80% of the flight ) ., With a novel adaptive model of the insect visual motion pathway we could show that the motion detector responses to background structures of cluttered environments are largely attenuated as a consequence of motion adaptation , while responses to foreground objects stay constant or even increase ., This conclusion even holds under the dynamic flight conditions of insects .

Insects , with their limited brain resources and high performance in a wide behavioral repertoire , are exquisite model systems for studying parsimonious signal processing ., They extract spatial information by actively shaping their self-motion ( e . g . when performing peering movements or during flight segments with fixed gaze ) and estimate distance according to the speed of the resulting retinal displacements ., The computation of retinal speed is accomplished by arrays of motion detector circuits retinotopically arranged in the second neuropile layer of the visual system ., Sharing general adaptive response characteristics with other neurons and neuronal circuits , the responses of motion detectors depend on stimulus history ., In the present study , we developed a novel adaptive model of the visual motion pathway of insects and analyzed the consequences of motion adaptation for computing spatial information about the 3D environment ., We found that motion adaptation facilitates the segregation of nearby objects from their cluttered background during dynamic locomotion ., The functional significance of motion adaptation is likely to generalize to optic flow-based spatial vision in other animals , and the motion adaptation mechanism implemented in our model could also be useful for artificial visual systems .

velocity, medicine and health sciences, classical mechanics, engineering and technology, motion detectors, social sciences, neuroscience, insect flight, biological locomotion, flight (biology), vision, sensory physiology, animal cells, sensory receptors, physics, visual system, signal transduction, psychology, cellular neuroscience, detectors, cell biology, equipment, physiology, neurons, photoreceptors, biology and life sciences, sensory systems, physical sciences, sensory perception, cellular types, afferent neurons, motion

journal.pcbi.1000099

Regulation of Signal Duration and the Statistical Dynamics of Kinase Activation by Scaffold Proteins

In the context of signal transduction , cells integrate signals derived from membrane proximal events and convert them into the appropriate cell decision ., Within the complex networks that integrate these signals lies a highly conserved motif involving the sequential activation of multiple protein kinases ., Signal propagation through these kinase cascades is often guided by a scaffolding protein that assembles protein kinases into a multi-protein complex ., Signaling complexes maintained by scaffolds are intensely studied and have been shown to affect myriad cell decisions 1–7 ., Despite numerous advances in the understanding of the signaling function of scaffold proteins 8–15 , many questions remain ., For instance , although scaffolds are believed to have profound effects on the dynamics of signal propagation 6 , 9 , 10 , 16 , the mechanisms that underlie how scaffolds regulate signaling dynamics are not well understood ., One key factor in specifying a cellular decision is the duration of a signal ( i . e . the time over which a kinase remains active ) 17 , 18 ., Differences in signal duration have been implicated as the basis of differential decisions in myriad cell processes ., For example , it has been suggested that decisions on growth factor induced cell proliferation , positive and negative selection of T cells , apoptotic programs , cell cycle progression , among many others , are regulated by the duration of signaling 19–24 ., Therefore , the issue of how a signal output , such as the activity of extracellular regulatory kinase ( ERK ) in a MAPK pathway , is distributed over time , is of considerable interest ., There are many ways in which the duration of the output of a kinase cascade can be controlled ., Regulation of signaling dynamics can arise from processes upstream of the cascade 25 ., For example , degradation of upstream signaling components such as the surface receptors 26 and differential kinetics of GTPase regulators 27 , 28 can be essential in regulating MAPK signaling dynamics 25 ., Also , multisite phosphorylation is predicted to influence signal duration 29 ., It has been also been shown that differential modes of feedback regulation that are manifested under different conditions within the same cascade can regulate signal duration 30 ., Scaffold proteins have also been implicated as key determinants in the regulation of signal duration 9 , 10 , 31 ., Because the many factors that control scaffold mediated signaling are difficult to systematically control in a laboratory setting , a precise understanding of how scaffold proteins affect the dynamics of signal transduction has proven elusive ., Computational models have been useful in understanding some of the many complex ways in which scaffolds influence signal transduction 16 , 32–34 ., However , it is currently impossible to model theoretically all aspects of any biological signaling process—computational models ultimately require that many gross simplifications be made ., Our aim is , therefore , not to attempt to simulate every detail of a specific biochemical pathway but rather investigate the consequences that emerge from a simple scenario of scaffold mediated signaling whereby a model cascade assembles onto a scaffold ., In modeling this scenario in itself , we hope to learn more about the functional and mechanistic consequences that these specific physical constraints , imposed by assembling components of a biochemical cascade onto a scaffold , confer to signaling pathways ., In parsing these effects from the myriad others that are undoubtedly important , our hope is that our results can serve as a framework for understanding the extent to which these effects are important in specific biological contexts such as the Mitogen Activated Protein Kinase ( MAPK ) pathway ., One theoretical analysis of scaffold mediated cell signaling revealed the presence of non mononotic behavior in signal output as a function of scaffold concentration 34 ., If scaffolds are required for signaling , then too few scaffolds will be detrimental to signaling ., On the other hand , if scaffolds are present in excess , signaling complexes become incompletely assembled and the signal output is attenuated ., As a consequence of this “prozone” effect , scaffolds were shown to also differentially affect the kinetics of signaling ., The observation that scaffolds can differentially affect signaling dynamics leads to many questions ., How do scaffold proteins control the time scales involved in signal propagation ?, An important metric of cell signaling is the time it takes for a downstream kinase to become active 35 , 36 ., As signal transduction is stochastic in nature , the more precise question is: what is the distribution of times characterizing the activation of a downstream kinase ?, How do scaffolds affect this distribution , and what might be the biological consequences of changes in this distribution as a result of signaling on a scaffold ?, We compute first passage time distributions 37 using a stochastic computer simulation method to investigate these questions ., Specifically , we use a kinetic Monte Carlo algorithm ., We have previously used such methods to study a different question concerning the regulation of signal amplitude by scaffold proteins 33 ., It is also possible that a differential equation model that considers mean-field kinetics could be used to study the first passage time distribution 37 ., However , such an approach would require the imposition of absorbing boundary conditions that can make the numerical analysis difficult ., Our simulation results suggest that , depending on physiological conditions , scaffold proteins can allow kinase cascades to operate in different dynamical regimes that allow for large increases and decreases in the speed and characteristic time scale of signal propagation ., Furthermore , and perhaps more importantly , scaffolds are shown to influence the statistical properties of the times at which kinases are activated in complex ways ., Scaffolding protein kinases cascades can allow for broadly distributed waiting times of kinase activation , whereas in the absence of a scaffold , the time it takes for a kinase to be activated is effectively characterized by a single time scale ., These stochastic characteristics of scaffold-mediated kinase cascades are , to our knowledge , elucidated for the first time and may have diverse biological consequences that pertain to how signal duration is regulated ., It is also our hope that our results provide a framework for achieving a deeper qualitative understanding of how scaffolding proteins can regulate the dynamics of cell signaling and the statistical properties of signal transduction ., For our study , we considered a model three tiered protein kinase cascade such as the MAPK pathway 38 ., Since our aim is to study the effects of spatially localizing protein kinases on signaling dynamics , we considered a minimal description of a model kinase cascade ., Many factors that are undoubtedly important in regulating signaling dynamics were not considered ., These factors include feedback regulation within the cascade , allosteric and or catalytic functions provided by the scaffold , and the effects of multiple phosphorylations of each kinase 11 , 25 , 26 , 30 , 39 , 40 ., In our model , signal propagation occurs in a three step hierarchical fashion: an initial stimulus ( S ) activates a MAP3K ( A ) that in turn , activates a MAP2K ( B ) , that subsequently can activate its MAPK ( C ) substrate—phosphatases can deactivate each activated species and this deactivation occurs regardless of whether or not the active kinase is bound to a scaffold ., A schematic is presented in Figure 1A that illustrates the basic processes that are allowed in our model ., A steady-state ensemble is considered ., That is , simulations are allowed to first reach a dynamic steady-state and once this state is reached , dynamics are studied ., We do not consider dynamics from the starting time that requires propagation through a hierarchical cascade ., Recent work has studied the statistical dynamics of kinase activation that result from the hierarchical organization of a kinase cascade; in that study , it was shown that the hierarchical structure of the cascade gives rise to broad waiting time distributions of cascade activation ., In the regime that we study here , these effects are absent since activation of the cascade requires that an inactive C protein encounter an active B protein; our motivation is thus to investigate how the dynamics of kinase activation can be affected by assembling components of the cascade onto a scaffolding protein that localizes single complexes ., Therefore , we do not emphasize how the hierarchical structure of a signaling cascade effects signal propagation and instead focus on how assembly of the cascade onto a scaffold affects signaling dynamics ., We also underscore the notion that in our approach , many undoubtedly important effects such as the hierarchical structure of protein kinase cascades , the influence of feedback loops , differential enzymatic mechanisms and allosteric control by scaffolds are neglected ., Again , by excising these effects , we restrict our attention to a hypothetical scenario that aims only to investigate the consequences of assembling components of a cascade onto a scaffold protein ., The key quantities computed and parameters used are discussed below in Table 1 and Figure 1B ., Additional details are provided in the Methods section ., To set the context , consider the consequences of signaling in two limiting cases in our model ., When the binding affinity of the kinases to the scaffold , E , is low ( defined here to be close to the thermal energy , E∼kBT; kB is Boltzmans constant and T is the temperature ) and kinases disassociate rapidly from the scaffold , few proteins on average are bound to a scaffold ., Therefore , signaling dynamics corresponds to that of a kinase cascade in solution ., For a very strong affinity , E≫kBT all available binding sites to scaffold proteins are occupied by kinases ( on average ) ., In this case , signaling dynamics are controlled by the time required for initial stimuli to encounter and interact with each fully assembled complex ., Therefore , we consider cases in which kinases can disassociate from their scaffolds and exchange with unbound kinases on time scales pertinent to cell signaling processes ., Such time scales correspond to disassociation constants ( Kd ) on the order of 1–10 µM and off rates , koff∼1s−1 ., Such Kd values correspond to free energies of binding of roughly 7–9 kcal/mol , an energy scale typical of protein-protein interactions in kinase cascades 41 ., We have used 12 kBT as the binding energy in our simulations which corresponds to ∼7 . 2kcal/mol ., We also discuss the robustness of our results with respect to changes in this value ., Scaffold concentration has been identified as a key variable that can regulate the efficiency of signal propagation through a kinase cascade 2 , 5 , 34 ., For the set of parameters used in the simulations ( Table 1 ) , signal output ( defined as the average steady state value of the final kinase in the cascade ) has a non-monotonic ( biphasic ) dependence on the relative concentration of scaffolds ζ ( , where Scaffold is the concentration of the scaffold and MAP3K0 is the concentration of the first kinase in the cascade ) and peaks at an optimal value of ζ\u200a=\u200a1 33 , 34 ., To quantify signaling dynamics , we consider a survival probability S ( t ) ( methods ) that , as mentioned , can be viewed as a type of autocorrelation function . where σ ( t ) equals 0 or 1 depending upon the activity of the final kinase within the cascade ( methods ) and the brackets indicate an average over all kinases in the simulation averaged over many simulations ., This quantity gives the probability that the final kinase in the cascade remains inactive at time t given that it was inactive at time t\u200a=\u200a0 . Therefore , signaling dynamics can be monitored by observing the decay of this function with time ., In Figure 2A , S ( t ) is computed for different values of the relative scaffold concentration , ζ ., The intrinsic time of signal propagation , τ , is the value at which S ( t ) decays to e−1 of its original value ( S ( t\u200a=\u200aτ ) =\u200ae−1 ) ., Upon increasing scaffold concentration , τ increases ., At very high scaffold expression levels , signals propagate so slowly that cell signaling is not observed on experimentally measurable time scales which we take to be in our simulations ≫106 Monte Carlo ( MC ) steps; 1 MC step∼1 µs assuming a lattice spacing of 10 nm and a diffusion coefficient of 10 µm2/s 42 ., The increase in τ spans several orders of magnitude as is observed in Figure 2B ., Distinct stages are also observed in the behavior of τ , and are separated by an inflection point occurring shortly past the optimal value of scaffold concentration ( ζ∼1 ) ., This phenomenon suggests that different physical processes are determining the signaling dynamics at different ranges of scaffold concentration ., These results also suggest that the concentration of scaffold proteins can in principle set an intrinsic time scale that determines the speed of signal propagation ., Such an intrinsic time scale arises solely from changes in the concentration of scaffold proteins ., This time scale can span several orders of magnitude for biologically relevant affinities and diffusion coefficients and increases monotonically with increasing scaffold concentration ., Note that these calculations consider only the speed of signaling and do not necessarily imply that signaling is more efficient when τ is small ., To observe the total amount of integrated signal flux , the survival probability is conditioned with the probability that a kinase in the pool of signaling molecules is active in the steady state ., We compute R ( t ) defined as S ( t ) multiplied by the average number of ( the final downstream ) kinases active at steady state , where fA is the fraction of active kinases at steady state ., The time derivative , , can be thought of as a flux of activated kinases being produced ., In Figure 2C , R ( t ) is plotted as a function of time ., For low concentrations of scaffolds , the small amount of signal , albeit quickly propagating , is rapidly quenched ., As scaffold concentration increases , both the amplitude and duration of the signal increase up to an optimal value ., Past the optimal value , higher scaffold concentrations result in signals with small amplitude but the duration of signaling is extended ., The behavior of the integrated reactive flux is a direct consequence of the existence of an optimal scaffold concentration and “bell shaped” titration curve since the area under these curves is proportional to the average signal output 33 , 34 ., Figure 2 emphasizes how the characteristic time for signal propagation is influenced by changes in the relative scaffold concentration ., It also appears that the qualitative features of S ( t ) change as scaffold concentration is varied ., The decay of some distributions appears highly concentrated at a particular time while the decay of other distributions appears more broadly distributed ., To further investigate this observation , we plotted the survival probability as a function of the dimensionless time , t/τ ., If the decay of S ( t ) is purely exponential , then S ( t/τ ) will have the form e−t/τ ., Figure 3 shows S ( t/τ ) for different values of scaffold concentration and a decaying exponential function is given as a reference ., One notices that S ( t/τ ) is exponential at negligible scaffold concentrations ., As scaffold concentration increases , the behavior of S ( t/τ ) deviates from a single exponential decay ., Near ζ\u200a=\u200a1 , S ( t/τ ) shows maximal deviation from purely exponential kinetics ., As scaffold expression increases past this point , the shape of S ( t/τ ) reverts back to an exponential form ., A deviation from exponential behavior can be quantified by considering a stretched exponential function , and fitting S ( t/τ ) to this form for different values of ζ ., One desirable feature of the stretched exponential function is the minimal number of parameters , τ and β , that are involved in the least-squares fit; also , the values of these parameters can be physically interpreted ., τ gives the characteristic time for one overall timescale of signal propagation , and β is a measure of how much the function , S ( t ) , deviates from a single exponential and thus how broadly distributed are the signaling dynamics ., Figure 3B shows how β depends on scaffold concentration ., For these simulations , β∼1 for small and large values of scaffold concentrations indicating exponential behavior ., For intermediate values , β peaks at a minimum of β∼0 . 6 , a significant deviation from purely exponential behavior ., In the limits of small and large scaffold concentrations , the presence of a single exponential decay , β∼1 indicates that signal propagation , or the relaxation of S ( t/τ ) , occurs at one characteristic time scale ., In the intermediate regime , β shows significant deviations from one , thus allowing for a broadly distributed signal ., When β is significantly less than one , signals can steadily propagate over several decades ., In this regime , the waiting time distribution f ( t ) , has a large tail and the activation of kinases is slowly maintained over many time scales ., Why do we observe exponential and non-exponential behavior under different conditions ?, Signal transduction in our model occurs on a time scale that is much slower than the microscopic time scales associated with diffusion , binding/unbinding , and enzyme catalysis ., We might therefore expect that some coarse-graining exists whereby events at these fast , “microscopic” time scales interact with other relevant biophysical parameters ( e . g . scaffold concentration ) to give rise to emergent properties that evolve on slower times scales ., These processes are a manifestation of the collective dynamics of the many processes that occur on faster time scales ., Understanding the factors that govern these emergent time scales would then provide insight into the origin of the different temporal characteristics that are revealed by our simulations ., In order for a signal to propagate ( i . e . for the last kinase in the cascade to become active ) , a hierarchical sequence of phosphorylation reactions among kinases must occur that leads to the final kinase in the cascade being activated by its upstream kinases ., The activation process may occur either in solution or on a scaffold ., Also , in the course of signaling , kinases can exchange from a scaffold ., Some kinases are bound to a scaffold that contains an incomplete assembly of the necessary signaling molecules , and are not signaling competent ., Ultimately , an inactive kinase can exist in one of three states: in solution , bound to a complete complex , or bound to an incomplete complex ., Figure 4A contains a diagram of such a minimal picture and arrows denote transitions between the four states ., This minimalist description clarifies the behavior in Figures 3A and 3B ., For low scaffold concentrations ( ζ≪1 ) , kinases predominately exist in solution and signal transduction is dominated by the time it takes for an upstream kinase to encounter its downstream enzyme ., Since a steady-state ensemble is used , the rate limiting step for signal propagation is the diffusion limited collision between an active B* molecule with an inactive C molecule ., For high scaffold concentrations ( ζ≫1 ) , kinases predominately exist in incomplete signaling complexes and signal transduction is limited by a time scale that characterizes the turnover of a signaling incompetent complex to one that is able to signal ., For intermediate concentrations , inactive kinases can exist in each of three states and transitions between these states also occur ., Thus , the source of the nonexponential relaxation ( i . e . β<1 ) arises from the mixing of many time scales that are relevant for intermediate scaffold concentrations ., Figure 4B illustrates this minimal picture of the kinetics of signal propagation derived from these physical considerations ., Also note that the sensitivity of our results to changes in model parameters can be understood from this simple picture of scaffold mediated signaling dynamics ., For instance , changes in kinase and scaffold concentrations result in changes in the relative amount of kinases existing in the three states in ways that have been previously characterized 33 , 34 ., Changes to other parameters such as the rates of activation and deactivation and the concentration of phosphatases alter the rates of transitions between these different states ., For instance , if phosphatase concentrations are very large , then activation in solution is very slow and occurs predominantly on a scaffold ., Also , slower rates of activation ( and larger rates of deactivation ) result in a larger portion of signaling originating from kinases that are bound to scaffolds ., In general , when the activation of kinases originates more ( less ) predominantly from a particular state in the minimal model , β increases ( decreases ) ., When multiple pathways to kinase activation contribute with comparable time scales , β is small , and signaling is broadly distributed over many time scales ., We have performed many simulations with varying parameters to test the robustness and parameter sensitivity of our findings and find that that the qualitative behavior of our results follow this simple , qualitative , physical picture ., Additional insight can be gleaned from consideration of the power spectrum of S ( t ) ., The power spectrum , where , , computed in the frequency domain , resolves the time scale dependence of kinase activation ., This approach has proven useful in studying the dynamics of complex biochemical networks in many contexts 43–45 ., We first note that S ( t ) obtained from the simulations fits well to the functional form ( χ2 values small ) ., Thus , we use the parameters β and τ that were extracted from the fits at low ( ζ\u200a=\u200a0 . 001 ) , optimal ( ζ\u200a=\u200a1 . 0 ) , and high ( ζ\u200a=\u200a3 . 5 ) scaffold concentrations to compute P ( ω ) for these three cases ., In Figure 5 , ( τopt ) −2 P ( ωτopt ) is plotted versus ωτopt where the time τopt is the characteristic time scale τ for relaxation at the optimal ζ\u200a=\u200a1scaffold concentration ., That is , time is rescaled to units of τopt ., For each curve , at lowωτopt≪1 frequencies P ( ωτopt ) is constant ( P ( ωτ→0 ) →τ2 ) signifying that kinase activation has become uncorrelated ., At high ωτopt≫1 frequencies , kinase activation is correlated and a power law decay is observed for each curve P ( ωτopt ) ∼ω−2 ., As a reference , note that for an exponential decay , S ( t ) =\u200ae−t/τ , the transition between these two regimes occurs at ωτ∼1 and is determined by the Lorentzian: In Figure 5 , for high ( ζ\u200a=\u200a3 . 5 , blue ) and low ( ζ\u200a=\u200a0 . 001 , green ) scaffold concentrations power spectra closely resemble the Lorentzian with the transition to P ( ωτopt ) ∼ω−2 behavior occurring at different frequencies ., At low ζ\u200a=\u200a0 . 001 concentrations , the inverse time scale or corner frequency at which kinase activation decorrelates is determined by the diffusion limited rates of activation and deactivation of the final kinase C* ., The corner frequency can be estimated fromwherek+ and k− are diffusion limited rates of activation and deactivation and are given by a diffusion limited encounter rate that is on the order of D Ntota where D is the diffusion constant used in the simulation , Ntot is the number of proteins , and a is the size of a protein taken to be the size of a lattice site ., Substitution of the numbers used in the simulation ( Table 1 and Methods ) achieves a value for the relaxation time that is commensurate with the relaxation time for ζ\u200a=\u200a0 . 001 in Figure 2; i . e . , τc∼105 mcsteps ., At high ζ\u200a=\u200a3 . 5 concentrations , the corner frequency is determined by rates of formation and disassociation of an intact signaling complex ., Furthermore , because of these many process that comprise the relaxation rate in this case , a numerical estimate of the corner frequency is difficult ., In the case of the optimal ( ζ\u200a=\u200a1 . 0 , red ) concentration , the transition from constant to P ( ωτopt ) ∼ω−2 behavior occurs smoothly over many decades from ωτopt∼0 . 1 to ωτopt∼10 . 0 ., The plot in Figure 5 also resolves different frequency dependent processes occurring in signal transduction ., At high frequencies or short times , ωτopt<10 . 0 , kinase activation is limited by the diffusive motion of the kinases in the cascade ., At intermediate frequencies , 0 . 1<ωτopt<10 . 0 , activation is dominated by transitions between kinases assembled in competent , incompetent , and solution based kinases ., For low frequencies ωτopt<0 . 1 or long times , kinase activation decorrelates for each scaffold concentration ., To illustrate how computed values of S ( t ) and the distribution of waiting times for kinase activation relate to conventional means of defining signal duration , we consider a differential equation for the time evolution of the activated form of the final kinase within the cascade ., In this picture , species become activated at rates derived from the functional form that was fitted to the survival probabilities that were computed from the simulations ., The waiting time or first-passage time distribution f ( t ) is used as a forward rate and the activated final kinase then can be degraded with a kinetics of degradation characterized by a rate constant , kφ ., A kinetic equation describing this process is written as:x is the number of active species , τ is the time constant of signal propagation , and β is the stretching parameter that quantifies deviations away from single exponential behavior ., In this picture , x ( t ) represents the average response to a stimulus f ( t ) that is distributed temporally according to and subject to a first order decay with characteristic time 1/kφ ., The equation for x ( t ) can be solved and using the initial condition , x ( 0 ) =\u200a0:x ( t ) was integrated numerically and is shown for different values of β in Figure 6A ., As seen in Figure 5A , decreasing values of β result in the trajectories having longer tails and thus an extended duration of signaling ., Also , smaller values of β result in the signal having a larger peak ., This property directly follows from the decay of S ( t ) that was shown in Figure 3A for different values of β ., At early times , S ( t ) decays more quickly when β is smaller; as a consequence , more kinases are activated at these times , thus resulting in a larger peak ., This concept of signal duration can be made more precise by considering a threshold amount of signal , T , that is required for the pathway to be considered active ., With a chosen value of T , the signal duration , υ , is defined as the time it takes for the signal to decay to some threshold value , T . That is , the equationis satisfied ., Figure 6B shows the signal duration , υ , as a function β for values of β ranging from 0 . 5 to 1 for different values of T . For smaller values of T , β<1 ( i . e . , scaffolds are present ) results in a large increase in signal duration compared to the case in which β\u200a=\u200a1 . Therefore for a fixed value of τ , the most broadly distributed signal leads to the longest signal duration ., We first showed that scaffold concentration is a key variable in regulating the speed of signal transduction ., Moreover , we showed that the concentration of a scaffold protein can influence signaling dynamics by controlling the distribution of times over which kinases become active ., This type of regulation may have many important consequences that are related to the influence of signal duration on cell decisions ., Controlling the times over which kinases are activated may also be useful in directing a specific , robust response in a number of ways ., Thus , the scaffold concentration itself provides another variable for maintaining signal specificity by controlling signal duration ., This is consistent with data from genetic studies involving KSR1 9 , 10 , where the authors reported that the concentration of KSR1 can control a cell decision involving commitment to adipogenesis ., Our study focused solely on aspects of scaffold mediated regulation of signal transduction and we only considered the times at which kinases are active in the course of signal transduction ., Many other factors also control signal duration ., For example , our study does not consider the negative feedback loops that are often associated with the upregulation of phosphatases 18 , 32 ) or the role of receptor downregulation in controlling signal duration ., Also we did not explicitly consider the role of positive versus negative feedback loops in shaping signal duration which is undoubtedly important 30 ., It was our focus to study how spatially localizing kinases on a scaffold protein influences signal duration ., We aimed to untangle this effect of scaffold proteins from other essential features of kinase cascades such as allostery and feedback regulation ., Also , other theoretical studies have investigated the first passage time statistics in signal transduction cascades and have found interesting dynamics that result from , in part , the sequential activation of multiple steps in a kinase cascade 35 , 36 ., Our studies of signaling through scaffold proteins supplement these findings and , to our knowledge , provide the first study that shows how scaffolds affect the statistics of signal transduction ., Several predictions from our model of how scaffolds regulate signaling dynamics can be tested ., Measurements that monitor the time course of signaling for different scaffold concentrations could potentially resolve the differences in signaling dynamics that are predicted ., Also , single molecule or fluorescence correlation based spectroscopic methods 46–48 could potentially probe the statistics of signaling dynamics inherent in kinase cascades and study how such statistics are related to reliable cell decisions ., Such techniques can monitor the propagation of a signal , at the level of an individual molecule and thus directly measure how kinase activation within a single cell is distributed over time ., We simulate a model protein kinase cascade such as the mitogen-activated protein kinase ( MAPK ) cascade ( Figure 1A ) in the presence and absence of a scaffold with a kinetic Monte Carlo algorithm 49 , 50 , which allows us to monitor the relevant stochastic dynamics ., Since we are investigating phenomena that occurs on the time scales of signal transduction , we course-grain the system so that proteins are represented as discrete objects , occupying a site on a lattice of dimensions 100×100×100 lattice spacings ., Scaffold proteins are modeled as rigid , immobile objects containing three binding sites that are each specific for a particular kinase ., When bound to a scaffold , kinases are tethered in nearest neighbor positions that are proximal to their downstream substrates ., Allowing the scaffold and scaffold-bound species to move does not affect the qualitative results ., Reflecting , no flux ( i . e . Neumann ) boundary conditions exist at each of the faces of the cubic lattice ., The system is not periodically replicated since our simulation box is a size on the order a cell ., Proteins can diffuse ( i . e . translate on the lattice in random directions ) , bind and unbind , and undergo state transformations according to the prescribed reaction network involving a three staged cascade of activation and deactivation events ( Figures 1A and 1B ) ., Protei

Introduction, Results, Discussion, Methods

Scaffolding proteins that direct the assembly of multiple kinases into a spatially localized signaling complex are often essential for the maintenance of an appropriate biological response ., Although scaffolds are widely believed to have dramatic effects on the dynamics of signal propagation , the mechanisms that underlie these consequences are not well understood ., Here , Monte Carlo simulations of a model kinase cascade are used to investigate how the temporal characteristics of signaling cascades can be influenced by the presence of scaffold proteins ., Specifically , we examine the effects of spatially localizing kinase components on a scaffold on signaling dynamics ., The simulations indicate that a major effect that scaffolds exert on the dynamics of cell signaling is to control how the activation of protein kinases is distributed over time ., Scaffolds can influence the timing of kinase activation by allowing for kinases to become activated over a broad range of times , thus allowing for signaling at both early and late times ., Scaffold concentrations that result in optimal signal amplitude also result in the broadest distributions of times over which kinases are activated ., These calculations provide insights into one mechanism that describes how the duration of a signal can potentially be regulated in a scaffold mediated protein kinase cascade ., Our results illustrate another complexity in the broad array of control properties that emerge from the physical effects of spatially localizing components of kinase cascades on scaffold proteins .

Signal transduction is the science of cellular communication ., Cells detect signals from their environment and use them to make decisions such as whether or when to proliferate ., Tight regulation of signal transduction is required for all healthy cells , and aberrant signaling leads to countless diseases such as cancer and diabetes ., For example , in higher organisms such as mammals , signal transduction that leads to cell proliferation is often guided by a scaffold protein ., Scaffolding proteins direct the assembly of multiple proteins involved in cell signaling by providing a platform for these proteins to carry out efficient signal transmission ., Although scaffolds are widely believed to have dramatic effects on how signal transduction is carried out , the mechanisms that underlie these consequences are not well understood ., Therefore , we used a computational approach that simulates the behavior of a model signal transduction module comprising a set of proteins in the presence of a scaffold ., The simulations reveal mechanisms for how scaffolds can dynamically regulate the timing of cell signaling ., Scaffolds allow for controlled levels of signal that are delivered inside the cell at appropriate times ., Our findings support the possibility that these signaling dynamics regulated by scaffolds affect cell decision-making in many medically important intracellular processes .

physics/interdisciplinary physics, cell biology/cell signaling, cell biology/cell growth and division, biophysics/theory and simulation, computational biology/molecular dynamics, computer science/numerical analysis and theoretical computing, computational biology, chemical biology, computational biology/signaling networks, biophysics, biophysics/cell signaling and trafficking structures, computational biology/systems biology, chemical biology/chemical biology of the cell

journal.pgen.1000827

Bacterial Genes in the Aphid Genome: Absence of Functional Gene Transfer from Buchnera to Its Host

The smallest known cellular genomes are those of symbiotic bacteria living in insects 1–4 ., These genomes have lost many genes considered essential in other bacteria , and one proposed explanation is that certain ancestral symbiont genes have been transferred to the host genome , with their products reimported to the symbiont cytosol 1 , 5 , 6 ., This process is known to have occurred in mitochondria and plastids during their evolution as symbiotic associates of eukaryotic cells 7 , 8 ., Because these associations are mutualistic , selection on host genomes could favor maintenance of genes that benefit the prokaryotic associate ., To date , strong evidence for gene transfer from mutualistic symbionts to insect hosts has not been found ., Among the best-known ( though not the most extreme ) small symbiont genomes are those of Buchnera aphidicola ( Gammaproteobacteria ) ( genome size: 420–650 kb ) , the obligate mutualistic symbiont of aphids 9–12 ., Aphids are plant-sap sucking insects that have close associations with various microorganisms ., Most aphid species , including the pea aphid Acyrthosiphon pisum , harbor Buchnera within the cytoplasm of specialized cells called bacteriocytes 13–13 ., Since the initial infection in a common ancestor of aphids more than 100 million years ago 17 , Buchnera have been subjected to strict vertical transmission through host generations , and the mutualism between Buchnera and their host has evolved to the point that neither can reproduce in the absence of the other ., Buchnera cannot proliferate outside bacteriocytes , and when deprived of Buchnera , the host insects suffer retarded growth and sterility , as they are dependent on Buchnera for the supply of essential nutrients 15 , 18–21 ., During the course of coevolution with the host , Buchnera has lost a number of genes that are considered essential for bacterial existence 9–12 ., The genome of Buchnera from A . pisum encodes about 620 genes ( genome size: 650 kb ) , which is only one seventh of that of most related bacteria , such as Escherichia coli 9 ., This raises the question of whether certain genes have been transferred from the genome of ancestral Buchnera to the genome of aphids ., In addition , aphids often contain other bacterial symbionts and pathogens 16 , raising the possibility of LGT from a variety of bacterial lineages ., Indeed , evidence is accumulating for extensive transfer of DNA ( mostly pseudogenes ) from the intracellular bacterium Wolbachia ( Alphaproteobacteria , Rickettsiales ) to its arthropod and nematode hosts 22–28 ., Moreover , previous studies revealed that A . pisum acquired at least two highly transcribed genes from bacteria 29 , 30 , providing strong evidence that laterally transferred bacterial genes can be of functional importance in metazoan recipients ., Recently , the full genome assembly of A . pisum was obtained by the International Aphid Genomics Consortium ( IAGC ) ( IAGC , paper under review ) ., These data provide the first opportunity for an exhaustive search of a genome of an animal that has coevolved with mutualistic intracellular bacteria , including an obligate mutualist with a highly reduced genome ., We screened the A . pisum genome for bacterial sequences using several computational search strategies , and performed phylogenetic and experimental studies on LGT candidates ., We identified a total of 12 genes or gene fragments that seem to have been transferred from bacterial genomes to the genome of an A . pisum ancestor ., Their structures , phylogenetic positions , evolutionary histories , and expression profiles are further discussed in this paper ., Among approximately 4 million sequence reads that were generated for the A . pisum genome project , 90 , 678 reads were removed prior to the assembly of the genome ( Acyr_1 . 0 ) due to low sequence quality or strong similarities to sequences of Buchnera , E . coli ( cloning host ) , or the pUC 18 ( cloning vector ) sequences ( IAGC , paper under review ) ., However , if the A . pisum genome recently acquired DNA fragments from Buchnera , such sequences would show strong similarity to the genomic sequences of Buchnera , and may be inappropriately removed at this stage ., To assess this possibility , we screened the discarded sequences for LGT candidates using three independent methods ., A single sequence read with regions of similarity to bacterial sequences and invertebrate sequences represents a potential candidate for an A . pisum genomic fragment containing laterally transferred bacterial DNAs ., To search for such candidates , we first used all of the 90 , 678 reads as queries , in BLASTX and BLASTN searches conducted against bacterial databases ( see Materials and Methods , Table S1 ) ., This revealed 33 , 686 reads with region ( s ) significantly similar ( BLASTX bit score ≥40 , BLASTN bit score ≥55 ) to bacterial sequences ( Figure S1 , box 1 ) ., Subsequently , these 33 , 686 reads were subjected to BLASTX and BLASTN searches against the RefSeq invertebrate databases ( Figure S1 , box 2 ) , demonstrating that 19 , 624 out of 33 , 686 reads also have region ( s ) significantly similar ( BLASTX bit score ≥40 , BLASTN bit score ≥55 ) to invertebrate sequences ., Of these , 19 , 279 reads contained a single region with similarity to both bacterial and invertebrate sequences; such regions are not related to LGT and instead represent evolutionarily conserved genes , which are widely distributed both in prokaryotes and eukaryotes ( Figure S1 , box 3 ) ., The 345 remaining reads were apparently chimeric and were subjected to BLASTX and BLASTN searches against the National Center for Biotechnology Information ( NCBI ) non-redundant ( nr ) database ( Figure S1 , box 4 ) , and visually inspected one by one ., This revealed that 96 reads were parts of pUC 18 or other vectors; these were discarded ., An additional 20 reads contained low-complexity sequences ( homopolymers or short repeats ) and were judged to be insignificant and removed ., Because we expect any given genomic region to be covered by at least two high quality reads , we removed 36 singletons showing chimeric bacterial-aphid sequences as potential artifacts introduced by cloning/sequencing errors ., The remaining 193 reads showed only weak and unreliable similarity to bacterial or animal sequences , leaving no promising candidates for LGT from this collection of reads ., To further assess this population of precluded reads , we assembled the 90 , 678 discarded reads using phred/phrap ., The assembly produced 5 , 094 contigs from 38 , 813 reads , leaving 51 , 865 reads as singletons ., Using these contigs as queries , BLASTX and BLASTN searches were conducted against the bacterial protein database and the A . pisum genome assembly ( Acyr_1 . 0 ) , respectively ., If a single contig has distinct regions each showing strong similarity to bacterial and A . pisum sequences , such a chimeric sequence would be a promising LGT candidate as mentioned above ., However , no such contigs were found , again indicating that the precluded 90 , 678 reads lack promising candidates for LGT ., To further focus on the possibility of recent LGT from Buchnera , all 90 , 678 reads were subjected to BLASTN searches against the Buchnera genome from A . pisum Buchnera str . APS ( NC_002252 , NC_002253 , NC_002528 ) 9 ., This revealed 26 , 529 reads with significant similarity ( BLASTN bit score ≥55 ) to Buchnera sequences ., After masking the regions similar to Buchnera , the sequences were subjected to BLASTN searches against the A . pisum genome assembly ( Acyr_1 . 0 ) , revealing 21 reads with regions similar ( BLASTN bit score ≥55 ) to the pea aphid genome ., However , none of these reads exhibited features of LGT; that is , they did not exhibit distinct regions with similarity to the Buchnera and aphid genomes respectively ., Thus , we concluded that the precluded reads contain no evidence for laterally transferred genes ., We next focused on screening of the A . pisum genome assembly ( Acyr_1 . 0 ) , using three independent strategies designed to detect LGT from any bacterial lineage ., Two strategies were based on BLASTP/deduced amino acid sequences ( Figure 1 ) and on BLASTX/six-frame translations ( Text S1 , Table S2 , Figure S2 ) , respectively ., These were designed to detect potential transferred genes that might be at different stages of degradation or divergence following transfer to the host genome ., We also conducted BLASTN searches designed to detect non-protein-coding sequences transferred from aphid symbionts ., First , all potential polypeptides ( PPPs ) not less than 60 amino acids were deduced from the genome assembly of A . pisum Acyr_1 . 0; 22 , 798 scaffolds ( N50\u200a=\u200a86 . 9 kb; Total size: 464 . 3 Mb ) , 6 . 2× coverage of the 525 Mb A . pisum genome ( IAGC , paper under review ) as described in the Materials and Methods ., A total of 1 , 105 , 168 PPPs corresponding to 92 , 293 , 525 amino acid residues were obtained ( Figure 1 , box 1 ) ., Using all 1 , 105 , 168 PPPs as queries , BLASTP searches were performed against the bacterial protein database ( see Materials and Methods , Table S1 ) ., These searches revealed 7 , 093 PPPs that were significantly similar to bacterial proteins ( BLASTP score ≥40 ) ( Figure 1 , box 2 ) ., Subsequently , these 7 , 093 PPPs were subjected to BLASTP searches against the RefSeq invertebrate protein database ., Comparisons of BLAST hit scores revealed that 818 out of 7 , 093 PPPs were significantly more similar to bacterial orthologs than to invertebrate orthologs ( Figure 1 , box 3 ) ., To further verify their similarity to bacterial proteins , BLASTP searches were performed against the nr protein database at the NCBI website using the 818 PPPs as queries ., For 742 PPPs , top BLAST hits were bacterial proteins ( Figure 1 , box 4 ) ., These 742 PPPs were located in 406 scaffolds , most of which were relatively short ( <10 kb , whereas N50 of all the A . pisum scaffolds is 86 . 9 kb ) and/or contained many unidentified nucleotides ( Ns ) ., Among them , 331 scaffolds contained only DNA sequences that were nearly identical to bacterial genomic sequences in the non-redundant nucleotide database at NCBI ., These 331 scaffolds ( Table S3 ) were assumed to represent bacterial contaminants , and 662 of 742 LGT-candidate PPPs located in these 331 scaffolds were thus eliminated as potential LGT candidates ( Figure 1 , box 5 ) ., Most of the contaminants showed closest matches to related species of Enterobacteriaceae ( Gammaproteobacteria ) such as members of the genera Pantoea , Serratia , or Enterobacter ( Table S3 ) , which are known to infect aphids and other insects as pathogens 31–33 ., Furthermore , some of these contigs showed near perfect identity to sequences within several BACs sequenced in the A . pisum genome project but of clear bacterial origin ( AC202220 , AC203059 , AC203074 ) ., As part of the A . pisum genome project , 39 of the scaffolds that appeared to derive from bacterial contaminants were screened with PCR in new DNA samples from antibiotic-treated A . pisum LSR1 ( the sequencing strain ) , and all were verified to be absent and thus contaminants in the original sample ( IAGC , in review ) ., In addition , two PPPs were located in two distinct scaffolds SCAFFOLD5147 ( EQ115919 ) and SCAFFOLD7004 ( EQ117776 ) that appeared to be artifactual chimeric fusions of DNA derived from the genomes of A . pisum and bacterial contaminants ., In these cases , regions similar to bacterial genes were short ( 367 nt in the 12 , 278-nt SCAFFOLD5147 and 373 nt in the 229 , 440-nt SCAFFOLD7004 ) , almost identical to known bacterial genes ( the region in the SCAFFOLD5147 was 87% and 93% identical at the nucleotide and amino acid levels , respectively , to the fadE gene ( YP_001269130 ) of Pseudomonas putida F1 ( Gammaproteobacteria ) ( CP000712 ) ; the region in the SCAFFOLD7004 was 91% and 97% identical at the nucleotide and amino acid levels , respectively , to the glnD gene ( YP_046710 ) of Acinetobacter baumannii str ., SDF ( Gammaproteobacteria ) ( CU468230 ) , and covered only by a single sequence read each ( based on visual inspections of the NCBI trace archive ) ., As these scaffolds seemed highly likely to be artifacts due to cloning and/or assembly errors , we also discarded these two PPPs ( Figure 1 , box 5 ) ., Twenty of the 80 remaining LGT-candidate PPPs showed only weak similarity to bacterial proteins in the NCBI nr protein database ( bit score ≤45 and E-value ≥0 . 001 ) ., Manual inspection of the BLAST hit sequences revealed that each of the aligned regions was short and that hits were derived from various genes that are not related to one another , indicating that the results were not reliable ., Thus , these PPPs were also discarded ( Figure 1 , box 6 ) ., In addition , three PPPs showed moderate similarity to bacterial sequences ( bit score >50 , E-value <0 . 0001 ) , but the aligned regions of both the queries and hit sequences consisted of tandem repeat sequences ., As lengths of the repeat units of the queries and hit sequences were different and the similarity appeared to be detected only by chance , these PPPs were also removed from the LGT candidates ( Figure 1 , box 6 ) ., Fifty-four of the 57 remaining LGT-candidate PPPs were parts of the A . pisum proteins predicted by the NCBI and IAGC ., Using full-length amino acid sequences of 54 corresponding proteins as query sequences , BLASTP searches were performed against nr protein database at NCBI ., Forty-nine of the 54 proteins were more similar to animal proteins than to bacterial proteins , and were orthologs of proteins widely distributed both in prokaryotes and eukaryotes ( Figure 1 , box 7 ) ., Only a fraction of each PPP showed slightly higher similarity ( BLAST bit score difference <13 ) to bacterial proteins than to animal proteins ., Thus , none of these 49 proteins appeared more similar to bacterial proteins than to animal proteins , and so were removed from the LGT-candidates ( Figure 1 , box 7 ) ., Finally , eight genes corresponding to the eight remaining PPPs were judged as promising LGT candidates ., These eight contained two copies of LD-carboxypeptidase ( LdcA ) , three copies of rare lipoprotein A ( RlpA ) , and one copy each of N-acetylmuramoyl-L-alanine amidase ( AmiD ) , 1 , 4-beta-N-acetylmuramidase ( bLys ) , and DNA polymerase III alpha chain ( ψDnaE ) ., To check the presence/absence of more paralogs for these genes , TBLASTN searches were performed against the A . pisum genome assembly using deduced amino acid sequences of the eight candidates as queries ( Figure 1 , box 8 ) ., This detected one more LdcA and two more RlpAs ., We also performed a screen based on six-frame translations of the A . pisum genome ( BLASTX ) , which is potentially more sensitive in detecting shorter and degenerate sequences , as the method is not limited by the threshold of the PPP length ( ≥60 aa ) and will produce protein alignments across stop codons ( Text S1 , Table S2 , Figure S2 ) ., This method identified 10 of the 11 LTG candidates found in the search based on PPPs , verifying the effectiveness of the two strategies ., The BLASTX-based approach identified a single additional candidate , ATP synthase delta chain ( ψAtpH ) ., We also performed BLASTN searches using the genomes of Buchnera str ., APS ( NC_002252 , NC_002253 , NC_002528 ) 9 and Hamiltonella defensa ( NC_012751 , NC_012752 ) ( Gammaproteobacteria; a facultative symbiont of aphids ) 34 as queries , as such searches could reveal transfers of non-protein-coding fragments that would not have been evident in the PPP-based or the BLASTX-based searches described above ., However , no additional LGT candidates were obtained in these searches ., Thus , in total , computational screens identified 12 promising LGT candidates ( LdcA1 , LdcA2 , ψ LdcA , AmiD , bLys , RlpA1 , RlpA2 , RlpA3 , RlpA4 , RlpA 5 , ψDnaE , and ψAtpH ) in the A . pisum genome ( Table 1 ) ., One each of LdcAs ( now renamed LdcA1 , ACYPI009109 ) and RlpAs ( renamed RlpA4 , ACYPI004737 ) were originally detected in our previous transcriptome analysis of the A . pisum bacteriocyte 29 , and were further verified to have been transferred from bacteria to the aphid genome via LGT 30 ., Extant Buchnera lacks these genes other than dnaE and atpH 9 , whereas many other bacteria , including E . coli , a close relative of Buchnera , possess all of them 35 ., To further verify the presence of these genes in the A . pisum genome , we conducted experimental analyses using quantitative PCR ., Bacterial symbionts , contaminants and pathogens present within the host are not expected to be at constant copy number relative to host genome copies , when multiple tissues or hosts are examined ., For example , Buchnera and facultative symbionts show large fluctuations in genome and cell copy number relative to single copy A . pisum genes , depending on the tissue sampled and on the age and condition of the individual aphid ( e . g . , 36–38 ) ., Pathogens are expected to vary even more in abundance , and typically are entirely absent from aphids , based on PCR assays 31 ., In contrast , sequences that are part of the host genome will display nearly the same copy number as single copy genes from the genome , both reflecting the number of host genomic copies within a sample ., To distinguish between the hypotheses that LGT candidates derive from the aphid genome rather than from contaminants , we examined copy number of these genes relative to a known single copy gene in the aphid genome , using real time quantitative PCR ( Figure 2 ) ., Two A . pisum strains were used for the analysis; one was the strain LSR1 ( n\u200a=\u200a3 ) , the North American strain that was used for the genome sequencing , and the other was the strain ISO ( n\u200a=\u200a4 ) , the Japanese strain that was used for our previous transcriptome analysis of the bacteriocyte 29 , 30 ., A ribosomal protein gene , RpL7 , which is believed to be present as a single copy per haploid A . pisum genome , was used as a standard ., ( This gene is present in only one copy in the A . pisum genome project and is only known as a single copy gene in other genomes . ), Of the three LdcAs , only LdcA1 was analyzed ., The target/standard ratios ( mean ± SE ) for LdcA1 , AmiD , bLys , RlpA1 , RlpA2 , RlpA3 , RlpA4 , RlpA5 , ψDnaE , and ψAtpH were 1 . 27±0 . 12 , 0 . 98±0 . 13 , 1 . 18±0 . 13 , 0 . 91±0 . 07 , 0 . 82±0 . 06 , 0 . 89±0 . 06 , 1 . 16±0 . 13 , 0 . 98±0 . 07 , 1 . 05±0 . 09 , and 1 . 04±0 . 12 , respectively ( Figure 2 ) ., That these ratios were nearly constant across samples and centered around 1 ( p>0 . 05 , one-way ANOVA followed by Tukey-Kramer test ) strongly suggests that they are encoded in the A . pisum genome as single-copy genes ., Moreover , the ratios for the nine genes showed no significant difference between the two A . pisum strains ( p>0 . 05 , Students t-test ) , indicating that both strains encode these genes in their genomes ., These results are a strong indicator that the candidate genes do not derive from contaminant bacteria , as the titer of such contaminants would dramatically differ among aphid individuals , which should result in ratio variation among samples ., To further characterize these genes , we performed detailed structural and molecular phylogenetic analyses ., The candidate in SCAFFOLD15447 ( EQ126219 ) was similar to bacterial genes encoding DNA polymerase III alpha subunit ( DnaE ) ( Table 1 ) ., The top BLASTX hit was DNA polymerase III alpha subunit Buchnera aphidicola str . APS ( NP_240067 . 1 ) ( E\u200a=\u200a4×10−19 ) , and essentially all the subordinate hits were DNA polymerase III alpha subunit proteins of various lineages of bacteria ., The amino acid sequence of the aphid DnaE was 66% and 38% identical to DnaE orthologs of Buchnera str ., APS and E . coli K12 , respectively ( Figure S3 ) ., Phylogenetic analyses clearly showed that the A . pisum ψDnaE forms a monophyletic clade with DnaE of Buchnera str ., APS ( 99% in Bayesian inference ( BI ) , 97% in maximum likelihood ( ML ) , 100% in neighbor-joining ( NJ ) ) , which is sister to that of Buchnera str ., Schizaphis graminum ( the strain derived from another aphid species , S . graminum ) ( 100/99/100 ) ( Figure 3 ) ., This indicates that A . pisum relatively recently acquired ψDnaE from Buchnera , after its divergence from the lineage leading to S . graminum ( 50–70 million years ago ) 10 , 17 ., However , the predicted aphid DnaE was 120 aa in length , whereas the DnaE of Buchnera str ., APS is 1 , 161 aa , the approximate length of this gene in bacteria generally ., No other DNA sequence corresponding to the missing part of DnaE was found in the A . pisum genome assembly ., These observations imply that the A . pisum ψDnaE is a pseudogene ., We further confirmed this possibility using a relative rate test showing that the A . pisum copy evolves at an accelerated rate , as expected for a pseudogene ( Text S2 ) ., The candidate in the SCAFFOLD4584 ( EQ115356 ) was similar to bacterial genes encoding ATP synthase delta subunit ( AtpH ) ( Table 1 ) ., The top BLASTX hit was ATP synthase delta subunit Buchnera str . APS ( NP_239847 . 1 ) ( E\u200a=\u200a1×10−13 ) , and essentially all the subordinate hits were ATP synthase delta subunit proteins of various lineages of bacteria ., The amino acid sequence of aphid AtpH was 58% and 35% identical to AtpH orthologs of Buchnera str ., APS and E . coli K12 , respectively ( Figure S4 ) ., However , the predicted aphid AtpH was 100 aa in length , and has three intermittent stop codons , whereas the AtpH of Buchnera str ., APS is 177 aa , the approximate length of this gene in bacteria generally ., No other DNA sequence corresponding to the missing part of AtpH was found in the A . pisum genome assembly ., These observations imply that the A . pisum ψAtpH is also a pseudogene ., Phylogenetic analyses gave results for the A . pisum ψAtpH that were similar to those for ψDnaE ., The copy in the A . pisum genome forms a clade with AtpH of Buchnera str ., APS ( 96% in BI , 65% in ML , 83% in NJ ) , which is sister to that of Buchnera str ., S . graminum ( 100% in BI , ML , and NJ ) ( Figure 4 ) ., Thus , A . pisum relatively recently acquired both ψAtpH and ψDnaE from Buchnera , after divergence from a common ancestor of A . pisum and S . graminum ., Three ACYPI009109 , SCAFFOLD11510 ( EQ122282 ) nucleotide number: 81202 . 80565 , and SCAFFOLD1029 ( EQ111801 ) nucleotide number: 7224 . 10729 ( Table 1 ) of the 12 candidates were similar to bacterial ldcA genes , which encodes LD-carboxypeptidases that are required for recycling murein ( peptidoglycan ) , a component of the bacterial cell wall 39 ., As demonstrated previously 30 , one of the A . pisum LdcA genes LdcA1; ACYPI009109 in the SCAFFOLD6884 ( EQ117656 ) has a functional protein-coding sequence ., On the other hand , another gene ( ψLdcA in the SCAFFOLD11510 ) newly found in this study ( Table 1 ) had 11 frame-shift mutations in its potential coding sequence ( Figure 5 ) , suggesting that this copy of LdcA is a pseudogene ., Molecular phylogenetic analyses demonstrated that the A . pisum LdcA1 and ψLdcA form a monophyletic clade ( 100% support in BI , ML , and NJ ) that is sister to the clade of ldcAs of rickettsial bacteria , including Wolbachia ( Alphaproteobacteria , Rickettsiales ) ( NP_966741 ) and Orientia tsutsugamushi ( Alphaproteobacteria , Rickettsiales ) ( YP_001248242 ) ( 100% in BI; 99% in ML; 97% in NJ ) ( Figure 6 ) ., This branching pattern can be most simply explained by the hypothesis that an ldcA copy was transferred from Wolbachia or some other rickettsial bacterium to the aphid genome , followed by duplication , and subsequent inactivation of one copy ., Symbionts from Rickettsiales are observed in some aphids 38 , 40 , 41 , suggesting this bacterial clade as the source of this gene ., However , the phylogeny is consistent with horizontal transfer among bacterial groups ( Figure 6 ) , and the A . pisum fragment potentially derives from another source such as a group of bacteria not yet sequenced ., Mitochondria are also derived from the Alphaproteobacteria , but they can be ruled out as likely sources of this gene , since all animal mitochondria are extremely reduced in gene content and lack homologs of ldcA ., The remaining LdcA gene ( LdcA2 in the SCAFFOLD1029 ) found in this study ( Table 1 ) contained a large sequence gap , and only 108 nucleotides of its potential protein-coding sequence had been determined ., This 108 bp region of LdcA2 was 100% identical to the corresponding region of LdcA1 ( Figure 5 ) ., Moreover , the BLASTN analysis using bl2seq revealed that an approximately 10-kb region containing LdcA2 ( total length unknown ) in the SCAFFOLD1029 ( 45066 bp ) is 97% identical to a region containing LdcA1 ( 1364 bp ) in the SCAFFOLD6884 ( 19038 bp ) ., Regarding the SCAFFOLD11510 containing ψLdcA , significant similarities to SCAFFOLD6884 and SCAFFOLD1029 were detected only in the ψLdcA region ., This may suggest that LdcA2 also arose from a duplication event , and that its evolutionary history is relatively short in comparison to that of ψLdcA ., However , we cannot exclude the possibility that LdcA1 and LdcA2 are alleles of a single gene , as the sequenced aphid genomic sample was heterozygous for some genomic regions ( IAGC , paper under review ) ., Another LGT candidate , ACYPI006531 in the SCAFFOLD15270 ( EQ126042 ) , was similar to bacterial genes encoding N-acetylmuramoyl-L-alanine amidase ( AmiD ) ( Table 1 , Figure 7 ) ., This enzyme is also required for recycling murein ( peptidoglycan ) , a component of the bacterial cell wall 42 ., The top BLASTP hit for the predicted gene ( XP_001945574 . 1 ) of ACYPI006531 was a putative N-acetylmuramoyl-L-alanine amidase O . tsutsugamushi ( Alphaproteobacteria , Rickettsiales ) ( YP_001248113 ) ( E\u200a=\u200a1×10−55 ) ., Subordinate hits were either orthologs of AmiD or AmpD , two types N-acetylmuramoyl-L-alanine amidases that are characterized in E . coli 42 ., The A . pisum gene ACYPI006531 was named AmiD , as it showed higher similarity to orthologs of AmiD than to AmpD ., Moreover , as is the case for other AmiD orthologs , the A . pisum AmiD has an extra C-terminal tail ( ∼100 amino acids ) that is absent from AmpD orthologs ., This structural feature typifies AmiD , although the function of the tail is not known 42 ., The amino acid sequence of A . pisum AmiD was 47% and 41% identical to AmiD proteins of O . tsutsugamushi and E . coli , respectively ( Figure 7A ) ., All three amino acids in the zinc-binding triad of AmiD ( His-34 , His-154 , and Asp-164 ) , which are essential for its catalytic activity 42–44 , were conserved in the A . pisum ortholog ., Figure 7B shows the structure of the aphid AmiD gene ., The gene appeared to consist of 2 exons and a long single intron , although the intron contained two gaps ., Phylogenetic analyses showed that the A . pisum AmiD is closely related to orthologs from Proteobacteria ( Figure 8 ) ., Moreover , there was robust support ( 100% in BI; 90% in ML; 90% in NJ ) for A . pisum AmiD forming a monophyletic clade with orthologs from intracellular symbiotic bacteria such as O . tsutsugamushi ( Alphaproteobacteria ) ( YP_001248113 ) and Amoebophilus asiaticus ( Bacteroidetes ) ( YP_001957902 ) ., O . tsutsugamushi is an intracellular bacterium that infects arthropods and mammals 45 , whereas A . asiaticus is an intracellular symbiont of a unicellular eukaryote , Acanthamoeba 46 ., This branching pattern can be most simply explained by the hypothesis that the aphid acquired amiD via LGT from a rickettsial bacterium ., It is possible that A . asiaticus acquired amiD via LGT from a bacterium belonging to Proteobacteria , as the A . asiaticus amiD is distantly related to orthologs from other sequenced species of Bacteroidetes , and LGT is common among prokaryotes generally 47 ., A putative ortholog of AmiD/AmpD was also detected in another metazoan species , the placozoan Trichoplax adhaerens ., However , the phylogenetic tree showed that the T . adhaerens ortholog is distantly related to the aphid AmiD ( Figure 8 ) , implying that the ancestors of A . pisum and T . adhaerens independently acquired the genes from different lineages of bacteria ., ACYPI004424 in the SCAFFOLD2508 ( EQ113280 ) appeared to encode a chimeric protein that consists of eukaryotic carboxypeptidase and a bacterial lysozyme ( 1 , 4-beta-N-acetylmuramidase ) ( Table 1 , Figure 9 ) ., A conserved domain search at the NCBI website revealed that the carboxypeptidase ( pfam00246 , E\u200a=\u200a3×10−45 ) and 1 , 4-beta-N-acetylmuramidase ( pfam01183 , E\u200a=\u200a2×10−22 ) are encoded in its N-terminal region and C-terminal region , respectively ., RT-PCR cloning of the transcript verified that the gene is transcribed and is truly chimeric ( AB509281 ) ., The top hit of BLASTP for the C-terminal domain of the predicted gene model was 1 , 4-beta-N-acetylmuramidase Wolbachia endosymbiont of Drosophila simulans ( Alphaproteobacteria , Rickettsiales ) ( YP_002727734 ) ( E\u200a=\u200a3×10−54 ) ., The subordinate hits were lysozymes of various lineages of bacteria ., As these bacterial lysozyme genes lack common gene symbols , ACYPI004424 was tentatively named bLys ( bacterial Lysozyme ) ., Lysozymes represent a family of enzymes that degrade bacterial cell walls by hydrolyzing the 1 , 4-beta-linkages between N-acetyl-D-glucosamine and N-acetylmuramic acid in murein heteropolymers 48 ., They are ubiquitously distributed among living organisms and are believed to be essential for defense against bacterial infection ., Lysozymes are classified into several types ( i . e . , chicken , goose , invertebrate , plant , bacteria and phage types ) , and the A . pisum bLys was clearly categorized as a bacterial type ( see below ) ., Interestingly , unlike all other fully sequenced Metazoa , A . pisum appears to lack genes encoding canonical lysozymes 49 ., If the bLys retains the bacteriolytic activity , this bacterium-derived lysozyme might compensate for the lack of canonical lysozymes in A . pisum ., Figure 9B shows the chimeric structure of the aphid bLys gene ., Bacterial lysozyme was encoded in the last ( 6th ) exon , whereas eukaryotic carboxypeptidase was encoded in the 1st-3rd exons ., The 1st exon also encoded a eukaryotic signal peptide , suggesting that the product is a secretory protein , as are other lysozymes ., The amino acid sequence of the lysozyme domain of the A . pisum bLys was subjected to molecular phylogenetic analysis ( Figure 10 ) ., The tree demonstrated that the aphid gene forms a clade with orthologs from Alphaproteobacteria ( 99% in BI; 82% in ML; 82% in NJ ) , and is especially closely related to a gene of Wolbachia pipientis wRi ( YP_002727734 ) ( 93% in BI , 63% in ML , 74% in NJ ) ., This is consistent with the hypothesis that the A . pisum bLys was transferred from a Wolbachia-like rickettsial bacterium to an ancestral aphid genome ., Five candidates ( AUG4_SCAFFOLD5510 . g2 . t1 , ACYPI008496 , ACYPI38879 , ACYPI004737 , and ACYPI005979 ) appeared to encode bacterial rare lipoprotein A ( RlpA ) ( Table 1 ) ., In contrast to the case of LdcAs , all of the five RlpA genes were clustered in a single scaffold , SCAFFOLD5509 ( EQ116281 ) ( Figure 11A ) ., They were numbered consecutively following their order in the scaffold , and the RlpA gene that we reported previously ( corresponding to ACYPI004737 ) 29 , 30 was renamed RlpA4 ., The N-terminus of the computationally predicted gene model of ACYPI38879 was slightly different from what we reported previously ( AB435384 , AB435385 ) 30 ., As our original predictions were based on full-length cDNA sequences and are highly reliable , we used these gene boundaries in the subsequent analyses ., A double-ψ β-barrel ( DPBB ) domain that is conserved in bacterial RlpAs was conserved in all of the A . pisum RlpAs ( encoded in the 3rd ex

Introduction, Results, Discussion, Materials and Methods

Genome reduction is typical of obligate symbionts ., In cellular organelles , this reduction partly reflects transfer of ancestral bacterial genes to the host genome , but little is known about gene transfer in other obligate symbioses ., Aphids harbor anciently acquired obligate mutualists , Buchnera aphidicola ( Gammaproteobacteria ) , which have highly reduced genomes ( 420–650 kb ) , raising the possibility of gene transfer from ancestral Buchnera to the aphid genome ., In addition , aphids often harbor other bacteria that also are potential sources of transferred genes ., Previous limited sampling of genes expressed in bacteriocytes , the specialized cells that harbor Buchnera , revealed that aphids acquired at least two genes from bacteria ., The newly sequenced genome of the pea aphid , Acyrthosiphon pisum , presents the first opportunity for a complete inventory of genes transferred from bacteria to the host genome in the context of an ancient obligate symbiosis ., Computational screening of the entire A . pisum genome , followed by phylogenetic and experimental analyses , provided strong support for the transfer of 12 genes or gene fragments from bacteria to the aphid genome: three LD–carboxypeptidases ( LdcA1 , LdcA2 , ψLdcA ) , five rare lipoprotein As ( RlpA1-5 ) , N-acetylmuramoyl-L-alanine amidase ( AmiD ) , 1 , 4-beta-N-acetylmuramidase ( bLys ) , DNA polymerase III alpha chain ( ψDnaE ) , and ATP synthase delta chain ( ψAtpH ) ., Buchnera was the apparent source of two highly truncated pseudogenes ( ψDnaE and ψAtpH ) ., Most other transferred genes were closely related to genes from relatives of Wolbachia ( Alphaproteobacteria ) ., At least eight of the transferred genes ( LdcA1 , AmiD , RlpA1-5 , bLys ) appear to be functional , and expression of seven ( LdcA1 , AmiD , RlpA1-5 ) are highly upregulated in bacteriocytes ., The LdcAs and RlpAs appear to have been duplicated after transfer ., Our results excluded the hypothesis that genome reduction in Buchnera has been accompanied by gene transfer to the host nuclear genome , but suggest that aphids utilize a set of duplicated genes acquired from other bacteria in the context of the Buchnera–aphid mutualism .

Bacterial lineages have repeatedly evolved intimate symbioses with eukaryotic hosts , the most famous cases being those of the cell organelles , mitochondria , and plastids ., Symbiont genomes typically lose many ancestral genes , raising the question of how they function with so few genes ., In organelles , part of the answer involves gene transfer to the host genome , allowing maintenance of essential functions ., So far , the extent of gene transfer to hosts has not been assessed for other cases of intimate , obligate symbiosis ., Aphids harbor an ancient coevolved intracellular symbiont , called Buchnera ., We used the newly available sequence of the pea aphid genome to conduct an exhaustive computational search for genes of bacterial ancestry ., We found that no functional genes have been transferred from Buchnera , ruling out such transfer as a driving force in genome reduction in this symbiont ., However , the aphid genome does contain eight transcribed genes of apparent bacterial origin , some of which have been duplicated after transfer ., Based on their expression patterns , most of these appear to function specifically in the aphid-Buchnera symbiosis , presenting the possibility that the maintenance of obligate intracellular symbioses can be affected by the acquisition and duplication of genes transferred from unrelated bacterial lineages .

genetics and genomics/genomics, evolutionary biology/microbial evolution and genomics, genetics and genomics/gene discovery, genetics and genomics/comparative genomics, evolutionary biology/evolutionary and comparative genetics, genetics and genomics/functional genomics, genetics and genomics/gene expression, evolutionary biology/genomics, genetics and genomics/genome projects, evolutionary biology, genetics and genomics, genetics and genomics/bioinformatics

journal.pgen.1000909

Whole-Genome SNP Association in the Horse: Identification of a Deletion in Myosin Va Responsible for Lavender Foal Syndrome

Heritable disorders affect many domestic species , including the horse ., In the Arabian breed of horse a neurological disorder has been reported that is lethal soon after birth 1 ., Affected foals can display an array of neurological signs including tetanic-like seizures , opisthotonus , stiff or paddling leg movements and nystagmus ( Figure 1 ) 2 ., Mild leucopenia is sometimes observed 2 , 3 ., These neurologic impairments prevent the foal from standing and nursing normally and , if not lethal on their own , are often cause for euthanasia ., In addition to these abnormalities , affected foals possess a characteristic diluted “lavender” coat color ., This resulting coat color , variously described as pale gray , pewter , and light chestnut , as well as lavender , has coined the name “Lavender Foal Syndrome” ( LFS ) 2 ., Also called “Coat Color Dilution Lethal” 2 , there is currently no treatment for LFS available ., Additionally , initial diagnosis can be difficult as the clinical signs of LFS can easily be confused with a number of neonatal conditions including neonatal maladjustment syndrome and encephalitis 2 ., The inheritance of Lavender Foal Syndrome is suspected to be recessive , although extensive pedigree analysis has not , to date , been published ., Outwardly healthy horses can sire lethally affected foals; therefore a recessive mode of inheritance for LFS is most likely ., Historically developed by the Bedouin tribesman on the Arabian Peninsula , the Arabian horse is one of the oldest recognized breeds of horse ., Valued for its beauty and athleticism , the Arabian has contributed to the development of many light horse breeds , most notably the Thoroughbred , a breed used extensively in horse racing across the world 4 ., The majority of documented cases of Lavender Foal Syndrome have been reported in the Egyptian Arabian , a sub-group of the Arabian breed found originally in Egypt but extensively exported and popular in the United States ., Egyptian Arabians have their own registry , although they are also part of the main Arabian studbook ., It is estimated that there are 49 , 000 living registered Egyptian Arabians worldwide ( personal communication , Beth Minnich , Pyramid Society ) ., Identifying the genetic basis of this condition and developing a diagnostic test for the LFS allele will enable breeders to make more informed selection of mating pairs , thus avoiding the production of affected foals and potentially lowering the frequency of this allele in the population , without wholesale culling of valuable stock ., Over the past 15 years the Horse Genome Project has produced several generations of analytical and diagnostic resources ( genetic tools ) that permit interrogation of polymorphisms across the entire equine genome 5 , 6 ., Previous mapping efforts using ∼300 microsatellite markers yielded results for several heritable diseases ( for examples see 7 , 8 ) ., However , this small number of markers limited genetic studies in the horse to simple traits in closely related families with fairly large numbers of samples ., The recently completed 6 . 8x whole genome sequence of the horse and the associated identification of approximately 1 . 5 million Single Nucleotide Polymorphisms ( SNPs ) located throughout the horse genomic sequence 9 has enabled the construction of a 56 , 402 element SNP chip for rapid whole genome scanning ( Equine SNP50 , Illumina , San Diego , CA ) ., SNP-based whole genome association studies have proven exceptionally successful when studying simple mendelian traits in domesticated species ., Two notable examples can be found in studies of coat traits in the dog 10 and recessive diseases of cattle 11 ., Previously described mutations in mice and humans provide several comparative phenotypes similar to Lavender Foal Syndrome ., Two genes in particular , Ras-associated protein RAB27a ( RAB27A ) and myosin Va ( MYO5A ) , yield phenotypes with striking parallels to LFS ., These two proteins , along with melanophilin ( MLPH ) are part of a transportation complex responsible for the trafficking of melanosomes to the periphery of the cell where they are transferred to the keratinocyte ( reviewed in 12 ) ., The myosin Va transport complex is also utilized in the dendrite of the neuron where it has been shown to move various cargo , including mRNAs , glutamate receptors , and secretory granules 13 , 14 ., Disruption of these diverse functions could explain the constellation of defects observed in RAB27A and MYO5A mutants ., In mice , 71 mutations in MYO5A and 106 in RAB27A have been recorded in the MGD database 15 ., In humans , several unique recessive mutations in these two genes have been shown to cause similar disorders ., The severity of the phenotype , known as Griscelli syndrome , varies with the gene and location of the mutation 16 ., Griscelli syndromes have been divided in to three categories based on the gene responsible; MYO5A in type 1 , RAB27A in type 2 , and MLPH in type 3 17 ., There are subtle differences in the phenotype of each of these subtypes ., For example , RAB27A mutations in both human and mouse disrupt granule exocytosis in T lymphocytes ., This leads to immunodeficiency and leukocyte infiltration in to vital organs , including the brain ., Thus , although neurological defects are often present in RAB27A mutants they are usually secondary to this infiltration 17 ., In contrast , MYO5A mutants exhibit a primary neurologic dysfunction and have normal immune function ., Based on this distinction MYO5A was chosen as the primary candidate gene for Lavender Foal Syndrome ., Pedigree data from the six affected foals available at the time of genotyping supported a recessive mode of inheritance ., A single common ancestor was identified six to eight generations from these six affected foals ( Figure S1 ) ., This common ancestor is present on both sides of the pedigree in each foal ., This stallion may represent a founder among this group and this convergence in the pedigree supports identity by descent for the LFS mutation ., Average inbreeding ( Fi ) was 0 . 0861 for affected foals , versus 0 . 0394 for parents of foals ., The extended pedigree also allowed for the calculation of the coancestry coefficient between each living relative and the nearest affected foal in the pedigree ., Based on this calculation we predicted that the frequency of the LFS allele would be 0 . 42 among the 30 relatives used for genotyping ., Genotypic association tests using the six affected foals and their 30 healthy relatives revealed a single region on chromosome 1 ( ECA1 ) with statistical significance above that of the rest of the genome ( Figure 2 ) ., These 14 highly significant SNPs encompassed a region spanning 10 . 5 Mb ( ECA1:129228091 to 139718117 ) ., Although extensive inbreeding and relatedness between affected individuals produced a high number of coincidentally significant ( p<0 . 05 ) SNPs across the genome , the high peak significance of SNPs in the candidate region ( p\u200a=\u200a4 . 62e-6 ) was convincing evidence for association ., In total there were 14 SNPs at this locus that were more significantly associated with the LFS trait than any other region in the genome ., The twelve LFS bearing chromosomes from the six affected horses represented only four unique haplotypes for this 10 Mb candidate region ., These four haplotypes possessed one large block of 27 SNPs in common ., This 1 . 6 Mb region was homozygous in all six affected horses and heterozygous in obligate carriers as well as many of the living relatives , as was predicted by the coancestry in the pedigree ., The linkage disequilibrium ( LD ) structure and p-values in this likely location for a recessive mutation are plotted in Figure 3 ., Only 10 Ensembl Gene Predictions fell within this region , including MYO5A , but not RAB27A ( UCSC Genome Browser 9 ) ., Genome-wide observed homozygosity from the genotypes obtained using the EquineSNP50 chip was on average 65 . 14% ., This was much higher than expected considering the homozygosity of the inbred mare chosen for whole genome sequencing was estimated at only 46% 9 ., The ten founder Egyptian Arabian individuals from this study , as well as an additional 10 unrelated individuals from the Thoroughbred , Arabian ( non-Egyptian ) and Saddlebred breeds were used to calculate average genome-wide LD ( Figure S2 ) ., This calculation revealed that the length of LD in the Egyptian was similar to that of the Thoroughbred , a breed with a long history of a closed studbook and relatively small foundation population ., LD in the Egyptian was also much longer than that of the Arabian population as a whole , which was most similar to the Saddlebred ., The Saddlebred breed registry was closed in 1917 and derived from fairly diverse types of horse suitable for use as transportation under saddle and in harness ., Individual PCR amplification and sequencing of the 39 exons of MYO5A from a LFS affected foal revealed three SNPs and one polymorphic microsatellite in intronic sequence , as well as a single base deletion in exon 30 of MYO5A ( Table 1 ) ., This deletion was further confirmed by sequencing in a second foal and its heterozygous parents ( Figure 4 ) ., The deletion is termed ECA1 g . 138235715del per Human Genome Variation Society ( http://www . hgvs . org/mutnomen/ ) nomenclature ., This deletion changes the reading frame , creating a premature stop codon in the translation of exon 30 , 12 amino acids following the mutation ., A multiple alignment of the predicted LFS exon 30 amino acid sequence , as well as the wild type sequence from eight species , shows that this region of the myosin Va protein is highly conserved ( Figure S3 ) ., The four intronic polymorphisms were not predicted to change the function of myosin Va and were therefore not investigated further ., We designed a PCR-RFLP assay using the Fau I restriction enzyme to detect this deletion ( Figure S4 ) ., Digestion of the PCR product produces a positive control fragment of 289 bp in all genotypes ., Presence of the deletion abolishes a Fau I site , changing the normal pattern of a 386 bp and a 90 bp fragment in to a single 476 bp product ., All seven affected foals ( the six originally submitted for mapping plus one additional obtained after mapping was completed ) were homozygous for the deletion ( Table 2 ) ., Eight out of the 14 parents of these affected foals were available for sampling and all carried the deletion ., Among 23 relatives of affected foals 16 were identified as carriers of the deletion ., A sample group of 114 Arabian horses was tested to provide a rough estimate of the frequency of the MYO5A exon 30 deletion , and therefore Lavender Foal Syndrome , in the breed as a whole ( Table 3 ) ., 10 . 3% of Egyptian Arabians ( six out of 58 horses ) and 1 . 8% of non-Egyptian Arabians ( one out of 56 horses ) were identified as carriers ., Here we describe the first successful use of the EquineSNP50 genotyping platform in identification of the mutation responsible for a genetic disorder in the horse ., We have described a frameshift mutation in the MYO5A gene that leads to Lavender Foal Syndrome in the Egyptian Arabian breed of horse ., This task was made more challenging by the small number ( six ) of DNA samples from available affected foals ., We improved our chances of success by using pedigree data to select control samples from the extended family and by utilizing a genotype association rather than allelic association statistic in combination with identification of regions of homozygosity ., The extreme predicted impact on function resulting from the single base deletion in MYO5A exon 30 makes it a very logical cause of LFS ., Indeed , an alignment of MYO5A exon 30 amino acid sequences from 8 diverse species shows that the exon is completely conserved in horses , humans , mice , dogs and cattle and contains only a few changes in the possum , chicken , and zebrafish ( Figure S3 ) ., As LFS affected foals do not have an immunodeficiency consistent with RAB27A mutations , and the genomic region containing this gene was not inherited as predicted by our recessive model , it is doubtful that this gene plays a role in Lavender Foal Syndrome ., The newly discovered deletion in exon 30 of MYO5A leads to a frame shift and premature termination of transcription ., Loss of the 379 amino acids at the C-terminus of the protein , which encode a portion of the secretory vesicle-specific binding domains of the globular tail , would likely impair binding of myosin Va to those cargo organelles bearing the appropriate receptors 18 ., Although this truncation leaves intact the melanocyte specific alternative exon , exon F , it has been previously shown that binding function is nonetheless destroyed without the cooperative action of downstream motifs 19 ., Additionally , the quantity of MYO5A protein may be significantly reduced , as is often observed in experimentally truncated constructs of this gene 19 ., The resulting loss of vesicle traffic could easily interfere with the normal function of melanocytes and neurons ., The neurologic deficits exhibited by LFS affected foals are relatively more severe than the symptoms reported in human cases of Griscelli Syndrome , which are most often due to changes in a single amino acid rather than loss of a significant portion of the transcript 16 ., However , in the mouse a broad spectrum of phenotypes are observed , owing to the variety of causative mutations available for study ., There is some speculation that a mild , survivable epileptic condition of young foals may represent a non-lethal phenotype of LFS carriers 2 as the two conditions are often seen in the same pedigrees ., However , this association has not been scientifically validated and samples from horses diagnosed with this condition were not available for study at this time ., Based on comparative phenotypes in the mouse this is a plausible scenario ., Several MYO5A alleles in the mouse , most notably mutations of the globular tail region like d-n and d-n2J , exhibit neurological and behavioral defects in juvenile homozygotes 20 ., These deficits improve with age and are often survivable , as has been described in the rumored condition of the horse ., Discovery of the mutation responsible for LFS will enable future studies to evaluate association of this allele with juvenile neurological dysfunction ., Our results suggest the population frequency of carriers of this deletion is 10 . 3% in the Egyptian Arabian ., It is possible that this may be an over estimation of carriers , as owners who suspect they have LFS carrying horses may have been more motivated to participate in the study ., However it is equally as likely that this figure is an underestimation as there is social stigma associated with producing LFS foals , thus motivating breeders to hide the carrier status of their breeding stock ., Despite strict policies regarding the confidential nature of identifying information in research projects , this still influences some breeders to avoid association with Lavender Foal Syndrome research out of fear of being rumored to own carrier horses ., Notably , three of the six carriers identified were reported to be breeding stallions ., Data from the Egyptian Arabian horse registry indicates that approximately 850 young horses are registered each year ( personal communication , Beth Minnich , Pyramid Society ) ., Given our estimate of the number of carriers in the population we expect that around nine LFS foals would be born in the US each year ., This is a small number; however rumors of carrier status can very quickly negatively impact the breeding career of high-priced stallions and lead to large economic losses ., This estimate also assumes mating at random ., In the case of the Egyptian Arabian horse this is not a realistic assumption given the commonplace use of inbreeding and line-breeding in the industry ., The allele frequency for LFS of 5 . 2% is not unlike the frequencies of other heritable diseases in various breeds of horse 21 , 22 ., We identified a conserved block of 1 . 6 Mb in common in the four LFS bearing haplotypes ., This is somewhat smaller than would be expected considering the average rate of decay of LD across just the six to eight generations that separate these four haplotypes ., Indeed , upon further research of the pedigrees from carriers identified during screening for the LFS allele in our sample of 107 Arabian horses , we identified carriers who did not possess this candidate founder in their pedigree ., Therefore it is likely the true founder of this mutation occurred far earlier ., The appearance of a more recent common ancestor is not surprising given the prolific use of popular sires and the prevalence of inbreeding in this population ., Prevention of the economic and emotional losses associated with lethal conditions in foals , included those affected with LFS is a high priority among Arabian breeders ., The market for Egyptian Arabian horses particularly values certain popular bloodlines ., This leads to close breeding as owners seek to increase the percentage of this ancestry in their foal crop ., This breeding strategy thus increases the need for vigilant prevention of recessive genetic disorders ., The test developed here will be a pivotal tool for breeders seeking to breed within lines segregating for LFS , yet minimize or eliminate the production of affected foals ., Widespread application of the EquineSNP50 chip in genetic research is just beginning ., In the case of Lavender Foal Syndrome , the limited availability of samples had impeded the progress of research using existing mapping tools for many years ., Although whole genome association using large numbers of SNP markers is heralded as a tool for complex , polygenic traits , here we have shown that it can be very successfully applied to a simple trait in a small number of individuals ., This work is the first use of the EquineSNP50 genotyping chip to successfully identify a causative mutation ., While whole genome association is often the tool of choice for mapping complex traits and QTLs , we have demonstrated that it can also be a much anticipated solution for simple traits that face additional challenges in phenotyping and/or sample number ., Testing for the LFS allele will be a valuable aid to breeders seeking to avoid losing foals while still using many of the popular lines that may carry Lavender Foal Syndrome ., As the Arabian horse was used to develop many of the modern light horse breeds it is possible that the LFS allele is present in these breeds as well ., In future work we will test additional sub-types of Arabian , as well as a variety of light horse breeds to better assess the population frequency in these groups ., It is possible that LFS segregates in these groups at a low frequency without detection , as it is easy to confuse with other neonatal disorders of the foal ., Procedures in living animals were limited to the collection of blood by jugular venipuncture or hairs pulled from the mane or tail ., Both procedures were conducted according to standard veterinary protocol and inflict minimal , if any pain ., All samples were voluntarily submitted by horse owners and/or attending veterinarians to the Antczak or Brooks laboratories according to protocols approved by the Cornell Institutional Animal Care and Use Committee protocol #1986-0216 ., Six initial samples from affected foals plus one foal obtained mid-way through the study , their 31 relatives , as well as 114 individual horses from the general Arabian horse population were available for study ., The diagnosis of Lavender Foal Syndrome was made by the attending veterinarian and was consistent with the previously published case reports 2 , 3 ., Population samples were voluntarily submitted by horse owners from across the US ., As multiple samples received from a single owner often included closely related individuals , these horses were selected so as no horse included in the study was related to any other within a single generation ., Although the identity and pedigree of study horses were made available , those data are not provided here to protect confidentiality ., Each of the six affected horses had unique parents , although they were often related farther back in the pedigree ( Figure S1 ) ., Samples were coded numerically during use to protect the anonymity of participating farms and owners ., Although Lavender Foal Syndrome is widely-known among breeders of Arabian horses , the number of documented cases available for study and genetic analysis is very low ., The six affected foals and their 30 relatives used for SNP genotyping in this study were collected over a 9 year period from Arabian breeders in various locations in the US ., The average SNP density in the horse has been estimated at 1 per 2 , 000 bp 9 ., It has been proposed that 100 , 000 SNPs should be sufficient for genome wide association mapping in the horse , given the moderate level of linkage disequilibrium both within and across breeds 9 ., Due to the small number of available affected horses and the smaller than optimal horse SNP chip ( only 56K SNPs ) , we decided to employ a modified family study using all of the available affected foals and their closest relatives , plus extensive pedigrees information available from the Arabian Horse DataSource ( Arabian Horse Association , Aurora CO ) ., Genomic DNA was isolated from fresh or frozen tissue or peripheral blood lymphocytes using the DNeasy Blood and Tissue kit ( Qiagen Inc . , Valencia , CA ) following the manufacturers protocol ., DNA was eluted , as well as diluted in , MilliQ ( Millipore Corp . , Billerica , MA ) water before use in downstream applications ., Hair lysates were prepared for PCR from hair bulbs as previously published 23 ., The Lineage v . 1 . 06 pedigree analysis program ( Personal Communication , John Pollack , Animal Breeding Group Cornell University ) was used to construct a pedigree and calculate Fi statistics as well as the coancestry coefficient for the 36 horses submitted for genotyping ( Figure S1 ) ., For Figure S1 the “prune” option was used to hide individuals with fewer than two offspring as well as those with no known ancestors in order to simplify the pedigree for easier viewing ., We selected six affected foals , seven of their parents ( all those from which samples were available ) and 23 close relatives from amongst banked samples held at the Antczak laboratory ., Genotyping on the EquineSNP50 chip was performed by the Genotyping Shared Resource at the Mayo Clinic , ( Rochester , MN ) using 75 µL of approximately 75 ng/µL genomic DNA ., Across the 36 samples the genotyping call rate averaged 98% with a minor allele frequency of 0 . 47 , on average ., Genotypes were filtered to remove SNPs with a MAF <0 . 05 and missingness >0 . 5 using the Plink Whole Genome Analysis Toolset 24 ., A Fishers exact 3×2 test for a significant genotypic association between each SNP and the affected status was performed using the R statistical package v2 . 8 . 1 25 ., Statistical results were visualized and LD plots generated using Haploview 26 or the JMP v7 . 0 software package ( SAS Institute Inc . , Cary , NC ) ., 281 SNPs from the significantly associated region were phased in to haplotypes using the Phase v2 . 1 . 1 27 ., Genome wide LD was estimated using the r2 statistic in the Plink Whole Genome Analysis Toolset under the following filters: minor allele frequency <0 . 05 and deviation from Hardy Weinburg Equilibrium p<0 . 0001 ., Ten individuals previously genotyped on the EquineSNP50 chip were chosen from the Arabian , Thoroughbred and Saddlebred breeds and compared to ten unrelated founder Egyptian Arabians typed for this study ., Values were binned in groups of 5000 and average r2 and inter SNP distance graphed using Excel 2007 ( Microsoft Corp . , New York , NY ) ., As no full length mRNA sequence is currently available for MYO5A in the horse , exons were identified based on homology to the human mRNA sequence ( NM_000259 ) aligned in the UCSC Genome Browser 28 ., This human transcript , comprising 12 , 238 nt of mRNA ( spanning 114 kb of genomic sequence ) encoding 1855 amino acids , is 97 . 9% identical to the homologous equine sequence ., Primers spanning these 39 exons were designed based on the EquCab2 . 0 genomic sequence from the UCSC Genome Browser using the Primer3 software 29 and purchased from Integrated DNA Technologies ( Coralville , IA ) ., These primers and their optimal annealing conditions are listed in Table S1 ., PCR products were submitted to the Cornell Core Life Sciences Laboratories Center for sequencing using standard ABI chemistry on a 3730 DNA Analyzer ( Applied Biosystems Inc . , Foster City , CA ) ., All sequences were submitted to Genbank under the following accession numbers: GU183550 and GU183551 ., Sequences were aligned and screened for mutations using the Contig Express program in the Vector NTI Advance v10 suite ( Invitrogen Corp . , Carlsbad CA ) or the CodonCode Aligner ( CodonCode Corp . , Dedham , MA ) ( Figure S3 ) ., The exon 30 sequence from an LFS horse was translated using Vector NTI Advance v10 and a multiple alignment constructed in Clustal X v . 2 30 using the following amino acid sequences from Genbank: horse XP_001918220 . 1 , human EAW77447 . 1 , mouse CAX15575 . 1 , dog XP_535487 . 2 , cow XP_615219 . 4 , possum XP_001380677 . 1 , chicken CAA77782 . 1 , zebrafish AAI63575 . 1 ., 25 ng of genomic DNA or 2 µL of hair lysate were amplified by PCR using the following primers: Myo5a . Exon30 . RFLP . F 5′-CAG GGC CTT TGA GAA CTT TG-3′ and Myo5a . Exon30 . R 5′-CAG CCA TGA AAG ATG GGT TT-3′ ., Reactions were assembled in a 10 µL total volume using FastStart Taq DNA Polymerase and included all reagents per the manufacturers recommended conditions ( Roche Diagnostics , Indiananpolis , IN ) ., Thermocycling on an Eppendorf Mastercycler Ep Gradient ( Eppendorf Corp . , Westbury , NY ) was also according to the manufacturers recommendations with an annealing temperature of 60°C and a total of 40 cycles for this primer pair ., The restriction digest used 10 µL of PCR product , 1 . 5 units Fau I ( New England Biolabs Inc . ( NEB ) , Ipswitch , MA ) , 1x NEB Buffer 4 and enough MilliQ water to bring the reaction volume to 20 uL ., Digests were incubated at 55°C for 1 hour ., The resulting products were combined with loading buffer ( Gel Loading Dye ( 6X ) , NEB ) and separated alongside a size standard ( 100 bp DNA Ladder , NEB ) by electrophoresis following standard conditions on a 3% agarose gel ( Omnipur Agarose , EMD Chemicals Inc , Gibbstown , NJ ) ., Agarose gels were stained ( SYBRsafe DNA gel stain ( 10 , 000X ) concentrate , Invitrogen Molecular Probes , Eugene , OR ) and visualized under UV illumination ( FluroChem HD2 , Alpha Innotec Corp . , San Leandro CA ) .

Introduction, Results, Discussion, Materials and Methods

Lavender Foal Syndrome ( LFS ) is a lethal inherited disease of horses with a suspected autosomal recessive mode of inheritance ., LFS has been primarily diagnosed in a subgroup of the Arabian breed , the Egyptian Arabian horse ., The condition is characterized by multiple neurological abnormalities and a dilute coat color ., Candidate genes based on comparative phenotypes in mice and humans include the ras-associated protein RAB27a ( RAB27A ) and myosin Va ( MYO5A ) ., Here we report mapping of the locus responsible for LFS using a small set of 36 horses segregating for LFS ., These horses were genotyped using a newly available single nucleotide polymorphism ( SNP ) chip containing 56 , 402 discriminatory elements ., The whole genome scan identified an associated region containing these two functional candidate genes ., Exon sequencing of the MYO5A gene from an affected foal revealed a single base deletion in exon 30 that changes the reading frame and introduces a premature stop codon ., A PCR–based Restriction Fragment Length Polymorphism ( PCR–RFLP ) assay was designed and used to investigate the frequency of the mutant gene ., All affected horses tested were homozygous for this mutation ., Heterozygous carriers were detected in high frequency in families segregating for this trait , and the frequency of carriers in unrelated Egyptian Arabians was 10 . 3% ., The mapping and discovery of the LFS mutation represents the first successful use of whole-genome SNP scanning in the horse for any trait ., The RFLP assay can be used to assist breeders in avoiding carrier-to-carrier matings and thus in preventing the birth of affected foals .

Genetic disorders affect many domesticated species , including the horse ., In this study we have focused on Lavender Foal Syndrome , a seizure disorder that leads to suffering and death in foals soon after birth ., A recessively inherited disorder , its occurrence is often unpredictable and difficult for horse breeders to avoid without a diagnostic test for carrier status ., The recent completion of the horse genome sequence has provided new tools for mapping traits with unprecedented resolution and power ., We have applied one such tool , the Equine SNP50 genotyping chip , to a small sample set from horses affected with Lavender Foal Syndrome ., A single genetic location associated with the disorder was rapidly identified using this approach ., Subsequent sequencing of functional candidate genes in this location revealed a single base deletion that likely causes Lavender Foal Syndrome ., From a practical standpoint , this discovery and the development of a diagnostic test for the LFS allele provides a valuable new tool for breeders seeking to avoid the disease in their foal crop ., However , this work also illustrates the utility of whole-genome association studies in the horse .

genetics and genomics/animal genetics, genetics and genomics/disease models, genetics and genomics/genetics of disease, genetics and genomics/bioinformatics, genetics and genomics/medical genetics

journal.pgen.1000542

Interactions between Cells with Distinct Mutations in c-MYC and Pten in Prostate Cancer

Prevailing models of multistep carcinogenesis posit that oncogenic mutations arise in isolated cells in situ followed by clonal expansion ., This implies that important competitive interactions occur between mutant and normal cells as well as between cells with distinct oncogenic mutations during tumorigenesis ., A detailed understanding of these interactions will further efforts aimed at therapeutic targeting of neoplastic and preneoplastic lesions ., However , these interactions have not been well studied in vivo due to a paucity of appropriate models ., We report here our attempt to model these interactions in a new transgenic model of prostate cancer , focusing on the oncogene c-MYC and the tumor suppressor Pten ( Phosphatase and tensin homolog ) , both of which are implicated in human prostate tumorigenesis 1 ., c-MYC overexpression is a common early event in prostate cancer 2 , 3 while PTEN is deleted/mutated in ∼30% of primary human prostate cancers 4–8 ., Previous attempts at modeling c-MYC overexpression in the mouse prostate have used prostate-specific promoters that target transgene expression to a majority of the cells in the prostatic epithelium ., Depending on the strength of the promoter used , this resulted in various grades of mouse prostatic intraepithelial neoplasia ( mPIN ) or adenocarcinoma 3 , 9 ., Similarly , Pten-mutant mice develop mPIN and prostate cancer 10–13 and Pten inactivation can cooperate with mutations in oncogenes and tumor suppressors in prostate tumorigenesis , including p27Kip1 14 , 15 , Trp53 13 and Fgf8b 16 ., Pten loss has been reported to activate the p53 pathway , leading to senescence 13 , 17 , 18 ., Activation of p53 may lead to cell cycle arrest or apoptosis depending on the downstream target genes induced ( i . e . cell cycle arrest genes e . g . p21cip1 versus apoptotic genes e . g . PUMA ) ., There is potential cross-talk between the c-MYC and the p53 pathways at various levels depending on the cell context 19 ., c-MYC activation can increase ARF expression , thereby stabilizing p53 protein levels 20 , and c-MYC can repress expression of some p53 target genes such as p21Cip1 21 ., In addition , Pten and p53 coordinately control c-Myc protein levels with the latter playing a critical role in maintaining the stemness of murine neural stem cells 22 ., To model the sporadic genetic alterations that are thought to occur during human somatic tumorigenesis 23 , we generated a transgenic mouse in which a latent c-MYC transgene can be focally activated in the prostatic epithelium by Cre expression ., We have also deleted one or both copies of Pten in the prostate concurrently with focal c-MYC overexpression , in order to examine the interactions of cell populations with distinct mutations within the same gland ., To target focal c-MYC expression in the prostate epithelium , we used Z-MYC mice that carry a single copy transgene in which the CMV enhancer/beta actin promoter drives expression of the beta-geo gene and a latent c-MYC transgene 24 ( Figure 1A ) ., Staining for beta-galactosidase confirmed mosaic expression in the prostate epithelium ( Figure 1A ) ., To induce c-MYC expression focally in the prostate , we crossed Z-MYC mice to PbCre4 mice 25 which express Cre recombinase in the prostatic epithelium ( Figure 1A ) ., Bigenic PbCre4;Z-MYC mice expressed c-MYC focally in cytokeratin 8 ( CK8 ) positive prostate luminal epithelial cells but not p63+ basal cells ( Figure 1C ) ., Furthermore , c-MYC expression is not abrogated in castrated animals indicating that the use of the CMV enhancer/beta actin promoter in our model has uncoupled prostate-specific expression from androgen-dependent gene regulation ( Figure 1B ) ., Focal c-MYC activation resulted in mild pathology , with most prostates showing normal histology or low grade mPIN ( LGPIN ) lesions up to 2 years of age ( Figure 2A and 2B ) ., This is unlikely to be due to low level c-MYC expression as the CMV enhancer/beta actin promoter is known to drive high level transgene expression ., A closer examination of the c-MYC expression pattern in the prostates of PbCre4;Z-MYC mice with no pathology showed that in young mice , the frequency of c-MYC-positive cells was ∼18% of the epithelial cells in c-MYC-positive glands ( Figure 3G ) , evocative of the frequency of LacZ-positive cells ( ∼17% ) in Z-MYC prostates ( Figure 1A ) ., By 1 year , the frequency of c-MYC positive cells has increased to ∼43% ( Figure 3G ) ., The lack of discernible histological abnormality in the prostates of a large fraction of older PbCre4;Z-MYC mice in the face of abundant c-MYC expression is reminiscent of the phenomenon of “field cancerization” in human tumorigenesis where incipient mutant cells occupy tissue fields without any apparent pathology ( Figure 3A–3F ) 26 ., These histologically normal but mutant cells may serve as targets for transformation with additional genetic mutations ., Next , we generated compound mutant mice with prostate-specific deletion of one or both alleles of Pten concurrently with focal activation of c-MYC ., Examination of PbCre4;Z-MYC;Ptenf/+ prostates revealed clear cooperation between c-MYC overexpression and Pten heterozygosity ( Figure 2 ) ., As reported previously 10 , 11 and confirmed by us here , conditional deletion of a single Pten allele had little effect on the prostate with mice up to 50 weeks of age showing minimal abnormalities ( Figure 2A and 2B ) ., By ∼10 weeks of age however , PbCre4;Z-MYC;Ptenf/+ mice already have evidence of focal HGPIN lesions ., Over time , these animals develop micro-invasive cancer as confirmed by the presence of areas with disruption in smooth muscle actin ( SMA ) immunoreactivity ( Figure 2B and 2C ) ., We used immunohistochemistry to examine the status of the wild type Pten allele in the HGPIN/cancer lesions in PbCre4;Z-MYC;Ptenf/+ mice ., Consistently , all lesions examined ( N\u200a=\u200a8 mice ) showed loss of Pten protein expression and phosphorylation of its downstream signaling components Akt and Foxo1 27 ( Figure 4 ) ., We analyzed proliferation by staining for phospho-histone H3 ( pHH3 ) , a mitotic marker ., Proliferation was increased significantly in PbCre4;Z-MYC prostates relative to controls , and Pten heterozygosity synergistically increased it further ( Figure 5A ) ., The proliferation rates in PbCre4;Ptenf/f and PbCre4;Z-MYC;Ptenf/f were similarly elevated ., However , the focal nature of c-MYC expression in our model means that analysis of total proliferation in the PbCre4;Z-MYC;Ptenf/f prostates may not be an accurate measure of the proliferation in foci of c-MYC+;Pten-null cells ., To overcome this , we performed double staining for c-MYC and phospho-histone H3 ., As shown in Figure 5B , double mutant ( c-MYC+;Pten-null ) cells were more proliferative than single mutant ( Pten-null ) cells within the same prostate glands ., Furthermore , double mutant cells are histologically distinct from adjacent single mutant cells ., The double mutant cells are of higher pathological grade with larger nuclei , high nuclear∶cytoplasmic ratios , hyperchromatic nuclei with prominent chromocenters , focal chromatin clearing and prominent single or sometimes multiple nucleoli ( Figure 5C and Figure S1 ) ., In addition , apoptotic and mitotic figures are prominent ., Single mutant ( Pten-null ) cells on the other hand showed low nuclear grade with comparatively small and uniform nuclei , abundant pale cytoplasm and low nuclear∶cytoplasmic ratios ., These cells also have inconspicuous nucleoli and the chromatin is comparatively fine ( Figure 5C ) ., These observations suggest that c-MYC+;Pten-null cells may out-compete Pten-null cells within the same prostate gland over time ., Indeed , analysis of PbCre4;Z-MYC;Ptenf/f animals showed that at early ages , c-MYC expression was focal within glands , but in older mice , lesions show uniform c-MYC expression , suggesting clonal expansion of c-MYC-positive cells in a time-dependent manner ( Figure 5D ) ., Analysis of apoptosis by staining for activated Caspase 3 shows that control and PbCre4;Ptenf/+ prostates had low levels of apoptosis , consistent with their normal histology , while focal expression of c-MYC in PbCre4;Z-MYC prostates modestly increased apoptosis ( Figure 6A ) ., Although c-MYC overexpression is known to induce apoptosis in several tissues , this depends on many variables including the level of c-MYC overexpression and the “tissue context” 28 , 29 ., The levels of apoptosis seen in PbCre4;Z-MYC prostates are consistent with increased cell turnover due to enhanced proliferation ., Pten-null prostates also had increased rates of apoptosis , and c-MYC overexpression further enhanced this effect ( Figure 6A ) ., These results were surprising as Pten loss is known to activate pro-survival pathways ., Therefore , we sought to determine if apoptosis is increased in HGPIN/cancer cells that have lost Pten expression in our PbCre4;Z-MYC;Ptenf/+ mice ., Double staining for Pten and activated Caspase 3 and quantitative analysis indicated higher rates of apoptosis in Pten-negative cells compared to Pten-positive cells ( Figure 6B ) ., Thus Pten loss does not protect prostate cells from apoptosis due to c-MYC overexpression ., In addition to Akt , the c-Jun N-terminal kinase ( Jnk ) pathway is known to be activated in Pten-deficient cells and tumors 30 , 31 ., We confirmed that the Jnk pathway is activated in both Pten-null and c-MYC-overexpressing/Pten-null prostates by immunohistochemistry for phospho-Jnk ( Figure S2A ) ., Since Jnk is well known to have the ability to activate apoptosis , cell survival and proliferation depending on cellular signal stimuli and cellular contexts 32 , we asked if increased Jnk activity sensitizes Pten-deficient cells to apoptosis ., We used small hairpin RNA to stably downregulate PTEN in the benign human prostatic cell line RWPE1 ., However , treatment with the Jnk inhibitor ( SP600125 ) led to an increase in apoptosis in PTEN knockdown cells in a dose-dependent manner , suggesting that PTEN loss-induced Jnk activity is anti-apoptotic , rather than pro-apoptotic ( Figure S2 ) ., It is known that Pten loss can activate the p53 pathway in the prostate cells and activation of the p53 pathway could lead to either senescence or apoptosis depending on the particular p53 target genes induced 13 , 17 , 18 , 33 ., We therefore sought to examine activation of the p53 pathway in our c-MYC/Pten model and to determine whether concurrent c-MYC expression alters the p53 response ., We observed induction of p53 , its targets p21cip1 and PUMA in Pten-null prostates ( Figure 7A ) ., However , while p53 and PUMA were induced in c-MYC-overexpressing Pten-null prostates , p21cip1 expression was not ( Figure 7A ) , consistent with the notion that c-MYC represses p21cip1 expression 21 ., Similar results were obtained in RWPE-1 cells ( Figure 7B ) ., While p53 and p21cip1 were induced upon PTEN knockdown , c-MYC overexpression repressed p21cip1 expression ( Figure 7B ) ., We hypothesized that in Pten-deficient cells with activation of the p53 pathway , repression of p21cip1 by c-MYC may switch the senescent response to apoptosis ., Indeed , using immunofluorescence , we found that in PbCre4;Z-MYC;Ptenf/f prostates , p16ink4a expression ( a marker of senescence ) is mainly localized to c-MYC-negative cells while apoptosis ( activated Caspase 3 ) is found predominantly among c-MYC-positive cells ( Figure 7C ) ., Thus Pten-deficiency activates the p53/p21cip1 pathway but concurrent c-MYC overexpression shifts the output of the pathway from senescence to apoptosis at least partly by repressing p21cip1 ., Human prostate carcinogenesis is focal , random , and incremental , but current mouse models do not faithfully recapitulate this ., Consequently , the competitive/cooperative interactions that may occur between mutant and normal cells during the early stages of tumorigenesis have not been well studied ., The model described here exploits the stochastic expression of a “Cre-activatable” c-MYC transgene ( Z-MYC ) to induce c-MYC expression in isolated cells surrounded by normal cells ., As illustrated by our studies when the Z-MYC mouse is crossed with prostate-specific Pten deletion , the focal nature of c-MYC expression allows analysis of cell populations with different genetic alterations within the same prostate gland ., Our studies have yielded several insights ., First , focal expression of c-MYC in prostate luminal epithelial cells , even though driven by the CMV enhancer/beta actin promoter , results in remarkably mild pathology with many mice showing histologically normal prostates and a subset of mice demonstrating LGPIN lesions ., These results imply a remarkable tolerance of luminal epithelial cells to c-MYC expression ., We showed that the acquisition of additional genetic mutations is essential for the appearance of discernable pathology by the fact that introduction of Pten heterozygosity into these animals resulted in cooperativity , with the development of HGPIN/cancer lesions which in all cases were associated with loss of Pten protein expression from the wild type allele ., These observations highlight an important point about c-MYC-expressing cells in histologically “normal” glands , as may occur in tumors and tissues with “field cancerization” 26 , 34–37 in that the overexpression of c-MYC in histologically “normal” cells may facilitate the acquisition of secondary mutations ., Although it remains to be established whether loss of Pten expression is due to genetic , epigenetic or post-transcriptional control , c-MYC expression may facilitate acquisition of secondary mutations by increasing cell turnover and/or genomic instability 38 , 39 ., Our PbCre4;Z-MYC;Ptenf/f mice allowed us to examine the behavior of prostate cells with distinct mutations in the same prostate ., c-MYC expression clearly confers an additional proliferative advantage to Pten-null prostate cells , allowing c-MYC+;Pten-null cells to outcompete Pten-null cells ., However , Pten deficiency did not alleviate apoptosis in c-MYC+;Pten-null cells ., This may appear surprising in light of the well-known , pro-survival effect of Pten loss 40 and a report that Pten loss decreased the apoptosis engendered by the inactivation of retinoblastoma ( pRb ) family proteins by a truncated SV40 T large antigen in the mouse prostate 41 ., Nevertheless , previous studies of mice with conditional deletion of Pten in the prostate and testicular germline cells have noted an increased rate of apoptosis upon Pten deletion 10 , 11 , 42 and Radziszewska et al recently showed that deleting Pten concurrently with c-MYC activation in pancreatic beta cells led to increased apoptosis 43 ., Furthermore , Pten deficiency has been reported to activate the p53 pathway leading to senescence 13 , 18 , 44 as well as to sensitize cells to ROS-induced apoptosis 17 ., Based on our studies and published reports , we propose the following model of cooperativity between c-MYC and Pten in prostate cancer ( Figure 7D ) : Overexpression of c-MYC initiates tumorigenesis by facilitating loss of Pten ., The latter leads to the activation of the p53 pathway , which can result in either senescence or apoptosis depending on the predominant Trp53 target genes induced ( i . e . cell cycle arrest genes e . g . p21cip1 versus pro-apoptotic genes e . g . PUMA , Bax etc . ) ., The expression of c-MYC drives cells down the apoptotic pathway as it selectively represses the cell cycle arrest-inducing target gene p21cip1 ., To summarize , we report a new Cre-dependent prostate cancer mouse model that reflects the focal , random and incremental nature of human prostate carcinogenesis ., We show that focal c-MYC expression cooperates with Pten heterozygosity to promote tumor progression due to the selection of cells with loss of Pten expression ., In addition , cells mutant for both c-MYC and Pten outcompete single Pten-mutant cells within the same prostates although Pten-deficiency sensitizes cells to apoptosis that is associated with activation of the p53 pathway and exacerbated by c-MYC expression ., Our results highlight the utility of modeling focal oncogene activation to study the interactions between cell populations with different genetic alterations in tumorigenesis ., Z-MYC , PBCre4 and Ptenf/f mice have been described 24 , 25 , 45 ., Female Z-MYC mice ( B6/129 ) were bred to male PbCre4 mice ( B6 ) obtained from MMHCC , Frederick , to generate PbCre4;Z-MYC offspring and littermate controls ., Ptenf/f mice ( B6/129 ) were obtained from The Jackson Laboratory ., To generate compound mutant mice , we generated PbCre4;Ptenf/+ males and Z-MYC;Ptenf/+ females which were further bred to obtain PbCre4;Z-MYC , PbCre4;Ptenf/f , PbCre4;Z-MYC;Ptenf/+ and PbCre4;Z-MYC;Ptenf/f offspring for experiments as well as their littermate controls ., Animal care and experiments were carried out according to the protocols approved by the Institutional Animal Care and Use Committees at Vanderbilt University ., Beta-galactosidase staining followed by counterstaining with nuclear fast red was performed as described 24 ., Tissues were prepared for histopathological analysis as described 46 , and slides were reviewed by IEA based on published criteria 47 ., Immunohistochemical analyses were performed as described 46 ., The following antibodies were used , in some cases with Tyramide Signal Amplification ( TSA; Perkin Elmer ) : anti-activated Caspase 3 ( rabbit , 1∶500 , Cell Signaling ) , anti-phospho-histone H3 ( rabbit , 1∶500 , Upstate ) , anti-phospho-Akt ( rabbit , 1∶100 , Cell Signaling ) , anti-phospho-Foxo1 ( rabbit , 1∶50 , Santa Cruz ) , anti-c-MYC ( rabbit , 1∶15 , 000 with TSA , Santa Cruz ) , anti-Pten ( rabbit , 1∶200 with TSA , Cell Signaling ) , anti-cytokeratin 8 ( mouse , 1∶2000 , Sigma ) , anti-p63 ( PIN Cocktail , Biocare Medical ) , anti-p53 ( rabbit , 1∶5000 with TSA , Santa Cruz ) , anti-p21 ( mouse , 1∶50 , Santa Cruz ) , anti-smooth muscle actin ( mouse , 1∶2000 , Sigma ) , anti-p16 ( rabbit , 1∶1000 , Santa Cruz ) , anti-Puma ( rabbit , 1∶200 , Cell Signaling ) and anti-phospho-Jnk antibody ( rabbit , 1∶100 , Cell Signaling ) ., For double immunofluorescenct stains , c-MYC or Pten detected by 1st primary antibodies were amplified by TSA system ( green , Fluorescein ) ., Alexa Fluor 594 ( red ) -labeled 2nd secondary antibodies ( Molecular Probes ) were used to detect 2nd primary antibodies ( anti-cytokeratin 8 , anti-p63 , anti-smooth muscle actin , anti-phospho-histone H3 , anti-p16 and anti-activated Caspase 3 ) ., Nuclear stain ( DAPI ) and sample mounting were performed using Vectashield mounting medium ( Vector Laboratories ) ., At least 500 cells per sample were counted and quantitated after immunohistochemistry for phospho-Histone H3 and activated Caspase 3 , respectively ., N\u200a=\u200a3–4 prostate samples from 9–15 week-old mice per group ., RWPE-1 , benign human prostate epithelial cell line ( ATCC ) was cultured in keratinocyte serum-free media supplemented with bovine pituitary extract and EGF ( Invitrogen ) ., We used lentiviral-mediated gene transfer to generate PTEN knockdown/c-MYC overexpressing cells ., 293FT packaging cells were plated on 10 cm culture dishes and transfected with PTEN shRNA construct/pLKO . 1 vector control ( Sigma ) or the c-MYC construct/FM-1 vector control along with vesicular stomatitis virus glycoprotein ( VSVG ) envelope plasmid and delta 8 . 9 packaging plasmid to produce lentivirus ., The FM-1 vector was obtained from J . Milbrandt 48 and was used to clone in human c-MYC cDNA ., Three days after transfection , medium containing viral particles was collected and added to RWPE-1 for infection with polybrene ( 8 µg/ml ) ., 24 hours post infection , medium was changed and another 24 hours later puromycin ( 1 µg/ml ) was added for selection of sh-Pten/pLKO . 1 cells ., YFP-positive c-MYC/FM-1 cells were sorted by flow cytometry ., These were performed as described 49 using the following antibodies: anti-Pten ( mouse , 1∶1000 , Cell signaling ) , anti-phospho-Akt ( rabbit , 1∶2000 , Cell signaling ) , anti-total Akt ( rabbit , 1∶2000 , Cell signaling ) , anti-c-MYC ( mouse , 1∶500 , Santa Cruz ) , anti-p53 ( mouse , 1∶1000 , Santa Cruz ) , anti-p21 ( mouse , 1∶1000 , Santa Cruz ) and anti-beta-actin antibody ( goat , 1∶1000 , Santa Cruz ) ., Coverslips were placed on the 24-well plates and 300 , 000 control or Pten knockdown RWPE-1 cells were plated on the coverslips ., Next day , cells were treated with Jnk inhibitor ( SP600125 ) or vehicle ( DMSO ) at 0 , 10 or 50 µM for one hour ., Then cells were washed with phosphate buffered saline and supplement-free medium was added to induce apoptosis ., After 48 hours , immunocytochemistry for activated Caspase 3 was performed and apoptosis was quantitated from triplicate data per group ., We compared groups by using t-test ., Values are considered statistically significant at P<0 . 05 ., Quantitative variables are expressed as means±SD while categorical variables are expressed as numbers ( % ) .

Introduction, Results, Discussion, Materials and Methods

In human somatic tumorigenesis , mutations are thought to arise sporadically in individual cells surrounded by unaffected cells ., This contrasts with most current transgenic models where mutations are induced synchronously in entire cell populations ., Here we have modeled sporadic oncogene activation using a transgenic mouse in which c-MYC is focally activated in prostate luminal epithelial cells ., Focal c-MYC expression resulted in mild pathology , but prostate-specific deletion of a single allele of the Pten tumor suppressor gene cooperated with c-MYC to induce high grade prostatic intraepithelial neoplasia ( HGPIN ) /cancer lesions ., These lesions were in all cases associated with loss of Pten protein expression from the wild type allele ., In the prostates of mice with concurrent homozygous deletion of Pten and focal c-MYC activation , double mutant ( i . e . c-MYC+;Pten-null ) cells were of higher grade and proliferated faster than single mutant ( Pten-null ) cells within the same glands ., Consequently , double mutant cells outcompeted single mutant cells despite the presence of increased rates of apoptosis in the former ., The p53 pathway was activated in Pten-deficient prostate cells and tissues , but c-MYC expression shifted the p53 response from senescence to apoptosis by repressing the p53 target gene p21Cip1 ., We conclude that c-MYC overexpression and Pten deficiency cooperate to promote prostate tumorigenesis , but a p53-dependent apoptotic response may present a barrier to further progression ., Our results highlight the utility of inducing mutations focally to model the competitive interactions between cell populations with distinct genetic alterations during tumorigenesis .

In most human cancers , mutations are thought to arise in a single cell or few cells surrounded by their unaffected neighbors ., Expansion of mutant cells can then allow the accumulation of additional mutations ., The cell–cell interactions that may occur between mutant and unaffected cells or between cells with distinct mutations during tumorigenesis have not been well studied due to the lack of suitable in vivo models ., To help fill this gap , we generated and characterized transgenic mice in which the oncogene c-MYC is activated focally in prostate epithelial cells ., We have also analyzed mice in which prostate epithelial cells with two mutations ( c-MYC overexpression and loss of Pten tumor suppressor ) are found next to cells with a single mutation ( loss of Pten ) ., Although loss of Pten in the prostate is tumorigenic , it also activates a cellular senescence response which restrains further tumor progression ., We found that concurrent c-MYC expression suppressed the senescence response in Pten-null cells in favor of apoptosis ., c-MYC+;Pten-null cells proliferated faster than Pten-null cells in the same glands , with the net result that c-MYC+;Pten-null cells outcompete Pten-null cells ., Our results demonstrate the utility of accurate models to mimic the heterogeneous and incremental nature of human prostate carcinogenesis .

genetics and genomics/disease models, genetics and genomics/animal genetics, oncology/prostate cancer, genetics and genomics/cancer genetics

journal.pgen.1002380

A High-Resolution Whole-Genome Map of Key Chromatin Modifications in the Adult Drosophila melanogaster

Epigenetics refers to the regulation of gene expression that is heritable to daughter cells without alteration of genetic information 1 ., Epigenetic regulation is commonly achieved via DNA methylation , covalent modification of histones , and association/dissociation of chromatin factors 2 ., Chromatin modifications of many genes in a genome in a specific fashion leads to epigenetic programming of the genome ., It has been assumed that chromatin modifications occur in a cell-type-specific fashion in order to specify and maintain diverse cell fates 3 ., This presumed central feature of chromatin modifications has been the subject of intensive investigation and has been supported by abundant evidence ., However , of equal importance , there must also be common patterns of chromatin modifications that exist in all types of cells , which would reflect general features of the epigenome that are shared by diverse cell types within an organism or even among distant species ., It is important to understand such general features of chromatin modifications , and substantial effort has been devoted to this area of study ., There is strong evidence supporting the existence of general features of chromatin modifications that are shared by all types of cells ., Perhaps the strongest evidence is the presence of constitutive heterochromatin in centromeres and telomeres — a feature not only present in all types of nucleated cells within an organism but also well conserved during evolution 4 ., Centromeric heterochromatin is essential for chromosome condensation and segregation during mitosis; whereas telomeric heterochromatin may be related to telomere function and telomeric silencing of transcription ., Beyond these two examples , relatively little is known about the general features of chromatin modifications in the bulk of the genome , especially in the euchromatic genome ., To explore these general features systematically , we combined high-resolution chromatin immunoprecipitation and high-throughput sequencing ( ChIP-Seq ) to map the distribution patterns of a panel of histone modifications , Heterochromatin Protein 1a ( HP1a ) , and RNA polymerase II ( RNA polII ) in Drosophila melanogaster ., This allowed us to construct a high resolution whole-genome map of Drosophila with these key chromatin modifications and the transcriptional activity mapped at 50 base-pair resolution ., Our mapping data are consistent with recent major mapping efforts in Drosophila cell lines and major developmental stages 5 , 6 , 7 , 8 ., Moreover , our map , derived from all cell types in the adult Drosophila weighted by their natural abundance , reveals striking features of the chromatin modifications with important functional implications ., To gain high resolution whole-genome maps of the Drosophila chromatin modification , we isolated nuclei from whole adult flies for ChIP-Seq ., In order to achieve an unbiased representation of both euchromatin and heterochromatin in the following ChIP , we modified the standard ChIP-Seq method by first treating nuclei with limited amount of micrococcal nuclease ( MNase ) and then separating chromatin into euchromatic and heterochromatic fractions ( Figure 1A ) ., Chromatin in heterochromatin fractions was further fragmented by sonication into a size range comparable to the euchromatic chromatin ( Figure S1A ) ., Chromatin from euchromatic and heterochromatic fractions were subjected to immunoprecipitation of post-translationally modified histone 3: histone 3 trimethylated at Lysine 4 ( H3K4me3 ) and acetylated at lysine 9 ( H3K9ac ) as euchromatic marks , whereas histone 3 trimethylated at Lysine 9 ( H3K9me3 ) and trimethylated at Lysine 27 ( H3K27me3 ) as heterochromatic marks ., To minimize biases introduced by partial MNase digestion and nucleosome positioning , we preformed the immunoprecipitation of total histone 3 ( H3 ) as a control for normalization ., In addition , crosslinked chromatin was used for immunoprecipitation of HP1a , a heterochromatic protein , as well as RNA polII that indicates transcription activity ( Figure 1A ) ., For these two epigenetic marks , a mock ChIP was conducted as a control for normalization ., The high specificity of HP1a antibody used in this study was confirmed by Western blotting ( Figure S1B ) ., All precipitated DNA was sequenced by Illumina Genome Analyzer 1G , which achieved 7 . 9-fold coverage of the Drosophila genome in total ( Table S1 ) ., The relative abundance of epigenetic marks across the entire genome was quantified as detailed in Materials and Methods and Figure S1 ., So far , most published bioinformatic analyses of ChIP-Seq are based exclusively on unique-mapping ( i . e . deriving from single genomic location ) Illumina reads , which have unambiguous genomic origins 9 ., However , we find that ∼24 . 5% of Illumina reads from the mock ChIP sample are multiple-mapping reads with more than one matching site within the genome ( Table S1 ) ., BLAST analyses indicate that these multiple-mapping reads represent repetitive , low complex , and transposon-derived sequences , frequently found in heterochromatic regions of the Drosophila genome ( data not shown ) ., The fact that some heterochromatic marks are mostly enriched in repetitive sequences and that these repetitive sequences function in heterochromatic silencing demands the inclusion of these multiple-mapping reads in the ChIP-Seq analyses ., To this end , we employed two different calculations in the score generation step of ChIP-Seq analyses: a unique-mapping only method , which calculates the ChIP-Seq scores purely based on unique-mapping reads ChIP-Seq ( U ) ; and a method combining both unique-mapping and multiple-mapping reads ChIP-Seq ( U+M ) ( Figure S1 ) ., In the latter method , a multiple-mapping tag contributes equally to all matching genomic sites with score matrices weighted by the reciprocal of the number of genomic matching sites ., Although this method cannot discriminate multiple matching sites for a single Illumina read , we reasoned that many multiple-mapping reads and unique-mapping reads together will generate individual scores for similar transposon/repetitive sequences in the genome ., A similar approach was recently employed to interrogate H3K9me3 distribution pattern within repetitive genomic regions in human CD4+ T lymphocytes 10 ., To validate our ChIP-Seq analyses , we first compared our ChIP-Seq results of HP1a distribution patterns with the published results of HP1a Chromatin IP combined with the genome tiling array experiment ( ChIP-Chip ) in Drosophila S2 cells 11 and DNA adenine methyltransferase identification combined with the genome tiling array experiment ( DamID-Chip ) in adult whole flies 12 ., Our ChIP-Seq ( U ) results faithfully reproduce HP1a localizations from the ChIP-Chip assay with a Pearson Product-Moment correlation coefficient as high as 0 . 83 ( Figure 1B ) ., We find that both ChIP-Seq ( U ) and ChIP-Seq ( U+M ) results feature eminent resolutions and can largely replicate previous observations of HP1a distributions in a gene-rich region ( Figure 1C ) ., Strikingly , our ChIP-Seq ( U+M ) scores successfully recapitulate previous findings of the DamID-Chip assay showing that HP1a is specifically associated with a Doc retrotransposon , but not with an adjacent copia retrotransposon ( Figure 1D ) ., Overall , our ChIP-Seq ( U+M ) results largely repeat the HP1a distribution patterns from DamID-Chip assay ( Pearson correlation coefficient\u200a=\u200a0 . 77 , Figure S2 ) ., Again , our ChIP-Seq ( U+M ) data on HP1a features much higher resolution ( 50 bp ) as compared to the DamID-Chip method ., Using the above-described method , we conducted the whole-genome mapping of H3K4me3 , H3K9me3 , H3K27me3 , H3K9ac , HP1a , and RNA polII in euchromatic arms ( chrX , chr2L , chr2R , chr3L , chr3R and chr4; hereafter called euchromatic genome ) as well as other sequenced internal scaffolds and unmapped regions ( XHet , 2LHet , 2RHet , 3LHet , 3RHet , YHet , U and Uextra; hereafter called heterochromatic genome ) ., To gain an overview of the distributions of chromatin modifications , we compared their ChIP-Seq ( U+M ) scores over different genomic features ( CDS , 5′UTR , 3′UTR , intron , transposon/repetitive sequence , and intergenic region ) within the euchromatic genome and all sequenced genome ( Figure 2A ) ., This comparison reveals distinct distribution patterns of chromatin modifications in the genome related to specific types of genomic sequences ., We find RNA polII and H3K9ac are highly enriched in protein-coding genes , with 69 . 3% of RNA polII scores and 62 . 3% of H3K9ac scores located within CDS , 5′UTR , 3′UTR and intron regions ( Figure 2A ) ., This is consistent with the notion that these two chromatin modifications are associated with actively transcribing genes 13 ., Within genes , RNA polII and H3K9ac show distinct distribution patterns with respect to subgenic regions: RNA polII is preferentially present in CDS and 5′UTR regions whereas H3K9ac is relatively enriched in introns ., In contrast to these euchromatic marks , 85 . 9% of HP1a scores and 78 . 7% of H3K9me3 scores are situated in transposons and repeats within all sequenced genome ( Figure 2A ) , which largely reflect the natural abundance of these two marks on polytene chromosomes 14 ., Interestingly , we find transposons and repeats include 59 . 3% and 73 . 3% of H3K4me3 scores within euchromatic and all sequenced genome , respectively ( Figure 2A ) ., This is consistent with previous reports that both euchromatic ( H3K4me3 ) and heterochromatic ( H3K9me3 ) marks are present within heterochromatin 15 , 16 ., To explore the chromatin modification of transposons , we calculated the total ChIP-Seq ( U+M ) scores of chromatin modifications on all transposons in the genome ., We find that heterochromatic marks H3K9me3 , H3K27me3 and HP1a are abundant within transposons ( Figure 2B ) ., In contrast , transposons are mostly devoid of transcription activity marks , H3K9ac and RNA polII ., These results are consistent with the notion that most transposons in the Drosophila genome are transcriptionally silenced whereas a small portion of transposons remain transcriptionally active 17 ., To investigate epigenetic marks co-localized in transposons , we performed pair-wise Pearson correlation analyses for chromatin modification densities in transposons classified into 185 classes ( Figure 2C ) ., The significant positive correlation between H3K9me3 density and H3K27me3 density indicates these two chromatin modifications are co-localized on transposons ( P . c . =\u200a0 . 9 , p\u200a=\u200a8 . 746×10−68 ) ., We find H3K9me3 is also co-occcurring with HP1a within transposons ( P . c . =\u200a0 . 336 , p\u200a=\u200a3 . 01×10−6 ) , which suggests HP1a is recruited here by this mark ., In addition , correlated RNA polII and H3K9ac densities ( P . c . =\u200a0 . 444 , p\u200a=\u200a2 . 42×10−10 ) implicates some transposons , like G6 and Burdock , are transcriptionally active in the Drosophila genome ( Figure 2C , 2D ) ., To further investigate the enrichment patterns of chromatin modifications within protein-coding genes , we sorted ∼2 . 4 million 50-bp windows within euchromatic genomes into 100 percentiles based on their ChIP-Seq ( U ) scores and calculated the percentages of genomic features for every percentile individually ( Figure 3A–3E , Figure 4A ) ., The relative abundance of a chromatin modification over a genomic feature was determined by comparing the percentages to the natural representation of the genomic feature within the euchromatic genome ( Figure 3A–3E , Figure 4A ) ., Furthermore , we determined the distribution of these chromatin modifications relative to the transcriptional start sites ( TSSs ) , the mid points of gene bodies , and the transcription end sites ( TxEnds ) of protein coding genes with regard to their transcriptional levels ( Figure 3F–3J , Figure 4B ) ., 6 , 756 genes with known gene expression levels were classified into 10 groups according to their relative expression levels in whole fly samples interrogated by microarray experiments ( GSE5382 , GSE7763 ) , with each group representing a 10% increment of expression levels ., Within protein coding genes , the top 10% of H3K4me3- and H3K9ac-dense sequences are highly represented in 5′UTRs and CDSs ( Figure 3A and 3B ) ., Specifically , both H3K4me3 and H3K9ac are highly enriched in the 5′ ends of high- and medium-expressing genes ( +50 bp∼+750 bp for H3K4me3 and +50 bp∼+1 kb for H3K9ac ) , but sharply declined around TSSs ( −50 bp∼+50 bp ) and severely under-represented in proximal promoter regions ( −600 bp∼TSS ) ( Figure 3F and 3G ) ., Such a dynamic pattern is not observed in low-expressing and silent genes ., H3K9ac differs from H3K4me3 in two additional features within protein coding genes ., First , moderately to highly H3K9ac-dense sequences ( 70th∼90th percentiles ) are also enriched in intronic sequences but devoid from intergenic regions ., This is consistent with the notion that H3K9ac specifically associates with transcriptional activity and can spread over the whole gene body 18 ., Second , H3K9ac is enriched in 3′ends of genes ( −1 kb regions upstream of TxEnds ) of medium- and high-expressing genes , in contrast to the slight enhancement of H3K4me3 at the TxEnds ( Figure 3F and 3G ) ., The H3K9me3 mark is the binding target of HP1a , and is generally regarded as an epigenetic silencing mark 19 , 20 ., Within protein coding genes , extremely H3K9me3-dense ( top 2% ) sequences are located in intergenic and intronic regions ( Figure 3C ) ., Intriguingly , in actively transcribed genes , H3K9me3 is highly enriched in the promoter region ( −1 kb∼−100 bp ) but generally depleted in the 5′ ends of genes ( Figure 3H ) ., This pattern is opposite to that of H3K4me3 and H3K9ac , and echoes recent observations that H3K9me3 is associated with promoters of active genes in mammalian genomes 21 , 22 ., Similarly , H3K27me3 , the binding target for Polycomb repressive complex 1 ( PRC1 ) , is enriched in discrete intergenic regions ( Figure S3A , Table S2 ) , but under-represented in CDS , 3′UTR and intronic regions ( Figure 3D ) ., Most of H3K27me3-enriched regions are located within cytological bands that were previously identified as cytobands bound by Polycomb proteins on polytene chromosomes and S2 cells ( Figure S3A ) 5 , 8 , 23 , 24 , 25 ., Moreover , of 167 predicted PRE/TREs 25 , 89 are enriched for the H3K27me3 marks , which validates these PRE/TRE as constitutive binding sites for PRC1 in adult flies ., For example , the three most prominent H3K27me3-enriched regions on chromosome arm 3R are the Antennapedia complex ( ANT-C ) , Bithorax complex ( BX-C ) , and a 200-kb region between mod ( mdg4 ) and InR , which contains multiple predicted PRE/TREs ( Figure S3B ) ., At boarders of ANT-C and BX-C , as well as in active genes CG7922 and CG7956 , H3K27me3 is dramatically reduced to background levels ., On average , genomic regions surrounding the 167 predicted PRE/TREs are significantly enriched for H3K27me3 marks comparing to randomly selected intergenic regions within the euchromatic genome ( Figure S3C ) ., Expectedly , the density of H3K27me3 in the promoter , 5′ ends , bodies , and 3′ ends of protein coding genes are negatively correlated to mRNA levels ( Figure 3I ) ., H3K27me3 is generally absent from medium- and high-expressing genes , but is enriched in the promoters and 5′ ends ( −1 kb∼+1 kb ) of silent and extremely low-expressing genes ., This pattern resembles the distribution of H3K27me3 in the human genome 26 and reflects its function in long-term gene silencing 24 , 27 ., Notably , for low expressing genes , H3K27me3 is enriched in the promoter regions ( −1 kb∼−250 bp ) and 5′ ends ( +200 bp∼+1 kb ) , but is absent around the TSSs ., This observation appears to be consistent with recent findings that H3K27me3 and H3K4me3 are co-localized at a group of ‘bivalent’ promoters poised for transcription 28 ., Consistent with the fact that RNA polII is the central player of transcription , the top 20% of polII-dense sequences are conspicuously over-represented within 5′UTRs and intergenic regions , yet moderately polII-dense sequences ( within 40∼80% ) are also enriched in CDS ( Figure 3E ) ., Moreover , the level of RNA polII is strictly correlated to the RNA expression level ( Figure 3J ) ., Particularly , polII concentrates around TSSs , forming a sharp peak within a narrow region immediately downstream of TSSs ( 0 bp∼+100 bp , Figure 3J ) ., Significant RNA polII signals are also present within gene bodies and at the 3′ ends of expressing genes ., Although HP1a is predominantly associated with transposons and repeats , about 23% of HP1a ChIP-Seq ( U+M ) scores are present in genic/intergenic regions ., Within these regions , HP1a is particularly enriched in the 5′UTR regions and coding sequences ( Figure 4A ) ., Within a transcriptional unit , HP1a is highly concentrated around the TSS with only low levels of HP1a spreading over the gene body ( Figure 4B ) ., Strikingly , the levels of HP1a concentration at the TSSs are strictly correlated to the mRNA levels of its residing genes , confirming previous reports ( see Discussion ) ., Particularly , the sharp peaks of HP1a immediately surrounding TSSs ( 0 bp∼+100 bp ) mimic the polII enrichment within the same regions ., These prominent similarities strongly suggest HP1a functions together with RNA polII in transcription ( see Discussion ) ., The high levels of agreement between our whole-fly-derived HP1a scores and ChIP-Chip scores generated from embryonic S2 cells indicate HP1a localizations are generally stable during development ., Thus , we recruited a published microarray dataset , which contains gene expression data for both wild type third instar larva with and without HP1a-knockdown 29 ., 12 , 521 interrogated genes were sorted and grouped into 100 percentiles based on their folds of changes in gene expression ( hereafter called fold of change percentiles; Figure 4C ) ., To better understand genes regulated by HP1a , we calculated 11 additional features for genes in all percentiles ( Figure 4C ) ., Interestingly , genes highly repressed in HP1a RNAi knockdown larva ( 1st∼3rd fold of change percentiles , green dots ) are overly high-expressing genes in wild type larva , which are generally short in length and away from centromeres ., By contrast , genes highly activated by HP1a knockdown ( 97th∼100th fold of change percentiles , red dots ) are generally devoid of any recognizable feature ., We find a distinct third class of genes , representing moderately activated genes in HP1a knockdown ( 80th∼97th fold of change percentiles , yellow dots ) ., This class predominantly contains high-expressing , large genes , characterized by their large numbers of sparsely located exons ., Notably , these genes also tend to localize within gene-rich regions ., However , none of the above gene classes is correlated to transposon/repeat densities either upstream , downstream or within the gene bodies ., The above analyses implicate that HP1a concentrated at TSSs may have a direct function in regulating the expression of its target genes ., To understand this function , we asked whether HP1a is specifically enriched at TSSs of its target genes ., The HP1a density surrounding TSSs of 10 gene classes grouped by 10% increments of fold of change percentiles was investigated ( Figure 4D ) ., We find HP1a is enriched at TSSs of genes that are either highly repressed ( 1∼10% percentile ) or highly activated ( 90∼100% percentile ) in HP1a knockdown , indicating HP1a has direct functions of both activation and silencing on its target genes ., Intriguingly , the highest levels of HP1a enrichment at TSSs are found among the third class genes that are moderately activated by HP1a RNAi , suggesting this gene class represents a distinct HP1a-mediated regulome ., We further calculated averaged levels of histone modifications over TSS regions ( +/−500 bp ) for all percentiles but failed to identify any correlation ( Figure 4E–4F ) ., This suggests that HP1a-mediated gene expression regulation is globally independent of other examined chromatin modifications ., Recent studies have revealed that RNA polII is poised or stalled at the TSS regions of about 10% genes in the Drosophila genome 30 , 31 ., It has been proposed that these poised/stalled polII allow rapid responses of gene activation to environmental stimuli and developmental cues ., To gain a detailed view of RNA polII dynamics and gene expression , we adopted a previously established strategy 31 and categorized TSSs of genes into three classes: those with elongating polII ( 785 TSSs ) , stalled polII ( 685 TSSs ) or no polII ( 695 TSSs; Figure 5A ) ., Notably , stalled polII is detected in the TSS of Hsp70 gene ( CG18743 ) , which is the first defined gene with stalled polII 32 ., We find that the presence of elongating polII at the TSSs corresponds to genes within the top 50% expression levels whereas absence of polII at TSSs represents genes within the lowest 40% expression levels ( Figure 5B , upper and lower panels ) ., Interestingly , genes with stalled polII at their TSSs exhibit a broader range of expression levels ( Figure 5B , middle panel ) ., To infer the precise positions of RNA polII at different types of TSSs , we calculated the frequency of polII-immunocoprecipitated reads matched to the sense and the antisense strands of genes and binned these reads into 5-bp windows ( Figure 5C ) ., A similar approach has been previously employed to position nucleosomes surrounding TSS regions 33 ., By this method , we pinpoint stalled polII into a narrow region , centered at the +35 bp position ( Figure 5C , middle panel ) ., This location is identical to previous permanganate footprinting results , which localized open transcription bubbles within this region 31 ., In contrast , for genes with elongating polII , only 30∼40% of polII resides around the TSS , however , it resides at the +45 bp position ( Figure 5C , upper panel ) ., The Kolmogorov-Smirnov test confirms that both of the 5′ ends distribution and the 3′ ends distribution of polII-immunocoprecipitated reads between stalled polII group and elongating polII group are statistically significant ( 5′ end: p\u200a=\u200a2 . 3×10−3; 3′ end: p\u200a=\u200a1 . 7×10−4 ) ., This 10-bp difference of RNA polII position may reflect distinct pausing stages during the transition from transcription initiation to fully engaged elongation ., It may be used as a signature to predict the transcriptional activity of a gene ., To understand the relationship between RNA polII stalling and epigenetic regulation , we analyzed the distribution of chromatin modifications within 2-kb regions around different classes of TSSs ( Figure 5D ) ., Interestingly , polII-stalled TSSs are associated with a strong peak of HP1a but not other chromatin modifications ( Figure 5D , middle panel ) ., This echoes our finding that HP1a-mediated gene expression regulation is independent of other interrogated chromatin modifications and suggests that HP1a is not recruited here by H3K9me3 , but possibly rather by interaction with RNA polII ., Distinct to this profile , genes with elongating RNA polII show very low levels of HP1a at TSSs but high levels of H3K4me3 and H3K9ac downstream of TSSs ( Figure 5D , upper panel ) ., To further explore the overall effect of chromatin modification on gene expression , we clustered 7 , 826 Drosophila genes with known expression levels based on similarities of their epigenetic profiles around TSSs ( Figure 6A ) ., Interestingly , hierarchical clustering reveals six prominent gene clusters , each of which displays a characteristic gene expression profile and epigenetic signature around TSSs ., Cluster, ( a ) represents high-expressing genes with only high levels of RNA polII but no other epigenetic marks ., Gene ontology analysis indicates this cluster is enriched for genes involved in transcription regulation , alternative splicing and development ( Table S3 ) ., Cluster, ( b ) contains low-expressing/silent genes with medium levels of RNA polII and H3K27me3 but high levels of HP1a ., Cluster, ( c ) and, ( d ) consist of high-expressing genes with high levels of RNA polII , and high levels and medium levels of H3K9ac , respectively ., These clusters are enriched for housekeeping genes , related to ribosome functions ., Cluster, ( e ) represents low-expressing/silent genes with H3K4me3 , H3K27me3 and H3K9ac present at TSSs ., This cluster is enriched for genes involved in G-protein coupled receptors ., Cluster, ( f ) contains medium-expressing genes with medium levels of RNA polII and high levels of H3K9ac ., This cluster is enriched for oxidoreductases encoding genes ., The above data reveal strong correlations between histone codes surrounding TSSs and expression of genes with distinct types of biological functions in a whole organism context ., To further understand this correlation , we employed a four-layer artificial neural network ( ANN ) 34 to predict gene expression levels by quantitative values of chromatin modifications around TSSs ., With 50% of data allocated as a training set , we achieved 86 . 7% accuracy in the prediction of quantitative gene expression levels , which strongly suggests a causal relationship between TSS-located histone codes and gene expression ( Figure 6B ) ., Furthermore , we extracted weights for an individual “neuron” within the input layer after training , and identified H3K9ac downstream of TSSs and H3K27me3 surrounding TSSs as the two most critical factors determining the accuracy of target gene expression prediction ( Figure 6B ) ., To further narrow down the critical regions of these chromatin modifications in determining gene expression , we fed a neural network with averaged densities of chromatin modifications in nineteen 50-bp windows around TSSs ( −450 bp∼+450 bp ) ., With overall 87 . 9% accuracy , we find the presence of RNA polII and H3K9ac downstream of TSSs ( 0∼450 bp ) are remarkable positive predictors of gene expression ( Figure 6C ) ., In addition , H3K4me3 and H3K27me3 around TSSs ( −100 bp∼+100 bp ) are also pivotal to gene expression prediction , which echoes the opposing functions of Trithorax group proteins ( TrxG ) and Polycomb group proteins ( PcG ) in regulating gene expression ., In searching for chromatin modifications at exon-intron and intron-exon junctions , we discovered that RNA polII is unevenly distributed at splicing junctions ., Specifically , RNA polII is concentrated within exons with a prominent peak centered at −90 bp upstream of exon-intron junctions ( Figure 7A ) ., By contrast , RNA polII scores drastically drop to the background levels once the transcription machinery goes into introns ., At intron-exon junctions , RNA polII is devoid from the region centered at −30 bp upstream of the junctions but accumulated on the exon sides ( Figure 7B ) ., This distribution profile of RNA polII mimics the nucleosome densities surrounding the exon-intron and intron-exon junctions in Drosophila 35 , implicating an influence of chromatin structure on polII elongation ., Our results support the hypothesis that nucleosomes enriched in exons function as ‘speed bumps’ at splicing junctions to slow the rate of RNA polII elongation in favor of RNA splicing 35 ., To gain further insight on the uneven distribution of polII at splicing sites , we calculated the numbers of exons and splicing variants for genes manifesting the polII slowing in exons ( 254 genes in total ) and compared to those of remaining genes ., As expected , those genes with polII slowing in exons have 2 . 07 annotated splicing variants on average , which are significantly more than other Drosophila genes ( Figure 7C and 7D ) ., Transposons occupy approximately one third of the Drosophila genome 36 ., In the everlasting competition with these parasitic DNA , flies have evolved defensive mechanisms to regulate transposition of transposons ., Recent discoveries indicate that transposon mobilization is controlled at two levels: transcriptional silencing by heterochromatin formation and post-transcriptional silencing via small RNA-based transposon RNA degradation ., Our finding that heterochromatic marks H3K9me3 and HP1a are enriched in transposons indicates that a general scheme of transposon silencing in Drosophila is packaging the transposon-rich sequences into heterochromatin ., Within heterochromatin , methyltransferase SU ( VAR ) 3–9 sets the H3K9me3 mark , which recruits HP1a to initiate the heterochromatin formation 19 , 20 , 37 ., In line with this view , we observed significant correlation between H3K9me3- and HP1a-levels in transposons ., The most striking correlation is between H3K9me3 and H3K27me3 , which suggests the possible co-localization of these two silencing marks in transposons ., The co-localization of H3K9me3 and H3K27me3 has been observed in the chromocenter core regions on Drosophila polytene chromosomes 38 ., Since no known enzyme can methylate H3 to trimethylation states for both Lysine 9 and Lysine 27 , it would be interesting to investigate in the future whether SU ( VAR ) 3–9 and E ( Z ) function synergistically to silence transposons by heterochromatin formation ., Recently , RNA-based transposon silencing mechanisms have been uncovered ., In Drosophila , posttranscriptional silencing pathways mediated by endo-siRNAs and piRNAs are involved in transposon silencing in the soma and germline , respectively 39 ., A common scheme in these pathways is that transcription from transposon-rich regions is employed by host cells to generate defensive small RNAs , which in turn are utilized to degrade transposon transcripts ., The presence of transposon-derived small RNAs dictates that transcriptional activity must exist in transposon sequences ., In support of this idea , we find euchromatic mark H3K4me3 is indeed prevalent in some but not all transposons ., This observation also echoes our previous finding that a transposon-rich region in the subtelomere of the right arm of chromosome 3 ( 3R-TAS ) contains both heterochromatic ( H3K9me2 , H3K9me3 and HP1a ) and euchromatic ( H3K4me2 , H3K4me3 and H3K9ac ) marks 16 ., Interestingly , this well-defined heterochromatin region is transcriptionally competent , giving rise to a panel of piRNAs and permissive to transcriptional activities from a reporter gene inserted in this region ., Therefore , it is conceivable that many transposons and repetitive sequences with similar epigenetic states are also transcriptionally active , albeit at low levels ., Our analysis of protein-coding genes reveals three salient features of chromatin modifications , which reflect distinct histone codes , in all transcriptionally active genes ., First , their transcribed regions are all enriched with H3K9ac ., This is consistent with the notion that H3K9ac specifically associates with transcriptional activity and can spread over the whole gene body ., Second , the TSSs and TxEnds of active genes are further enriched with H3K4me3 ( Figure 3F ) , which is consistent with the enrichment of H3K4me3 around TSSs of transcriptionally active genes in mammalian genomes ., In addition , the drastic enrichment of H3K4me3 and H3K9ac in the 5′ transcribed region ( 5′TR ) and the sharp decrease at TSSs to become severely under-represented in promoter regions ( Figure 3F and 3G ) is also observed for H3K4me3 in the human genome ., In contrast to H3K4me3 and H3K9ac , H3K9me3 shows the opposite pattern in the promoter-5′TR; whereas H3K27me3 is underrepresented in both promoter and 5′TR of active genes ( Figure 3H and 3I ) ., These striking patterns of histone code around the TSS collectively represent an epigenetic signature for all actively transcribed genes ., The robustness of this signature corresponds nicely to the transcriptional activity of a gene ., It indicates that , in active genes , the promoter region is highly enriched in HP1a , the 5′TR is highly euchromat

Introduction, Results, Discussion, Materials and Methods

Epigenetic research has been focused on cell-type-specific regulation; less is known about common features of epigenetic programming shared by diverse cell types within an organism ., Here , we report a modified method for chromatin immunoprecipitation and deep sequencing ( ChIP–Seq ) and its use to construct a high-resolution map of the Drosophila melanogaster key histone marks , heterochromatin protein 1a ( HP1a ) and RNA polymerase II ( polII ) ., These factors are mapped at 50-bp resolution genome-wide and at 5-bp resolution for regulatory sequences of genes , which reveals fundamental features of chromatin modification landscape shared by major adult Drosophila cell types: the enrichment of both heterochromatic and euchromatic marks in transposons and repetitive sequences , the accumulation of HP1a at transcription start sites with stalled polII , the signatures of histone code and polII level/position around the transcriptional start sites that predict both the mRNA level and functionality of genes , and the enrichment of elongating polII within exons at splicing junctions ., These features , likely conserved among diverse epigenomes , reveal general strategies for chromatin modifications .

Just as a genome sequence map is indispensible to genetic studies , an epigenome map is crucial for epigenetic research ., This is especially true for a sophisticated genetic model such as Drosophila melanogaster , where the wealth of information on genetics and developmental biology awaits systematic epigenetic interpretation on a whole-genome scale ., In this manuscript , we report a high-resolution map of key chromatin modifications in the Drosophila genome constructed by the ChIP–Seq approach ., This map is derived from all cell types in the adult Drosophila weighted by their natural abundance ., It contains key histone marks , HP1a and RNA polymerase II , mapped at 50-bp resolution throughout the genome and at 5-bp resolution for regulatory sequences of genes ., It reveals striking features of chromatin modification and transcriptional regulation shared by major adult Drosophila cell types ., We anticipate that this map and the salient chromatin modification landscapes revealed by this map should have broad utility to the fields of epigenetics , developmental biology , and stem cell biology .

biology

journal.ppat.1003899

Electron Tomography of HIV-1 Infection in Gut-Associated Lymphoid Tissue

HIV-1 remains a significant public health concern with over 33 million people infected world-wide 1 ., Most HIV-1 transmissions occur across an epithelial barrier , resulting in generation of a founder population within the mucosa , viral dissemination to lymphatic tissue , and exponential viral replication throughout the lymphatic system 2 ., These events result in depletion of most CD4-positive T cells in mucosal compartments , and establishment of a reservoir of resting cells with integrated provirus that is not susceptible to antiretroviral therapy ., In the absence of therapy , progressive immune system collapse and progression towards AIDS ensue in most infected persons ., Accumulating evidence indicates that both acute and chronic HIV-1 infection profoundly affect the gastrointestinal ( GI ) tract 3 , 4 ., Studies of SIV infection in non-human primates demonstrated that intestinal CD4 T cell depletion occurs within days , even before T cell depletion can be detected in the peripheral blood or lymph nodes 5; similar events occur in HIV-1–infected humans 2 , 6 ., Several features of the GI tract facilitate its susceptibility to HIV-1 infection:, ( i ) the GI mucosa includes high levels of pro-inflammatory , HIV-1–stimulatory cytokines produced by exposure to antigens in the external environment ,, ( ii ) a dense clustering of cells that facilitates cell-to-cell transmission , and, ( iii ) a majority of the activated memory T cells expressing CD4 and CCR5 that serve as the preferred target cells for HIV-1 infection 7 , 8 ., Indeed , the gut-associated lymphoid tissue ( GALT ) harbors the greatest concentration of potential HIV-1 target cells in the human body 9; >50% of CD4 T cells from the lamina propria in the lower GI tract are destroyed during acute HIV-1 infection , and early infection of the GALT is believed to be central to chronic HIV-1 infection and disease progression 10 , 11 ., Furthermore , the presence of CD4 and CD8 T cells , dendritic cells , and macrophages in the GALT make this tissue an integral site for HIV-mediated immune depletion ., Mouse models with humanized immune systems are emerging as a tractable , cost-effective means by which to study HIV-1 infection in mucosal lymphoid tissue 12 ., One such model , humanized bone marrow/liver/thymus ( BLT ) mice , are individually created by transferring human fetal thymic and liver organoid tissues , along with CD34-positive human stem cells , into immunocompromised mice ., BLT mice reconstitute significant levels of human lymphoid immune cells; e . g . , T and B cells , monocytes , dendritic cells and macrophages in peripheral blood and organs including the GI tract 13 , 14 ., Important aspects of human HIV-1 infection are recapitulated in this system , including T cell depletion in the gut and peripheral blood , and both systemic and mucosal virus transmission during the course of the disease 15 , 16 ., Furthermore , BLT mice exhibit high levels of human immune cell engraftment at mucosal sites and significant antigen specific immune responses by multiple cell types 17 , 18 ., Electron microscopy ( EM ) was instrumental in the original identification of HIV-1 19 , 20 ., Subsequently , diagnostic EM analyses of biopsies from infected patients revealed important aspects of HIV-1 transmission in humans at varying stages of infection , from early acute disease to AIDS progression 21 ., More recently , 3-D EM , specifically electron tomography ( ET ) , cryoelectron tomography ( cryoET ) and ion-abrasion scanning electron microscopy , have been applied at increasingly higher resolutions , facilitating improved understanding of HIV-1 virion structure 22–24 , virus budding 25 , 26 , and virus transmission between immune cells 27 , 28 ., 3-D EM of isolated virions and infected cells can provide a detailed understanding of HIV-1 ultrastructure and transmission between cultured cells , but does not address the complex cellular environment found in mucosal tissues within an organism experiencing an active infection ., Here we used ET to analyze GALT from humanized HIV-1–infected BLT mice in order to visualize HIV-1 infection in mucosal tissues in 3-D at ultrastructural resolution ., These analyses allowed us to localize infected substructures within intestinal tissue , classify virions as mature or immature , identify infected cells , visualize structures we interpreted as components of the host cell machinery involved in viral budding , and assess the propensity for viral spread by cell-to-cell versus free virus routes of infection ., In parallel studies , we used immunofluorescence ( IF ) and immuno-electron microscopy ( immunoEM ) to verify the identities of viral particles , locations of infected tissue , and to distinguish human from murine and infected from uninfected cells ., Human hematopoietic cells derived from transplanted human stem cells have been shown to repopulate the GALT of BLT mice , and HIV-1 infection of these mice results in CD4 T cell depletion , initially in GALT and then systemically 13 , 16 ., Following established protocols 13 , BLT mice were infected with HIV-1 approximately 20 weeks after transfer of human immune tissues and cells , using only mice that met the following criteria for adequate human immune reconstitution: >25% of peripheral blood cells were within a lymphocyte gate on forward-versus-side scatter plots; >50% of cells in the lymphocyte gate were human ( human CD45+/mouse CD45− ) ; and >40% of human cells in the lymphocyte gate were T cells ( human CD3+ ) ., Ten to twenty weeks post infection , mice were sacrificed and segments of small intestine and colon were excised ., IF was used to survey locations of HIV-1–infected cells in GALT ( Figure 1A , B ) ., Following infection with HIV-1 , human CD4 T cells were depleted from the lamina propria ( Figure 1B ) , as previously reported 13 , 16 ., Staining for the p24 capsid protein of HIV-1 localized primarily in CD4+ cells in regions near the crypts ( Figure 1B , inset ) , which harbor significant populations of immune cells and multipotent stem cells 29 ., No evidence of human cells or HIV-1 infection was found in non-humanized infected controls ( data not shown ) ., We next analyzed GALT samples in parallel by ET and immunoEM/ET ., Tomography of frozen hydrated tissue samples by cryoET was not possible because the samples were too thick for imaging without sectioning and were infectious biohazards ., We therefore imaged fixed and sectioned samples , either positively-stained plastic-embedded or negatively-stained methylcellulose-embedded sections ., For ET alone , preservation quality was improved by lightly fixing HIV-1–infected tissue with aldehydes and then further processing them by high-pressure freezing and freeze substitution fixation 30 ., This “hybrid” fixation method allowed for safe handling of infectious material and obviated the most structurally damaging steps of traditional chemical fixation 31 , yielding well-preserved positively-stained samples ., Tomograms were reconstructed from 200 nm or 300 nm sections , often in montaged serial sections of volumes up to 6 . 1 µm×6 . 1 µm×1 . 2 µm ., Although these samples could not be used for immunoEM because antibody epitopes are rarely accessible in epoxy-embedded , positively-stained samples 32 , analogous GALT samples generated from the same animal were prepared for immunoEM/ET as negatively-stained methylcellulose-embedded sections 33 ., Measurements of virions and other structures reflected proportional thinning typical of plastic-embedded and negatively-stained samples 34 ., Consequently most structures were ∼30% smaller than counterparts from cryoEM studies or virions in solution or in cultured cells 22–24 , 35 , 36 ., ET surveys of HIV-1–infected BLT mouse GALT revealed budding virions ( Figure 2A , B; Figure S1A ) and free mature and immature particles ( Figure 1C , Figure 2C–D , Figure S1B ) ., Virions were detected in all HIV-1–infected mice , while none were found in mock-infected controls ( data not shown ) ., The virions were verified as HIV-1 using antibodies against HIV-1 p24 and the envelope spike ( Figure 2E , F ) ., Virions were imaged in tissue at all stages of egress , from early plasma membrane Gag assembly to nearly completed buds and fully mature , free HIV-1 ( Figure S2 ) ., Budding profiles and immature free virions were distinguished by core structures that exhibited radial layers and often appeared as an incomplete internal sphere ( a “C” shape in projection ) 24 , 26 ., Mature HIV-1 particles were distinguished from immature particles by the collapse of their cores into a variety of conical shapes , typically “bullet-shaped” cones but often cylinders or ellipsoids 23 , 37 ( Figure S2 ) ., Although envelope spikes on HIV-1 and SIV can be distinguished in positively-stained samples 38 , we observed few projections emanating from virion surfaces , consistent with biochemical and cryoET studies of purified HIV-1 virions that demonstrated a low number of envelope spikes: an average of ∼14 ( ranging from 4–35 ) per virus particle 39 , 40 ., After establishing that HIV-1 could be identified in infected BLT GALT by ET and immunoEM , we surveyed GALT samples to determine locations of infection ., Plastic-embedded sections of small intestine ( jejunum and ileum ) and large intestine ( colon ) were examined to find HIV-1 and infected cells , which were identified by budding profiles at their surfaces ., Within a given animal , the extent of infection and the distribution of virions were similar between the small and large intestine ., However , virions were found in differing amounts amongst sub-structures in the intestinal mucosa ., The largest populations of HIV-1 virions and infected cells identified by EM were located in crypts ( Figure 1A , C ) , consistent with IF ( Figure 1B ) ., Approximately one in ten crypts showed evidence of HIV-1 infection ., The mucosal region surrounding the villus base and the crypts contained few free virions or infected cells ( ∼1 in >100 ) ; when present , infected cells were often near a capillary or venule ( Figure S1A ) ., The numbers of free virions and infected cells in the lamina propria were less than in the crypts ( Figure S1B ) ., Typically , infected lamina propria were in villi continuous with infected crypts ., Few infected cells or virions were found in the smooth muscle layer surrounding the intestine ., In addition , free virions were rarely found in blood vessels because even the high viral loads of the HIV-1–infected BLT mice from which the samples were derived ( up to 126 , 000/mL in peripheral blood ) translated to only ∼1×10−7 virions/µm3 ., Thus at the scale of individual EM images or even large-format tomograms , HIV-1 virions would be rarely seen , and our imaging of >50 blood vessels contained within tomograms yielded only two examples of free virions ( data not shown ) ., To identify potential human target cells of HIV-1 infection , we conducted immunoEM ( Figure S3A–D ) using antibodies specific for human proteins ., Human CD4 localized primarily to the plasma membrane in uninfected cells ( Figure S3A ) , but we found extensive CD4 labeling in the endoplasmic reticulum ( ER ) of CD4-positive cells with budding virions or nearby free virions ( Figure S3B ) , correlating with the finding that HIV-1 Vpu induces cell surface CD4 to redistribute to the ER to avoid surface retention of newly-forming virions 41 ., Double labeling with antibodies against HIV-1 Nef and human CD4 ( Figure S3C ) or class I human leukocyte antigen ( HLA ) and human CD4 ( Figure S3D ) confirmed that cells exhibiting a predominantly ER localization of CD4 were human cells infected with HIV-1 ., No instances of Nef expression were found in uninfected or non-human cells ( data not shown ) , which served as an internal control for the specific of the antibodies and further validated the BLT model of HIV-1 infection ., Tomograms of immature virions derived from negatively-stained infected tissue revealed detailed structural information ., With the exception of the widening of lipid bilayer membranes , presumably caused by obligatory light fixation associated with this method , the overall architecture of the Gag shell in immature virions conformed to known properties of HIV-1 determined from studies of viruses isolated from cultured cells 22 , 24 , 35 , 36 , 42 ( Figure 3; Figure S4 ) ., Indeed , the immature virions in our tissue samples ( Figure 3 , S4A , B ) exhibited features observed in cryoET analyses of purified frozen hydrated HIV-1 22 , 24 ( Figure S4C ) ; e . g . , individual layers of the Gag shell , including the hexagonal lattice of the capsid ( CA ) portion ( Figure 3 ) ., The symmetry of the CA layer was confirmed by hexagonal features in the Fourier transforms of immature virions , but not in transforms of adjacent cytoplasm ( Figure 3B; Figure S4A ) ., More than 50 crypts of Lieberkühn were imaged in the course of this study ., In the ∼10% of crypts that were infected , HIV-1 virions were found primarily in pools within dilated regions of intercellular spaces ( Figure 1C; Figure 4; Figure S5; Movie S1 ) ., Pools were defined as a population of virions within an intercellular space that was continuous within a given 3-D volume ., Multiple intercellular spaces could be present within the volume , but unless the spaces were visually continuous , virions within them were regarded as separate pools ( Figure 4B , C ) ., The numbers of free virions in intercellular pools ranged from 5 to >200 ., In single-frame tomograms ( 3 . 2 µm×3 . 2 µm×200 nm ) , most pools contained 10–40 particles ., Larger pools were observed in serial-section reconstructions encompassing greater tissue volumes ., In longitudinal sections of crypts , most pools were found between the base and middle ., Infected human immune cells , identified by the presence of budding virions , were often found near virion pools ., Virions within a given pool were distinguished as mature or immature based on the presence of a cone-shaped core in mature particles and radial Gag layers in immature particles ( Figure S2 ) ., The numbers of mature and immature particles in intercellular pools were quantified within reconstructed volumes of infected crypts ., Pools could be classified as either “mostly mature” or “mostly immature” ( Figure S5A ) ., Of >100 pools containing many hundreds of virions , approximately 90% of pools were classified as mostly mature and 10% were mostly immature ., Potential HIV-1 target cells and pools of virions were plentiful in GALT , particularly in crypts , thus it was not always possible to determine from which cell a particular virion population originated ., In order to quantify virions from a particular cell and infer temporal data with respect to virion pools , we imaged regions of the intestinal smooth muscle layer ( Figure 1A ) , which contains few HIV-1 target cells ., Figure S5B shows an HIV-1-infected cell in the smooth muscle ., The surface of this cell exhibited several HIV-1 budding profiles , and groups of free virions were located both in close proximity to and at varying distances from it ., There were no other infected cells within several microns , thus we could be confident that nearby free virions had originated from that cell ., We found that 62% of virions ( n\u200a=\u200a16 ) in immediate proximity ( ≤0 . 5 µm ) to the cell were immature , while 73–75% of virions in groups located 0 . 8 µm ( n\u200a=\u200a15 ) and 1 . 3 µm ( n\u200a=\u200a32 ) away were mature ., Of >100 virion pools that were imaged , most were in obvious extracellular spaces ., Some pools ( ∼5% ) appeared to be intracellular , but were revealed by ET to be connected to the extracellular space by narrow channels that averaged ∼27 nm in width ( range\u200a=\u200a23–32 nm; n\u200a=\u200a6 ) ( Figure 4D–E ) and contained 2–20 mature virions ., A few of the budding regions were large enough that potential continuities with the plasma membrane were outside of the reconstructed volume ., The presence of seemingly intracellular virion pools connected to microchannels could identify the cell as an infected macrophage , a cell type in which internal virus-containing compartments were proposed to represent specialized domains of the plasma membrane that were sequestered intracellularly 43 , 44 and/or endosomal compartments 45 , 46 ., ET surveys of HIV-1 infected GALT showed evidence of virological synapses for direct cell-to-cell virus transmission , a route of HIV-1 transmission within tissues whereby a virus buds from an infected cell and directly contacts and infects an adjacent uninfected cell 47 ., Formation of a virological synapse results from interaction of gp120 on an infected cell with its receptors on a target and also involves other host proteins such as LFA-1 and ICAM proteins on the surfaces of both the donor and target cells 48 , 49 ., A large format reconstruction ( 2×3-frame montage ) of GALT revealed an HIV-1–infected cell , likely a dendritic cell or macrophage based on the convoluted processes intercalating between neighboring cells ( Figure 5A; Movie S2 ) ., A presumptive virological synapse was visualized as a region of contact between a budding virion and an adjacent cell ( Figure 5B; Movie S2 ) ., Although this positively-stained sample could not be examined by immunoEM , we found similar features in negatively-stained samples that labeled with antibodies against LFA-1 and ICAM-1 ( Figure 5C , D ) , supporting the identification of these regions as virological synapses ., In another example , an infected cell that showed numerous budding profiles included one that closely approached the surface of an adjacent cell although still attached to its host cell via a ∼50 nm neck ( Figure S6A ) ., The surface region of the cell proximal to the approaching bud was denser than surrounding surface regions and extended toward the bud ., In a third example , a budding profile from an infected cell appeared to project into an invagination in the plasma membrane of an adjacent cell ( Figure S6B , C ) ., Tomographic views through the volume containing this region showed the boundaries of the invagination followed the contours of the budding profile ( Figure S6C ) , suggesting a dynamic response to the approaching nascent virion ., By reconstructing a large 3-D volume of infected tissue , we could address whether direct cell-to-cell transmission was an obligatory means of virion transfer between two adjacent cells ., Movie S1 shows a 1 . 4 µm×2 . 9 µm×1 . 2 µm tomogram in which the outlines of two adjacent cells were distinguished ., Both cells were identified as infected by the presence of budding virions and were therefore HIV-1 targets ., A region resembling a virological synapse was not observed in the reconstructed volume , however a large accumulation of free mature virions were present in the space between the cells , suggesting that direct cell transfer is not a required mechanism of HIV-1 transmission between closely apposed infected cells ., The lack of an observed virological synapse in such cases could be the consequence of CD4 down-regulation in the infected cells ., However the existence of natural recombinant HIV-1 strains , which could result from infection by one HIV-1 strain of a cell already infected with a different viral strain 50 , suggests that residual CD4 remaining at an infected cell surface can allow for infection via free virus or direct cell-to-cell transfer ., The large number of budding virions within BLT GALT tomograms offered the opportunity to characterize structural aspects of HIV-1 budding in infected tissue ( Figure S7 ) ., Actin filaments were often found near forming buds ( Figure S7A ) similar to those previously observed at HIV-1 budding sites in cultured cells 25 ., Budding profiles exhibited varying lengths of necks , including some with no neck ( Figure 3C , D; Figure S7B ) ., In the colon , early budding virions without necks were often observed forming from surfaces that were not obviously plasma membrane ., However , serial-section tomography revealed that these domains were usually continuous with the plasma membrane proper , indicating that they were convoluted regions of the cell surface and not distinct cytoplasmic compartments ., Some budding virions exhibited necks with 50–80 nm lengths and varying widths ( Figure S7C ) , with narrower necks likely representing those approaching scission ., Virions were also observed budding at the ends of extremely long cellular projections ( Figure 2A , B ) that were likely filopodia extending from dendritic cells , as observed in culture 27 , 51 ., ET analyses of HIV-1 budding in cultured cells revealed a subset of RNA-free immature virions with a novel “thinner” Gag lattice lacking the nucleocapsid-RNA layer , which were suggested to represent aberrant , noninfectious virions resulting from premature activation of HIV-1 protease 25 ., Using our measuring convention , the previously-described thin Gag lattice 25 measured 9–10 nm ., Analysis of 100 free or budding immature virions from tissue samples yielded no examples with a thin ( 9–10 nm ) Gag lattice that lacked discernable RNA densities ., Instead , we found that the Gag lattice widths in all of the immature virions we surveyed ( n\u200a=\u200a100 ) within infected tissue was 14 . 6±0 . 8 nm ( Figure S4B , Figure S7D , E ) ; significantly different than the thin 9–10 nm Gag lattices previously described 25 ., In addition , there were no systematic structural differences in Gag lattices correlating with the type of budding profile: the Gag shell thicknesses measured in 30 long-necked and 30 neck-free buds were similar and presumptive RNA densities were present in all cases ( Figure S7D ) ., Release of HIV-1 virions from infected cells involves recruitment of the host endosomal sorting complexes required for transport ( ESCRT ) machinery to sites of virus assembly by the Gag polyprotein 52 ., These interactions culminate with the polymerization of ESCRT-III proteins , recruitment of vacuolar protein sorting-associated protein 4 ( VPS4 ) ATPase oligomers , fission of the cellular membrane attaching the virion to the host cell , and disassembly of the ESCRT machinery ., We used antibodies against ESCRT-III proteins , human charged multivesicular body proteins ( hCHMPs ) 1B and 2A , and ALG2-interacting protein X ( ALIX ) , an ESCRT adaptor protein that facilitates the transport of Gag to the cell membrane 53 and can mediate interactions between ESCRT-I and ESCRT-III complexes 54 , to detect components of the ESCRT pathway in infected tissue by immunoEM ., We found that hCHMP1B , hCHMP2A and hALIX localized predominantly to the neck regions of budding HIV-1 virions ( Figure 6A-C ) ., The labeling was specific , but sparse due to the small number of epitopes and their availability only at section surfaces ., At scission regions of budding virions in which the neck of the bud was greater than half the diameter of the bud , clusters of 4–6 spoke-like projections nearly 20 nm in length radiating from a centralized origin at the base of the budding virion were sometimes observed ( Figure 3C , D; Figure 6D; Figure S8A; Movie S3 ) ., As the larger neck diameter may define these buds as being at an initial stage of egress , these radial projections could represent components of the early portions of the ESCRT pathway such ESCRT-I or ALIX recruited by assembling HIV-1 Gag molecules ., Indeed , the size and shape of the structures approximate models for the ESCRT-I-II supercomplex determined by a combination of spectral techniques 55 ., By contrast , in tomograms of budding virions with narrower necks ( less than half the diameter of the bud itself ) , we observed parallel electron dense striations circumscribing the neck of the bud in both positively- and negatively-stained sections ( Figure 7A–E; Figure S8B , C; Movie S4 ) suggestive of ESCRT-III components polymerizing at membranes 56 , 57 ., Similar electron dense striations were detected at the necks of budding virions arrested at a late stage by expression of dominant-negative ESCRT-III or VPS4 proteins 58 ., In addition , budding profiles in positively-stained samples often showed 1–5 electron-dense “spots” in the neck or base of a bud ( Figure 7F , G; Movie S5 ) ., The spots were observed in over half of ∼50 budding profiles in which the diameter of the neck was half or less of the diameter of the budding virion; presumably a late stage of budding ., Available antibodies against VPS4 did not stain efficiently by immunoEM , however their interpretation as VPS4 oligomers was consistent with fluorescence imaging showing recruitment of 2–5 VPS4 dodecamers to the sites of viral budding just prior to virion abscission 59 , 60 ., In addition , the size and relative shape of the putative VPS4 densities ( Figure 7G ) correlated with cryoEM reconstructions of VPS4 61 ., Many aspects of the pathologies related to HIV-1 infection , including immune cell death and tissue destruction , occur in GALT ., However , 3-D ultrastructural details of a natural GALT infection were unknown because ET had not been applied to in vivo infection in GALT or other lymphatic tissues ., BLT humanized mice are an emerging model for studying HIV-1 infection , and BLT GALT maintains cellular architecture , cell-cell interactions , immune cell populations and signaling more accurately than cell culture infection models 12 ., As such , the BLT mouse system is a reliable model for structural studies of HIV-1 infection in a tissue environment ., In addition , the inclusion of human thymic tissue in BLT mice allows for T cell maturation in the context of human , rather than murine , MHC proteins; an aspect that is not present in humanized mouse model systems produced with human hematopoietic stem cells but without thymic tissue ., Dense areas of HIV-1–infected cells , including CD4 T cells , macrophages and dendritic cells , and free HIV-1 virions were found in crypts within BLT GALT by IF , ET and immunoEM ( Figure 1B , C ) ., Blood vessels were imaged in mice with a wide range of viral loads; however , we were unable to correlate the relative abundance of virions detected in GALT with the viral load measured in the blood ., In fact , only two examples of virions within blood vessels of BLT mice were detected as compared with hundreds of virions within mucosal tissue ., This finding is consistent with reports highlighting a discrepancy between blood viral load and HIV-1 levels in tissues 62 , 63 ., Thus analysis of HIV-1–infected tissues by methods such as ET may provide valuable information in addition to blood viral load measurements when evaluating treatment regimens ., Potentially relevant to infection and immune cell recognition mechanisms , large pools of free HIV-1 were found within infected GALT ( Figures 1C , Figure 4 , Figure S5 ) ., Although most pools contained mainly mature virions , some pools contained a majority of immature virions ( Figure S5A ) , a phenomenon not observed in EM studies of HIV-1 infection of cultured cells ., Pools of virions were usually found between cells , but also in compartments that appeared to reside within cells ., These compartments were often connected to the cell surface by microchannels 20–30 µm in width ( Figure 4D ) ., These narrow channels likely undergo dynamic changes in morphology , as their width would be too narrow to accommodate passage of HIV-1 to the extracellular space ., We interpreted such channels as invaginations of the plasma membrane , consistent with reports that macrophages can assemble HIV-1 in intracellular virus-containing compartments created by internally sequestered plasma membrane 43 , 44 , 64 ., In infected tissue , we found that pools of HIV-1 virions located between two cells could contain mature or immature virions ( Figure S5A ) , whereas the intracellular pools connected by microchannels contained only mature virions ( Figure 4D ) ., One possibility for the difference in maturation states of inter- versus intracellular pools of HIV-1 is that intracellular virions connected to the extracellular space by microchannels are not subject to movement by interstitial fluid through intestinal tissue and could remain in a single location long enough to complete maturation , perhaps representing viral reservoirs that allow low levels of de novo infection to proceed in the presence of anti-retroviral therapy and/or antibodies 65 ., Although the discovery of virion pools suggested that infection by free virus could occur within infected tissue , we also found evidence of direct cell-to-cell transmission of HIV-1 in infected GALT ( Figure 5; Movie S2 ) ., The virological synapse is a mechanism of cell-to-cell transmission in which juxtaposition of an infected and uninfected cell promotes infection by directing viral assembly , budding , maturation , and fusion machinery to discrete locations of cellular contact between cells 47 ., In a large 3-D reconstruction of two adjacent HIV-1–infectable target cells ( Movie S1 ) , we found a large pool of mature virions but no evidence for a virological synapse , suggesting that formation of virion pools and infection by free virus can occur even when adjacent cells are both infectable by HIV-1 , or had been infectable prior to down-regulation of CD4 ., In addition , this result validated our frequent finding of large pools of free virions in HIV-1–infected tissue , demonstrating that this phenomenon was not necessarily the consequence of the juxtaposition of a human infected cell and a murine cell , as may occur in BLT GALT ., EM studies of HIV-1 virions produced in cultured cells suggested that maturation is a rapid process , because intermediate maturation states were not detected and because virions found near cells were predominantly mature 66 ., However , our finding of pools containing immature virions in proximity to infected cells in tissue suggested maturation dynamics and/or virion diffusion properties differ between cells organized within tissue versus those cultured in vitro ., In addition , we never found examples of RNA-negative budding virions with a thin Gag lattice in tissue samples , as had been observed in ∼18% of immature particles in cryoET analyses of HIV-1 produced in cultured cells 25 ., Thus , higher numbers of aberrant particles and of exclusively mature virions in close proximity to producer cells could be artifacts of producing virions in cultured cells , suggesting that the BLT model of in vivo infection more accurately recapitulates the HIV-1 lifecycle than cell culture models ., Although ET relies on fixed tissue and cannot directly recapitulate virion dynamics in live cells , our studies provided a glimpse into temporal aspects of HIV-1 maturation ., We determined that an isolated infected cell within a large tissue volume was the sole producer of several populations of imaged virions located at varying distances from the cell ., This allowed us to determine that a single infected cell can produce at least 63 viruses ( the number of virions in the three pools in Figure S5B ) ., The total number of virions produced per cell is likely far larger , as regions above and below the cell were not represented in the reconstruction ., Using a predicted rate of interstitial fluid movement in intestinal tissue of 0 . 1–2 µm/sec 67 , a virion would travel 2 µm in 1–20 sec , indicating that maturation could occur just seconds after release from an infected cell ., This argues that , in tissue , virions found ∼2 µm away from a producer cell budded only seconds earlier , supporting an assumption of rapid virus maturation ., Furthermore , our finding of mostly immature virion pools in close proximity to the infected cell and mostly mature virion pools further away from the cell ( Figure S5B ) is consistent with synchronous release and subsequent maturation of HIV-1 ., The trigger ( s ) for and/or block ( s ) to maturation that could promote synchronized virus maturation in tissue could include proximity to an infected p

Introduction, Results, Discussion, Materials and Methods

Critical aspects of HIV-1 infection occur in mucosal tissues , particularly in the gut , which contains large numbers of HIV-1 target cells that are depleted early in infection ., We used electron tomography ( ET ) to image HIV-1 in gut-associated lymphoid tissue ( GALT ) of HIV-1–infected humanized mice , the first three-dimensional ultrastructural examination of HIV-1 infection in vivo ., Human immune cells were successfully engrafted in the mice , and following infection with HIV-1 , human T cells were reduced in GALT ., Virions were found by ET at all stages of egress , including budding immature virions and free mature and immature viruses ., Immuno-electron microscopy verified the virions were HIV-1 and showed CD4 sequestration in the endoplasmic reticulum of infected cells ., Observation of HIV-1 in infected GALT tissue revealed that most HIV-1–infected cells , identified by immunolabeling and/or the presence of budding virions , were localized to intestinal crypts with pools of free virions concentrated in spaces between cells ., Fewer infected cells were found in mucosal regions and the lamina propria ., The preservation quality of reconstructed tissue volumes allowed details of budding virions , including structures interpreted as host-encoded scission machinery , to be resolved ., Although HIV-1 virions released from infected cultured cells have been described as exclusively mature , we found pools of both immature and mature free virions within infected tissue ., The pools could be classified as containing either mostly mature or mostly immature particles , and analyses of their proximities to the cell of origin supported a model of semi-synchronous waves of virion release ., In addition to HIV-1 transmission by pools of free virus , we found evidence of transmission via virological synapses ., Three-dimensional EM imaging of an active infection within tissue revealed important differences between cultured cell and tissue infection models and furthered the ultrastructural understanding of HIV-1 transmission within lymphoid tissue .

HIV/AIDS remains a global public health problem with over 33 million people infected worldwide ., High-resolution imaging of infected tissues by three-dimensional electron microscopy can reveal details of the structure of HIV-1 , the virus that causes AIDS , how it infects cells , and how and where the virus accumulates within different tissue sub-structures ., Three-dimensional electron microscopy had previously only been performed to image infected cultured cells or purified virus ., Here we used three-dimensional electron microscopy to examine an active infection in the gastrointestinal tract of HIV-1–infected mice with humanized immune systems , allowing visualization of the interplay between the virus and host immune cells ., Recapitulating the course of infection in humans , immune cells were depleted in infected humanized mouse gut-associated lymphoid tissue , and individual HIV-1 particles were detected as they budded from host cells and accumulated in pools between cells ., HIV-1 was mapped to different substructures and cell types within the gut , and free virions were found to accumulate in pools between cells and also to infect adjacent cells via regions of cell-to-cell contact called virological synapses ., Our three-dimensional imaging of an HIV-1 infection in tissue uncovered differences between cultured cell and tissue models of HIV-1 infection and therefore furthered our understanding of HIV-1/AIDS as a disease of mucosal tissues .

medicine, immune cells, viral classification, immunology, host-pathogen interaction, microbiology, lymphoid organs, retroviruses, animal models, immunodeficiency viruses, model organisms, animal models of infection, infectious diseases, viral immune evasion, hiv, t cells, biology, mouse, immune system, virology, hiv clinical manifestations, viral diseases

journal.pcbi.1003366

Designing Molecular Dynamics Simulations to Shift Populations of the Conformational States of Calmodulin

Protein behavior in solution may be manipulated and controlled through tailored structural perturbations 1 and rational control of the solution conditions 2 http://www . pnas . org/content/109/50/E3454 . full . pdfhtml ., In the living cell , proteins adapt to particular subcellular compartments which pose different environmental variables such as pH and ionic strength ( IS ) , adapting their biophysical characteristics to tolerate pH fluctuations that are caused by cellular function 3 ., Furthermore , proteins interact with many other biological macromolecules while they are transferred from one compartment to another , with subtle control over protonation and pK changes upon binding to other proteins and ligands 4 , 5 ., Interactions with the environment and other molecules are closely related to local anisotropy and dynamical heterogeneity of proteins 6 ., The dynamics may be electrostatically guided , perhaps through long-range electrostatic interactions that select and bring interacting partners together , steering the protein to alternative conformations 7 ., The main perturbation effect of the long-range electrostatic interactions is manifested on the acidic/basic groups in the protein which can be charged or neutral in relation to their conformation dependent pKa values 8 ., Interacting with other molecules and changes in the environmental variables such as subcellular localization can induce shifts in ionization states of charged groups on a protein by proton uptake/release ., Such changes facilitate the protein to span a large conformational space and enable it to participate in diverse interaction scenarios ., Any in depth understanding developed through studying the conformational changes in proteins induced by shifts in the charge states of select amino acids would contribute to our knowledge base on diverse functionality observed in promiscuous proteins 9 ., In this study , we focus on the conformation-related effects of introducing perturbations on charged group ( s ) of calmodulin ( CaM ) ., CaM is a notorious example among proteins having the ability to change conformation upon binding to diverse ligands 10 , 11 ., It was shown that negatively charged side chains in calcium loaded CaM ( Ca2+-CaM ) are attracted to positively charged residues in many of its targets 12 ., Another study showed that Ca2+-CaM changes conformation when introduced to a solvent at low pH and low ionic strength 13 ., Also , it was proposed that electrostatic interactions between acidic residues in CaM contribute to determining the most populated conformation under varying solution conditions 13 ., Previously , we have studied the conformational changes in Ca2+-CaM 14 , ferric binding protein 15 and a set of 25 proteins that display a variety of conformational motions upon ligand binding ( e . g . , shear , hinge , allosteric ) 16 using the perturbation response scanning method ., This coarse grained methodology is based on the assumption that the equilibrium fluctuations at a given local free energy minimum of the protein possess information on other viable conformations when an external force is applied 15–17 ., Our study on CaM determined key residues that lead to the experimentally observed conformational changes upon application of force in specific directions 14 ., Several different servers ( H++ 18 , propKa 2 . 0 19 , pKd 20 and PHEMTO 21 , 22 ) showed that the pKa of E31 value is upshifted; furthermore , the equivalent position in Calbindin was measured to have pKa of 6 . 5 23 ., In a follow-up study , we focused on residues with upshifted pKa values and we made a systematic study of the dynamics of Ca2+-CaM on time scales up to 200 ns for three separate initial configurations; extended form , compact form and extended structure with 10 protonated residues ( 9 acidic residues and a histidine ) 24 ., We found that Ca2+-CaM with 10 protonated residues undergoes a large conformational shift from the extended structure to a relatively compact form on the time scale of tens of nanoseconds ., The latter was compatible with other structures reported in a nuclear magnetic resonance ( NMR ) ensemble of CaM 25 ., Experimental work investigating dynamical behavior of Ca2+-CaM has shown that it occupies a number of hierarchical set of substates even in the crystal form 26 ., Dynamical information obtained from fluorescence resonance energy transfer ( FRET ) experiments measuring the distance distributions between labeled sites illustrate that at least two conformations exist in solution under physiological conditions 27 ., More recently , pseudo contact shifts and residual dipolar couplings of the C-terminal domain obtained using NMR 28 revealed neither the dumbbell shaped conformation observed in early crystal structures of the molecule 29 , nor the compact conformation determined later on 30 exist in significant proportions in solution ., Ca2+-CaM is identified as a protein which populates multiple conformations 28 , 31 ., A shift between the distribution of populations is induced by changing environmental conditions such as pH , Ca2+ concentrations and ionic strength 13 , 27 ., Each of these manipulated properties has effects on the charged groups of Ca2+-CaM ., The presence of multiple conformations is a physical property of Ca2+-CaM , and it is likely that the heterogeneity of structure is at least partially responsible for the ability of Ca2+-CaM to recognize diverse targets ., Squier and coworkers have suggested that association of the C-terminal domain of CaM with a target may disrupt a structurally important hydrogen bond involving the central linker , facilitating formation of a compact binding conformation of Ca2+-CaM 32 ., More recently , through rather benign mutations such as E47D , they have determined noninterfacial residues important for molecular recognition through indirect effects – an increase in fluctuations stabilizes the bound state 33 ., It was further hypothesized that pH and ionic strength dependent shifts in the populations of conformational substates result from changes in electrostatic interactions in the central linker 13 , 27 ., For example , the shift in favor of the more compact conformation at reduced pH may result from the loss of electrostatic interactions that serve as spacers at neutral pH . This hypothesis is corroborated by inspection of the proximity of side chains of glutamic and aspartic acid residues surrounding the hinge region in the compact Ca2+-CaM crystal structure 30 ., In this manuscript , we report extensive molecular dynamics ( MD ) simulations of fully solvated , extended and compact Ca2+-CaM under different perturbation scenarios , with focus on E31 ., We have previously shown that E31 is located in a unique position to manipulate the overall structure; it also has an upshifted pKa into the physiological range and there are several experiments implicating its involvement in signaling coordination between the two lobes ( see 14 and references cited therein ) ., Structural perturbations are introduced as either E31A mutation or its protonation ., We also perturb environmental factors such as pH and IS ., We analyze the structural dynamics through identifiers based on reduced degrees of freedom defined specifically for Ca2+-CaM ., Key events leading to or preventing conformational change are discussed ., We elaborate on the events occurring along the path sampled between different conformational states identified by MD simulations and we evaluate the effect of charge balance on the conformations ., The molecular mechanisms that lead to the observed effects , their relationship to the experimental data , and the consequences of the observations that enhance our understanding of the dynamics and function of Ca2+- CaM are outlined ., CaM consists of 148 amino acids made up of the N-lobe ( residues 1–68 ) , the C-lobe ( residues 92 to 148 ) and a linker which is helical in many , but not all , of the reported structures ., Each lobe in CaM has two helix-loop-helix ( EF-hand motif ) calcium binding sites connected by unstructured sequences ., Structured elements include helices I ( residues 5–17 ) , II ( residues 30–39 ) , III ( residues 46–54 ) , IV ( residues 69–73 ) , V ( residues 83–91 ) , VI ( residues 101–110 ) , VII ( residues 119–129 ) , and VIII ( residues 137–144 ) ., Ca2+ coordinating residues in each of the four EF-hands are D20-D22-D24-E31 in loop I , D56-D58-N60-E67 in loop II , D93-D95-N97-E104 in loop III , and D129-D131-D133-E140 in loop IV ., All MD simulations reported in this work include the four Ca2+ ions ., The existing X-ray structures of Ca2+-loaded , peptide free calmodulin ( Ca2+-CaM ) are either in an extended or a compact form ., There are many examples for the extended form in the protein data bank ( PDB ) and we utilize that with PDB code 3CLN whereby the coordinates of the first four and the last residue are not reported 29 ., The compact form is represented by the 1PRW coded structure 30 , and has a bent linker as do many ligand bound Ca+2-CaM conformations present in the PDB ., These particular structures have been determined at 2 . 2 and 1 . 7 Å resolution , respectively , and were both crystallized at low pH conditions in the range of 5–6 , by growth in water-organic mixture compounds ., We have previously reported the RMSD comparison for the overall structure as well as the N- and C-lobes of various x-ray structures , including 3CLN , 1PRW and five ligand bound forms 14 ., An ensemble of Ca2+-CaM structures have also been reported ( PDB code 2K0E ) 25 by using experimental NMR order parameters ( S2 ) together with interproton distances derived from nuclear Overhauser effects ( NOEs ) as restraints in MD simulations using RDC-refined solution structure of Ca2+-CaM ., The IS of the experimental setup is 10 mM and the pH is 7 ( the conditions in ref . 25 are the same as in ref . 34; personal communication ) ., The ensemble has 160 structures and reveals that Ca2+-CaM state samples multitude of conformations including , but not limited to , the compact and extended ones ., In particular , unlike in the X-ray structures , there also exist compact conformers where the linker is not bent , as we pointed out in our previous study 24 ., Throughout this work , an efficient approach to distinguish between the different conformations of CaM proves to be useful: We define two low resolution degrees of freedom projecting the 3N-dimensional conformational space into a visually tractable two-dimensional one ., These are the linker end-to-end distance ( l ) and torsion angle ( φ ) ., The former is defined as the distance between the Cα atoms of the two outermost residues of the linker , residues 69 and 91 ., The latter is the torsion angle defined by four points: the center of mass of the N-lobe ( residues 5 to 68 ) , linker beginning and end points ( Cα atoms of residues 69 and 91 ) , the center of mass of the C-lobe ( residues 92 to 147 ) ., These points are schematically shown in Figure 1 ., Six sets of simulations were performed with various initial starting conditions ., Simulations are summarized in Table 1; IS values reported correspond to the equilibrated box dimensions ., For some systems , we have performed independent runs to check the reproducibility of the results ., Each condition has at least 150 ns of total sampling time ., We prolong the simulation in case there are substantial changes in the relative positioning of the two lobes and/or the length of the linker , measured by the region sampled on the ( l , φ ) plane described in the previous subsection ., The details of each simulation are as follows; the label for each type of simulation is indicated in parentheses and will be used throughout the text: ( ) Initial coordinates are taken from the extended 3CLN pdb coded structure and all residues are assigned their standard protonation states to study the conformational dynamics of extended form in solution ., 45 Na+ and 30 Cl- ions are added to attain IS\u200a=\u200a150 mM at the physiological range ., There is a one MD run of 150 ns and an additional control run of 50 ns ., ( ) Initial coordinates are taken from the compact 1PRW and all residues are assigned their physiological protonation states to study the dynamics of compact structure in solution ., The system is neutralized by 15 Na+ ions ., Due to the smaller box dimensions formed for this more compact structure , this protocol leads to IS\u200a=\u200a161 mM at the physiological range ., There are two runs of 200 ns each for this system ., ( ) Starting from 3CLN and all residues having the same protonation states as in ( ) , the system is neutralized by 15 Na+ ions ., This leads to a low IS of 82 mM ., There is a on MD run of 400 ns and an additional control run of 50 ns ., ( ) Starting from 3CLN structure , only E31 is protonated ., The system is neutralized by 14 Na+ ions leading to IS\u200a=\u200a91 mM; there are two runs for this system , one of length 400 ns and the other of 150 ns ., ( ) Starting from 3CLN structure , E31A mutation is made ., The system is neutralized by 14 Na+ ions leading to IS\u200a=\u200a94 mM; there are three runs of 400 ns each for this system ., ( ) In all the previously listed simulations , residues are assigned charge states according to pH\u200a=\u200a7 . 4 using pKa values calculated and listed in ref ., 24 ., These systems are assumed to be at physiological pH . Acidic residues 11 , 31 , 67 , 84 , 93 , 104 , 122 , 133 and 140 , as well as H109 consistently have pKas shifted from their standard values to ∼5 . 5 ., In the system , these are protonated to mimic the low pH conditions ., The reader is referred to ref ., 24 for details on the calculation of pKa values ., There are two runs for this system , one of length 200 ns and the other of 100 ns ., The system is neutralized by 5 Na+ ions leading to IS\u200a=\u200a43 mM In addition , control runs of 100 ns duration have been carried out on the extended , low IS proteins , singly or doubly protonating other residues with upshifted pKa values ., These are labeled , , and are not separately listed in Table 1; they are neutralized by 14 , 13 and 13 Na+ ions , respectively ., We use the NAMD package to model the dynamics of the protein-water systems 35 ., The protein is soaked in a water box with at least 10 Å of water from all directions using VMD 1 . 8 . 7 program with solvate plug-in version 1 . 2 36 ., The CharmM22 force field parameters are used for the protein and water molecules 37 ., Water molecules are described by the TIP3P model ., Each system is neutralized by using VMD autoionize plug-in ., Long-range electrostatic interactions are calculated by the particle mesh Ewald sum method , with a cutoff distance of 12 Å and a switching function of 10 Å 38 ., RATTLE algorithm is utilized and a step size of 2 fs is used in the Verlet algorithm 39 ., Temperature control is carried out by Langevin dynamics with a damping coefficient of 5/ps ., Pressure control is attained by a Langevin piston ., Volumetric fluctuations are preset to be isotropic ., The system is run in the NPT ensemble at 1 atm and 310 K . Equilibration of the pressure is achieved within 2 ns ., The equilibrated box dimensions of each system are listed in Table 1 ., The coordinate sets are saved at 2 ps intervals for further analysis ., Starting from 3CLN which represents the extended Ca+2-CaM structure captured in most X-ray studies , we externally perturb the physiological conditions for which results were displayed in figure 2:, ( i ) We lower the IS while keeping the pH at 7 . 4; and, ( ii ) we lower the IS as well as reducing the pH to 5 . 0 24 ., These systems are labeled and , respectively ( Table 1 ) ., The regions sampled by are displayed in figure 3 ., This is a continuation of the MD simulations from our previous work 24 , where the run has now been extended from 200 ns to 400 ns ., The RMSD values of the subunits as well as the overall structure are shown in figure 3a ., The initial conformer is not stable in solution as confirmed by the protein RMSD change ., While the linker and the two lobes each display low intra-domain motions ( less than 3±1 Å RMSD ) , their relative orientations change substantially ( up to 13 Å in the overall RMSD ) ., When projected on the reduced degrees of freedom ( figure 3b ) , the trajectories clearly display the three separate sampled states: The two lobes initially point towards each other and within the first 25 ns , the N- and C-lobes complete a ca ., 120° torsional motion reaching state II which is then sampled for 195 ns after which a new state is reached ( III ) by a further torsional motion of 100° ., In the last 180 ns of the trajectory , state III is sampled ., Snapshots exemplifying these three distinct states are shown in figure 3c ., We note that a prompt move into region II also occurs in the supplementary 50 ns run ., All the structures that are sampled throughout the MD trajectory are compared with the experimental ones ., The protein spends the first 220 ns in intermediate states with linker length ( l>31 Å ) , and φ\u200a=\u200a−80° , 100° ., During the MD simulation , the initial ( X-ray derived ) structure 3CLN is only transient and the conformations sampled in regions I and II do not overlap with any of those from the NMR ensemble ., This is consistent with other NMR and single molecule experiments where very low occupancy is assigned to the fully extended structure 31 ., After 220 ns , the system eventually relaxes into a region with l\u200a=\u200a28 , 34 Å , φ\u200a=\u200a−210° , −130° which overlaps with many of the 2K0E NMR ensemble members ( figure 3b ) ., These observations imply that there is an energy barrier between the regions with φ\u200a=\u200a90° and φ\u200a=\u200a150° so that the system must counter-rotate by a large torsional angle , instead of flipping the 60° directly ., In the runs , we find that by mimicking low pH , low IS environment , the sampled regions in figure 3b do not change ( see figure S1 ) , but the sampling is accelerated ., We do not go into the details of these runs since we have already published a detailed account of the conformations sampled and key events leading to the conformational change 24 ., However , it suffices to say that the same sequence of states I→II→III are followed in both runs ., The shift from state I to II occurs at ca ., 20 ns similar to the time scales observed in , but that from II to III occurs at ca ., 70 ns ., For both the and the systems , the main intra-domain conformational change occurs as a reorientation of helix II in the N-lobe ., For example , the nearly right angle between helices I-II , that was always maintained at nearly right angles ( 80±6° ) at physiological IS in the and runs , is now reduced within the first 10–20 ns of the trajectory ., It is maintained at a value of 56±7° in and 60±5° in throughout the window of observation ., The major event that stabilizes the closed conformation is the formation of salt bridge ( s ) between the N-lobe and the linker in each case: E7-K77 , E11-K77 or E54-K75 in ( established in both runs prior to 50 ns ) and E7-R74 or E11-K77 in the runs ( forming permanently at ca . 40 ns in both samples ) ., Despite being a counterintuitive observation , it was shown as a general result that two negatively charged nano-sized spheres may be put into close contact by utilizing the competition of hydrophobic and Coulombic interactions , provided that the charges are placed discretely along the surface 40 ., At physiological IS and pH , there exists a high energy barrier between the extended and compact structures , corroborated by the 100 µs time scale of jumps between them , measured by single molecule experiments 27 ., Thus , a direct passage between the black and blue shaded regions in figure 2 is not observed within the time window of observations of the MD simulations ., However , once achieved , the compact conformation is stable despite the net repulsions between the two lobes ( net charge on the N- and the C-lobes are -8 and -6 , respectively ) ., One may argue that the pH of the X-ray experiment ( 5 . 4 ) may have contributed to the stability of the 1PRW crystal structure , since the acidic residues 7 , 11 , 14 , 120 , 127 are found to be neutral at this pH 41 ., The interface between the two lobes involves E7 and E11 on the N-lobe interacting with E127 on the C-lobe , as well as pairing between E14-E120 ., Thus , it may well be that the acidic contacts do not repel each other in the crystal due to the loss of the charges ., In contrast , our MD simulations starting from the 1PRW structure assigns their usual charges to these residues to mimic the physiological pH conditions ., Nevertheless , the interface accommodates the repulsions between the closely located negative charges by slightly expanding around the adjacent helices and rotations in the side chains ( figure S2 ) ., Thus , the initial state is maintained during the 200 ns window of MD observations , regardless of the charge states of the interface residues ., With this robust accommodation of charges in mind , we seek the reasons behind the relaxation of the initial X-ray structure to new conformations when the environmental conditions are perturbed ., To be noted is the conformational plasticity in the MD runs at low IS ( figure 3b ) , and the similarity between a subset of the NMR ensemble structures and state III structures ., The ionized states of the acidic residues make the electrostatic component dominant and strongly oppose direct inter-domain association on the time scale of the simulations 41 ., This fact does not keep the system from sampling a plethora of conformations in the φ space ., Thus , to understand how the interfered charge distribution in the environment affects the vicinal solvent layer around the protein , we study the distribution of the solvated non-specific ions around the protein in each case ., We display in figure 4a , the radial distribution function ( RDF ) of the ions in the solvent around the side chain heavy atoms of the protein at low and high IS ., The first peak belongs solely to the contact of Na+ ions with O− atoms of the negatively charged residues ., The second peak is due to the solvent mediated interactions ., Interestingly , although there are plenty of positively charged residues on the surface of the protein , Cl− ions ( which only exist in the physiological IS run ) rarely interact with them ., At low IS , the Na+ ions strongly interact with the negative charges on the protein , thus screening the extreme repulsions between the two lobes and allowing rotational motions around the linker ., To achieve physiological IS , Cl- ions as well as additional Na+ ions are added to the system ., In the presence of these additional mobile negative charges the Na+ ions mainly reside away from the protein surface and in bulk water where they may also dynamically interact with Cl− ions ( we check that there is no permanent ion pairing occurring between Na+ and Cl− ions ) ., More interestingly , lack of salt in the solvent environment reduces the time scale of conformational change by three orders of magnitude , from sub-milliseconds to sub-microseconds ., Decomposed into the different regions on the protein at low IS ( Figure 4a inset ) , the most significant interaction of the Na+ ions is with the linker residues , followed by those of the C-lobe and even less so with the N-lobe ., We have also monitored the trajectories to find that the cations are mobile and they do not have a preferred position near the linker ., These ionic distributions are contrary to expectations from the net charges , with that of the linker being only -1 , whereas those of the N- and the C-lobes are -8 and -6 , respectively ., Thus , the ionic interactions are geometry specific , and designate the smooth surface of the linker ( relative to the two lobes ) as a region that has a tendency of binding non-specific ions ., We conclude that the conformational plasticity of the torsional motions observed in altered charge environments is due to the clustering of the cations around the linker which screens the strong repulsions between the two lobes ., We check the effect of the change in the number densities of the ions at different ionic strengths on the values of the RDF peaks ., We confirm that the reduction of the peaks exceeds that expected by the 2 . 3 fold increase in the number densities of the ions in the system ( e . g . the linker peak is reduced 3 . 3 fold . ) In terms of the absolute values , the average number of Na+ ions within the first coordination shell of the acidic residues of the linker is 0 . 5 and 1 . 13 for the and systems , respectively ., While the removal of Ca2+ ion from EF-hand loop I readily induced compaction of CaM in a previous MD simulation 42 , we are interested in revealing its role in CaM dynamics in fully loaded state ., We have previously shown that E31located in this loop is unique in that its perturbation in a given direction reproduces the closed form structure with high overlap 14 ., In fact , unlike its positional counterparts on the other EF-hand loops of CaM , the role of E31 is not as central in Ca+2 ion coordination as its involvement in signaling coordination between the two lobes ., This statement is supported by a series of experimental E→K point mutation studies at the four equivalent EF-hand positions ( 31 , 67 , 104 and 140 ) 43 , 44 ., Two results are striking: E31K mutation, ( i ) has wild type activation on four different enzymes while the others do not;, ( ii ) does not lead to apparent binding affinity changes while the rest lead to the loss of Ca+2 binding at one site ., It was also shown that proton flux is an important factor affecting conformational changes in CaM and its enzyme targets 45 ., We have therefore protonated E31 while keeping the IS low in the set of MD simulations ., Since the topology of the residue is the same except for the reduced charge on the side chain , it still interacts with the Ca2+ in the EF hand I ., We have monitored this motif throughout the trajectories and ensured there is no loosening in the motif ., Strikingly , we find that the net effect of this single point protonation on the sampled conformations is similar to increasing the IS , keeping them near the initial extended structure ( compare figures 2b and S3b ) , with an average l value of 33 Å and torsional angle range φ\u200a=\u200a80° , 130° ., Despite the protonation of a single point , the RDFs measured in these runs are also more similar to the high IS system ( figure 4b ) , significantly reducing the density of Na+ ion clustering around the protein , mainly affecting the linker region ( value reduced to 3 . 1 from 10 . 3; see Table 2 ) ., E31 is able to significantly reduce the ion density around the protein and the linker at the same time ( Table 2 ) ., For example , protonation of D122 , the second residue with the most significantly upshifted pKa , in a control run also leads to similar values ., E31/D122 double protonation further reduces the ion density around the whole protein and the linker; while the E31/H107 double protonation does not bring in this additional effect ., However , protonation of 10 residues to mimic the pH 5 environment in is effective in further reducing the charges around the linker environment , while its effect on the overall protein is less apparent ., The most drastic change in the extended conformation occurs in the system ., As we discuss in detail below , the E31A mutation opens a direct path between the extended conformation and compact structures with a bent linker , accessing conformations not sampled by any of the other systems ., runs are characterized by increased mobility of the N-lobe ( 4 Å RMSD ) accompanied by an additional stability in the C-lobe ( RMSD<2 Å ) as well as the linker ., The stability in the latter two regions , not directly perturbed by the E31A mutation , contrasts the simulations discussed in the previous subsection ., We emphasize that the Ca+2 ion coordination is never lost in any part of these MD simulations which total 0 . 55 µs and 1 . 2 µs in and , respectively ., By inspecting the MD trajectories , we find that the main direct difference between and runs is that while the calcium binding motif is not disrupted in the former , residue 31 can no longer participate in the motif in the latter due to its short side chain and hydrophobic character ., Interestingly , the E31A mutation restores some of the depleted charge distribution around the acidic residues that occurred upon its protonation ( Figure 4b and Table 2 ) ., The reorientation that takes place in the N-lobe is quantified by an increase of the RMSD value from 2 Å to 4 Å ( Figure 5a ) ., The angle between helices III–IV displays a drastic change , with helix III tilting towards helix IV ., This is followed by the formation of a salt bridge between residues E47 and R86 at 60 ns which may be traced in the sharp decrease in l from 34 Å to 27 Å ( Figure 5b ) ., After the salt bridge formation , at 80 ns , the linker is further bent from residue 81 and l drops to 25 Å bearing a compact conformation ., Snapshots are taken before and after transition and shown in Figure 5c ., The observed conformational change is reversible , and the extended structure is restored at ca ., 160 ns ., No significant pKa shift appears for charged residues in any part of the trajectory ., We find that the transition state is well defined , occurring through the same point in both forward and reverse steps ., Time intervals of the transitions between the extended and compact ( forward transition ) and between compact and extended conformations ( reverse transition ) are examined in more detail in figure 6 ., The positions of structures near the transition state in 200 ps intervals are plotted on the ( l , φ ) plane ., Note that the axes have been zoomed in ., The transition between the extended and compact states is also examined via the tool Geometrical Pathways 46 , 47 ., This tool utilizes geometric targeting ( GT ) method that has recently been introduced 46 as a rapid way to generate all-atom pathways from one protein structure to some known target structure ., GT is based on the philosophy that essential features of protein conformational changes can be captured by solely considering geometric relationships between atoms ., The protein is modeled as a geometric system , with constraints established to enforce various aspects of structure quality such as preserving covalent bond geometry , preventing overlap of atoms , avoiding forbidden Ramachandran regions for backbone dihedral angles , avoiding eclipsed side-chain torsional angles , and maintaining hydrogen bonds and hydrophobic contacts ., We note GT cannot predict relative timing of events ., Using Geometrical Pathways in Biomolecules server 46 , we have generated 10 random pathways between representative structures collected structures at 50 ns ( extended ) and at 80 ns ( compact ) of the run 1 ., The RMSD step size is 0 . 05 Å ., The structures generated in the forward pathway by Geometrical Pathways are also plotted on figure 6 with the median of the pathway and the standard error bars along both axes ., The random pathways produced via Geometrical Pathways overlaps with that visited by MD ., They are widely distributed along the interdomain torsional angle dimension , but have narrow distribution in end-to-end-linker distance ., GT generated pathways take energetics into account indirectly , through geometric factors only ., Their overlap with the MD pathway corroborates that the conformational change may be achieved as a series of geometrically viable sequential steps , if the energy barrier between the two states allows them to take place ., In fact , a stabilized conformational change between states I and II is observed in only one of the three runs ., However , several attempted jumps occur with a kinked linker conformation in all simulations

Introduction, Methods, Results, Discussion

We elucidate the mechanisms that lead to population shifts in the conformational states of calcium-loaded calmodulin ( Ca2+-CaM ) ., We design extensive molecular dynamics simulations to classify the effects that are responsible for adopting occupied conformations available in the ensemble of NMR structures ., Electrostatic interactions amongst the different regions of the protein and with its vicinal water are herein mediated by lowering the ionic strength or the pH . Amino acid E31 , which is one of the few charged residues whose ionization state is highly sensitive to pH differences in the physiological range , proves to be distinctive in its control of population shifts ., E31A mutation at low ionic strength results in a distinct change from an extended to a compact Ca2+-CaM conformation within tens of nanoseconds , that otherwise occur on the time scales of microseconds ., The kinked linker found in this particular compact form is observed in many of the target-bound forms of Ca2+-CaM , increasing the binding affinity ., This mutation is unique in controlling C-lobe dynamics by affecting the fluctuations between the EF-hand motif helices ., We also monitor the effect of the ionic strength on the conformational multiplicity of Ca2+-CaM ., By lowering the ionic strength , the tendency of nonspecific anions in water to accumulate near the protein surface increases , especially in the vicinity of the linker ., The change in the distribution of ions in the vicinal layer of water allows N- and C- lobes to span a wide variety of relative orientations that are otherwise not observed at physiological ionic strength ., E31 protonation restores the conformations associated with physiological environmental conditions even at low ionic strength .

Calmodulin ( CaM ) is involved in calcium signaling pathways in eukaryotic cells as an intracellular Ca2+ receptor ., Exploiting pH differences in the cell , CaM performs a variety of functions by conveniently adopting different conformational states ., We aim to reveal pH and ionic strength ( IS ) dependent shifts in the populations of conformational substates by modulating electrostatic interactions amongst the different regions of the protein and with its vicinal water ., For this purpose , we design extensive molecular dynamics simulations to classify the effects that are responsible for adopting different conformations exhibited in the ensemble of NMR structures reported ., Lowering the IS or pH , CaM experiences higher inter-lobe orientational flexibility caused by extreme change in the non-specific ion distribution in the vicinal solvent ., Amongst the titratable groups sensitive to pH variations , E31 is unique in that its protonation has the same effect on the vicinal layer as increasing the IS ., Furthermore , E31A mutation causes a large , reversible conformational change compatible with NMR ensemble structures populating the linker-kinked conformations ., The mutation in the N lobe , at a significant distance , both modulates the electrostatic interactions in the central linker and alters the EF-hand helix orientations in the C lobe .

journal.pgen.1007231

Hereditary cancer genes are highly susceptible to splicing mutations

As the cost of sequencing technologies is declining , the number of genomes and exomes sequenced is increasing , resulting in an expanding archive of genetic variation in both diseased and healthy individuals 1 , 2 ., To keep pace with the ever growing variant archive , in silico tools are being created to determine the functional impact of variants discovered 3–6 ., However , most tools used to determine the pathogenicity of variants rely on in silico methods aimed at deciphering protein features associated with the variant and fail to take into account the potential regulatory functions of sequences in gene processing mechanisms and expression 7 ., The sequences that encode for proteins ( exons ) and the intervening , noncoding sequences ( introns ) are known to have an important regulatory role in an RNA processing mechanism known as precursor messenger RNA ( pre-mRNA ) splicing ., Variants that alter the regulatory regions necessary for splicing typically result in the deletion of large portions of the coding sequence and generally result in a non-functional protein 8 ., Among the reported sequence variants , splicing mutations located at the 5′ and 3′ canonical exon-intron boundaries , or splice sites , make up 13 . 4% of the disease-causing mutations reported in the Human Gene Mutation Database ( HGMD ) 9 ., However , in addition to splicing variants located at the splice sites , splicing variants within the exonic sequences can also modulate splicing by altering the multitude of exonic splicing enhancers ( ESE ) and silencers ( ESS ) present in exons ., Due to the difficulty in classifying exonic mutations as splicing mutations , it is becoming evident that new methods and tools will need to be implemented to correctly and thoroughly identify exonic splicing mutations ( ESM ) ., An ESM is a hereditary disease allele that falls within the exon and was originally annotated as a protein coding mutation ., For the purpose of this analysis , a splice site mutation ( SSM ) falls within the 5`splice site ( i . e . -3 to +6 position 5`end of the intron ) or the 3`splice site ( i . e . -20 to +3 position of the 3`end of the intron ) ., Recently , studies have been aimed at re-analyzing reported sequence variants for splicing defects 10 , 11 ., Much of this work suggests that splice-altering variants are more common than previously anticipated ., For example , a recent re-analysis of 20 coding mutations located in exon 10 of MLH1 , reveal a high proportion of previously uncharacterized ESM ( 17 of the 20 or 77% ) 11 ., In fact , using the position dependence of splicing elements as a measure to infer disruptive splicing , it has recently been predicted that one-third of all disease-causing variants lead to aberrant splicing 12 ., Here , we present a comprehensive analysis of splicing mutations in human disease ., We report 86 genes enriched for SSM , in patients that present with hereditary disease ( see Materials and Methods ) ., Of these 86 SSM-prone genes , three were the main causal genes of Lynch Syndrome ( MLH1 , MSH2 , and PMS2 ) , which account for 32% , 39% , and 14% of Lynch Syndrome cases , respectively 13 ., Lynch Syndrome , a cancer-susceptibility disorder caused by autosomal dominant germline mutations in the mismatch repair ( MMR ) genes above , accounts for ~5% of all colorectal cancers ., In addition , individuals with Lynch Syndrome have an elevated risk of developing early-onset colorectal and endometrial cancers 14 ., With colorectal cancer being the second leading cause of cancer death in the United States 15 , it will be imperative to understand the disease mutational mechanisms underlying Lynch Syndrome to aid in the development of therapeutic strategies ., However , not only were Lynch Syndrome genes members of the 86 SSM-prone genes , but it was also found that the COSMIC set of cancer genes were overrepresented 16 ., This work highlights the importance of allocating additional priority to investigating splicing defects in a described set of genes , many of which have been associated with some feature of cancer risk or progression ., A recent analysis of coding mutations located in exon 10 of MLH1 revealed a high level of coding mutations ( 17/22 or 77% ) altered the splicing of exon 10 11 ., To see if the results of this survey of MLH1 exon 10 was indicative of high levels of splicing phenotypes in exonic mutations across all genes , a larger pool of exonic variants ( outside canonical splice sites ) was analyzed using a high-throughput reporter assay , MaPSy 10 ., MaPSy was used to screen variants in five additional MLH1 exons ., Of the 36 pathogenic MLH1 exonic mutations surveyed with MaPSy , 11 ( 30 . 5% ) affected splicing ( Fig 1A and 1B , S1 Table ) in an in vivo minigene assay and in an in vitro splicing assay ., On average , disease causing point mutations disrupt splicing 10% of the time ( MaPSy 5K panel , n = 4 , 964 alleles ) 10 ., In other words , the rate of splicing misregulation in MLH1 disease alleles was almost three times higher than the background rate of splicing disruption in disease alleles ., Mapping potential exonic splicing regulatory sequences ( ESRs ) 17 in the MLH1 exons analyzed in MaPSy revealed exon mutations that altered splicing resulted in a greater difference in wild type ( wt ) –mutant ( mt ) ESR scores than mutations not resulting in a splicing defects ( average ∆ESR score 1 . 845 and 0 . 8583 respectively , P = 0 . 0280 Mann-Whitney , S1 Fig , S1 Table ) ., MLH1 missense and nonsense mutations were found to frequently disrupt splicing in vitro and in vivo: 6/22 ( 27% ) missense mutations and 5/14 ( 36% ) nonsense mutations ., Taken together , this data, a ) confirms the previous report that exonic mutations in MLH1 frequently disrupt splicing, b ) exonic mutations that alter ESR signals are more likely to result in a splicing defect , and, c ) suggests that the rate of splicing disruption is not homogenous across genes ( i . e . MLH1 is an outlier ) ., Interestingly , ESMs were also disproportionately distributed among the exons within the MLH1 gene ., Of the five exons that were included in this study , three had no ESMs ., However , all the exonic mutations in exon 8 ( 6/6 ) and 71% ( 5/7 ) of the mutations in exon 15 significantly altered splicing ( Fig 1A and 1B ) ., Thus , it appears that certain exons in MLH1 are more prone to splicing disruption ., To investigate the possibility that certain exons may be more prone to ESMs , a permutation approach was used to identify exons that exceeded the expected number of ESMs discovered ( see Materials and Methods ) ., 11 of the 2 , 061 exons analyzed using MaPSy were predicted with a P < 0 . 01 to have more ESM than expected ( S2 Fig ) ., Remarkably , two of these 11 exons identified in the simulation as being enriched for ESMs were MLH1 exon 8 and exon 15 , further confirming the previous finding ., To mechanistically investigate the defective splicing of MLH1 mutations , the representation of MLH1 alleles in the fractions of the in vitro spliceosomal assembly assay was examined ( see Materials and Methods and S3 Fig ) ., Here , the accumulation of an allele in intermediate complexes was interpreted as an indication that the allele blocked the next stage of spliceosome assembly 10 ., In general splice site recognition is thought to occur early in spliceosome assembly 8 , 18 , however for the ESMs in MLH1 , the disruption occurred later ., 63% of exonic splicing mutations were primarily blocked at the A complex in transition to the B complex and 37% were blocked at the B complex ( Fig 2 ) ., Several mutants reduce more than one step in the assembly ( Fig 2 ) ., As expected , adjacent mutations that were close enough to fall within the same cis-element shared a similar pattern of disruption ., In effect , these clusters of variants mutationally defined a particular cis-elements required for particular spliceosomal transitions ( e . g . Fig 2 , CM045463 and CM082944 ) ., The surprisingly high fraction of disease-causing splicing mutations both reported in the splice-sites and unreported in exonic positions of MLH1 ( as shown by the MaPSy 5K panel ) may be due to chance or the enrichment for splicing mutations in the gene/disease ., To eliminate the null hypothesis , Monte Carlo ( MC ) simulations were used to generate a distribution of SSM frequencies for each gene given the total number of mutations reported in that gene ( see Materials and Methods ) ., Of the ~3 , 600 disease genes reported in the HGMD , 86 genes , including the three main casual Lynch Syndrome genes ( MLH1 , MSH2 , and PMS2 ) , had more SSM than expected based on the distribution of SSM in the HGMD dataset ( Fig 3A , S2 Table ) ., Although SSM generally have a severe impact on splicing outcome by disrupting the essential interactions with the core spliceosome components , variants located within the exonic sequence can also alter splicing by disrupting the myriad of exonic splicing regulatory ( ESR ) elements 18 ., Using the results obtained from the MaPSy 5K panel , we found that the 86 SSM-prone genes not only had a higher proportion of mutations in the canonical splice sites but also contained exonic mutations that were almost twice as likely to disrupt splicing as exonic mutations that occurred in the remaining genes ( 1 . 84-fold effect; P = 1 . 48 x 10−9 , Kruskal-Wallis , Fig 3B ) ., These results suggest that the 86 SSM-prone genes are not only prone to SSMs but also to ESMs , with three ESMs in the 86 SSM-prone genes being validated in individual minigene constructs ( Fig 3C ) ., We next sought to determine if a certain class of disease genes were overrepresented in the 86 SSM-prone genes ( S2 Table ) ., The initial report of an association between MLH1 and splicing mutations also associated other cancer related genes such as BRCA1 , BRCA2 , and NF1 with disrupted splicing ., Furthermore , Gene Ontology ( GO ) enrichment analysis 19 of the 86 SSM-prone genes revealed an enrichment of genes associated with the DNA repair pathway ( P = 2 . 53x10-2 , S3 Table ) , a pathway commonly associated with cancer phenotypes 20 , 21 ., To determine if cancer genes were overrepresented in the 86 SSM-prone genes , the Catalogue of Somatic Mutations in Cancer ( COSMIC ) was crossed referenced with the HGMD disease genes 16 ., Of the 609 cancer genes associated with elevated somatic mutations in tumors ( i . e . the COSMIC gene set ) , 280 were reported with germline mutations in hereditary cancers ( i . e . HGMD ) ., These cancer genes were particularly enriched in the SSM-prone genes ( 1 . 5 fold in the upper category 20/86 , P < 0 . 01 , permutation test , Fig 4A ) ., Not only were cancer genes overrepresented in the SSM-prone genes , but they also contained 1 . 5-fold more SSM and 1 . 4-fold more ESM than the rest of the genes in the HGMD ( P = 0 . 011 and P = 0 . 0075 , Mann-Whitney , for SSM and ESM respectively , Fig 4B and 4C ) ., When further dividing the cancer genes into oncogenes and tumor suppressor genes ( TSG ) , it became apparent that TSG have more SSM and ESM than the rest of the genes in the HGMD ( P = 0 . 0178 and P = 1 . 14 x 10−4 , Mann-Whitney , for SSM and ESM respectively , S4 Fig ) ., However , this enrichment for SSM and ESM was not apparent when comparing oncogenes to the rest of the genes in the HGMD ( P = 0 . 4821 and P = 0 . 1914 , Mann-Whitney , for SSM and ESM respectively , S4 Fig ) ., Thus , it appears that TSG are more prone to splicing dysfunction most likely due to their loss-of-function disease mutational mechanism ., A number of genomic and sequence features have been implicated in the context of splicing 17 , 22–25 ., We , therefore , sought to determine if genomic and sequence features existed that would result in the predisposition of a gene to SSM ., In fact , multiple features appeared to modulate the predisposition of a gene to SSM ., When analyzing 19 genomic features ( S4 Table ) 17 , 23 , 26–28 , we found that the 86 SSM-prone genes contained 2 . 5 fold more introns than the rest of the genes in the analysis ( P = 2 . 54 x 10−14 , Kruskal-Wallis , S5 Fig ) ., Thus a trivial explanation for predisposition of the 86 SSM-prone genes is the larger mutational target presented by their higher number of splice sites ., To determine if the SSM-prone genes were predisposed due to the number of introns , we repeated the MC simulation normalizing for the number of introns ( see Materials and Methods ) ., Surprisingly , this correction did not dramatically alter the result ., After normalization , about 74 . 4% ( 64/86 ) of the genes that were significantly enriched for splice site mutations , were present in the recalculated SSM-prone gene list ( S5 Table ) ., In addition to having more introns , the 86 SSM-prone genes are generally more haploinsufficient ( HI ) , have shorter and more structured exons ( predicted to have more base-pairing interactions ) , and less conserved variants found in the exomes of ~60 , 000 healthy individuals 26 ( S5 Fig ) ., To determine the relative contribution of each feature to the classification , several machine learning approaches were trained on the HGMD mutation dataset ., Briefly , the Random Forest ( RF ) 29 and a Logistic Regression ( LR ) predictive models were utilized to predict whether a gene would be associated with a significant excess of SSM ( red dots , Fig 3A; for feature ranking please see Materials and Methods ) ., The model indicates that HI genes and genes with less structured exons have a higher risk of being frequently affected by SSM ( Fig 5A ) ., In addition to feature prioritization , the classifier was also used to predict additional genes that may be prone to SSM but had not yet been identified as human disease genes ., To test the performance of both classifiers , ROC curve analysis was performed ., The mean area under the curve was measured for both machine learning models ., The RF model was the most predictive ( AUC = 0 . 839 , Fig 5B , see S6 Table for cross-validation ) ., A control classifier trained to predict genes that were not prone to SSM ( i . e . Lower-Expected genes , Fig 3A , green ) was considerably less accurate , presumably because this category is lower confidence with fewer associated mutations overall ., As haploinsufficiency was an important feature in the prediction of SSM predisposition ( upper category ) and splicing defects generally result in a severe loss of gene function , it is possible that the degree of haploinsufficiency largely determines a genes predisposition to SSM ., However , the RF model still performed well with HI removed ( AUC = 0 . 805 ) ., Therefore , it does not appear that there is a single dominant feature such as HI or the number of introns that drives the accuracy of the predictor ., Instead it is most likely a combination of features that determine a genes predisposition to SSM ., This analysis suggests that the prediction of genes predisposed to SSM using a broad spectrum of features is feasible and could potentially be used to identify new disease genes that are prone to splicing mutations ., In order to identify new disease genes that are prone to splicing mutations , the predictive model was applied to ~13 , 000 non-disease associated genes ( Fig 5C ) ., While the classifier was run at a range of stringencies ., Using a probability cutoff of 0 . 6–0 . 86 returned by the classifier , 499 genes were predicted to be SSM-prone ( see Materials and Methods , S7 Table ) ., It is possible that these 499 genes were not previously identified as disease genes because their function was required for organismal viability ., To explore the degree to which variation can be tolerated in these 499 genes , the aggregated exome sequencing data from 60 , 706 presumably healthy individuals provided by Exome Aggregation Consortium ( ExAC ) 26 was cross referenced with the 499 genes ., The 499 predicted SSM-prone genes had significantly fewer reported ExAC splice site ( SS ) region variants than the rest of the testable genomic genes in the analysis ( Fig 5D , P = 6 . 1043e-18 , Mann-Whitney ) ., This analysis suggests that the splicing elements in the predicted SSM-prone genes are evolving under a higher level of selective pressure ., However , this analysis considers all variations equivalently making no distinction between neutral variants and clear loss of function variants ., For the variants that fall within the splice sites , position weight matrix ( PWM ) models can be used to evaluate whether a variant represents a stronger or weaker match to the splice site consensus ., In other words , PWM can potentially distinguish loss of function splicing mutants from neutral variation ., In this analysis , variants that greatly weaken the match to a splice site model ( e . g . ∆ > 5 , Fig 5E ) and would be expected to result in a loss of function are four fold underrepresented in common single nucleotide polymorphisms ( SNPs ) ., This suggests a scenario where loss of function variants are eliminated from the variant pool before the SNP can reach a reasonable frequency in the population ., Conversely , variants that fall within the 5′ ss but strengthen the agreement of the site to the consensus tend to accumulate in the high frequency set ( e . g . ∆ < -2 , Fig 5E . ) ., The same trend is observed in variants that localize to the 3′ ss ( S6 Fig ) ., An independent measure of selection can be found in analysis that maps obvious loss-of function variants to the predicted SSM-prone genes ., For example , 3 , 230 genes that were depleted of predicted protein-truncating variants ( PTV’s ) in the exomes of 60 , 706 individuals are a gold standard for genes in which loss of function variants are poorly tolerated 26 ., While PTV depletion is unrelated to splicing , there is a four or five-fold enrichment of predicted SSM-prone genes in this dataset ( S7 Fig , P = 7 . 53e-98 Fisher’s Exact , S7 Table ) ., The lower proportion of ExAC variants located in the genomic genes predicted to be SSM-prone and the enrichment of PTV-intolerant genes in the SSM-prone genes suggests that they are intolerant to variation and appear to be functionally important genes ., It is therefore more likely that splice disrupting variants that map to these genes will be deleterious ., To gain more insight into the uncharacterized set of predicted SSM-prone genes , GO Enrichment analysis was performed ., Regulation of cell cycle ( P = 2 . 20e-2 ) and mitosis ( P = 5 . 08e-5 ) were the two functions enriched in predicted SSM-prone genes ( S8 Table , for individual GO term associations see S7 Table ) ., Since the hallmark of cancer is generally the abnormal growth and division of cells , it is possible that mutations within this set may play some yet undiscovered role in cancer ., While a more complete characterization of these genes awaits future study , an online browser has been developed to visualize the splicing results of the exonic mutations assessed in the SSM-prone cancer genes studied using MaPSy ( S9 Table ) ., High rates of splicing disruption were reported in the literature for exonic variations in a panel of exons in medically important genes 10 , 11 , 30 , 31 ., As there have been a wide variety of estimates of the degree to which splicing defects accompany disease-causing mutations , this current study was initially intended to perform this analysis at a larger scale ., The query was expanded to include both exonic and splice site mutations in the set of human genes known to cause hereditary disease ., This analysis confirmed the initial reports of high mutation rates in the genes studied but also demonstrated that the degree to which splicing causes disease varies significantly from gene to gene ., Recent analysis of mutations in MLH1 , a mismatch repair gene tied to Lynch Syndrome , indicated a high degree of splicing disruption as a common disease mechanism of exon 10 ., Due to Lynch Syndrome’s highly penetrant nature in inherited colorectal cancer predisposition , understanding the pathogenesis of the syndrome will be fundamental in devising treatment methods ., To further analyze the disease mechanisms in MLH1 , 36 additional exonic mutations were tested with 31% disrupting splicing ( Fig 1 ) ., The degree to which exonic mutations affect splicing also vary across exons ., For example , in MLH1 , all of the ESM occurred in two of the five exons tested ( Fig 1A ) ., Earlier work on spliceosome assembly suggested a mechanism where the spliceosome ‘commit’ to splice sites early in the process 32 ., In contrast , many of these mutations that disrupted splicing fairly late in the assembly of the spliceosome ( Fig 2 ) ., Overall , the MaPSy assay demonstrated a three-fold increase in likelihood that a missense mutation in MLH1 would result in a splicing defect ., This study confirms earlier findings of high frequency of splicing defects in MLH1 mutants , but also suggests that the Lynch Syndrome genes , MLH1 , MSH2 and PMS2 , and the other tested genes are outliers and are prone to splicing disruption ., A major conclusion drawn from this study is the existence of a class of diseases that are often caused by splicing mutations ( i . e . SSM and ESM ) ., The role that splicing defects plays in genetic disease varies across disease genes but genes with elevated SSM also have elevated ESMs ( Fig 3 ) ., The discovery of a class of genes prone to splicing mutations , led to an exploration of what features and cellular functions that predisposed splicing genes encode ., GO term analysis indicated that many of these genes were involved in cancer initiation and progression ., Defining a set of ‘cancer’ genes at the intersection of the COSMIC and HGMD dataset revealed a significant elevation of SSM and ESM in cancer genes , including genes involved in Lynch Syndrome ( Fig 4 ) ., Cancer genes are enriched in the SSM-prone genes ( Fig 3A , red category ) ., Cancer genes in this category have higher predicted haploinsufficiency than cancer genes associated with lower levels of SSMs ( Fig 4D ) ., Machine learning was used to determine other features associated with the SSM-prone genes ( Fig 5A ) ., In general , no single feature dominated , rather a combination of features determined whether a disease gene was prone to splicing mutations ., However , there are certain properties of splicing mutation that warrant further consideration ., Splicing disruptions are potent loss of function mutations ., This property probably explains the evidence of haploinsufficiency in the SSM-prone genes ., Finally , unlike protein coding variants , splicing variants could have tissue specific affects ., Consistent with a model of tissue specific affects , Lynch syndrome causes a wide variety of cancer types ., While beyond the scope of this work , further studies will be needed to explore tissue specific differences in splicing for Lynch syndrome mutations ., As there is a high medical importance in discovering new cancer genes , the random forest classifier that was trained on the set of 86 SSM-prone genes was applied across the entire genome to reveal a set of 499 predicted SSM-prone genes ., One possibility is these 499 SSM-prone genes could be targets of splicing factors that contain dominant oncogenic mutations ( e . g . SF3B1 , U2AF1 ) 33–35 ., Highly significant enrichment in the overlap between the targets of these driver mutations and SSM-prone genes was observed ., However , this enrichment disappeared when a correction for intron number was applied to the analysis ., While little is known about this novel set of genes , the mark of purifying selection is evident in the degree of variation tolerated in these genes ., Using the ExAC dataset , significantly fewer variants are tolerated within splice site regions in the predicted SSM-prone genes ., Stratifying these variants by the degree to which the mutation disrupts the splice site suggests a strong selection against splicing mutations in common SNPs ., In other words , variants that significantly decrease the PWM scores at the 5′ ss and 3′ ss are underrepresented in common SNPs implying that they are removed by natural selection before they reach MAF >0 . 01 in the human population ( Fig 5E , S4 Fig ) ., The finding that more than half of the 499 predicted SSM-prone genes also do not tolerate premature stop codons is further indication of strong selection ( S5 Fig ) ., While it is beyond the scope of this work to define the role and function of each of these genes , there is an indication that many relate to cancer ., Of the 12 GO terms enriched in this set , 4 categories were also associated with the original set of cancer genes suggesting the existence of novel cancer genes ( comparison of COSMIC cancer gene GO terms and 499 predicted SSM-prone gene GO terms ) ., Taken together these findings suggest a set of genes that should be prioritized in the analysis of clinical sequencing data with a particular emphasis on cancer ., The 36 exonic MLH1 mutations assessed for splicing defects mapped to internal exons and were selected based on their classification of being disease causing ( DM ) with a previously undocumented role in splicing ., The splicing efficiency of wildtype and mutant exons was calculated as below:, log2 ( spli∕∑i=1nsplinpi∕∑i=1ninp ), where spli is the count for spliced output i , inpi is the count for input i , and n is the number of species that were analyzed in the library pool ., MaPSy experiments in vivo and in vitro were performed as previously described 10 ., Briefly , solid-phase oligonucleotide synthesis technology was used to generate a 200 nt fragment ( 200-mer ) that included both the wildtype and mutant exons , 15 nt of the downstream intron and ≥55 nt of the upstream intron , and were flanked by 15-mer common primer sequences ., The in vivo splicing reporters were generated using overlapping PCR and consists of the Cytomegalovirus ( CMV ) promotor , Adenovirus ( pHMS81 ) exon with part of its downstream intron at the 5′ end , followed by the 200-mer library , and exon 16 of ACTN1 with part of intron 15 and the bGH polyA signal sequence at the 3′ end ., The resulting in vivo reporters were transfected into human embryonic kidney hek293T cells ., After 24 hours of transfection , RNA was extracted and both the input reporters and spliced species were sequenced ., The in vitro splicing reporters have a similar design to the in vivo reporters , but exclude the ACTN1 exon , and the CMV promoter was replaced with the T7 promoter ., The in vitro splicing reporters were obtained through in vitro transcription using T7 RNA Polymerase ., The resulting RNA was then used for splicing reactions in 40% HeLa-S3 nuclear extract ., Pools of the input and spliced RNAs were converted to cDNA and prepped for deep sequencing ., The allele ratios between wildtype and mutant exons in the different spliceosomal fractions were obtained as follows:, log2 ( mie/miimje/mji ), where mie and mii is the counts for the minor allele in the selected pool and input , respectively , mje and mji is the counts for the major allele in the selected pool and input , respectively ., For each wildtype-mutant pair , the allele that splices more efficiently is assigned as the major allele ., Wildtype and mutant sequences of exon 15 of MLH1 ( NM_000249 . 3:c . 1684C-T ) , exon 2 of BRCA1 ( NM_007294 . 3:c . 5425G-T ) and exon 12 of OPA1 ( NM_015560 . 2:c . 1199C-T ) were synthesized by Synbio Tech ( Monmouth Junction , NJ ) and incorporated into MaPSy in vivo backbone ( Adenovirus ( HMS81 ) and ACTN1 exon 15 by overlapping PCR 10 ., MaPSy constructs were transfected into 293T cells and RNA were extracted after 24 hours ., RT-PCR were subsequently performed and ran on 1 . 5% agarose gel , as previously described 10 ., Hexamer ESEs and ESSs were downloaded from published data ( 17 ) ., A sliding window of 1 nucleotide was used plot the predicted ESEs and ESSs in the MLH1 exons assayed with MaPSy ( S1 Fig ) ., The ‘ESR wt/mt difference’ in S1 Table was computed as the wild type-mutant difference in hexamer scores ( 17 ) ., Disease causing splicing and coding sequence mutations ( DM–disease mutations ) were selected from the Human Genome Mutation Database ( HGMD ) ., The mutations were then classified as SSM , missense , or nonsense mutations ., To be considered an SSM , the variant was required to be within the canonical splice-sites ( -3 to +6 positions at the 5′ ss and -20 to +3 at the 3′ ss ) and labeled as a splicing mutation by HGMD ., The number of missense , nonsense , and SSM mutations were determined for each intron-containing gene ., The list of 86 SSM-prone genes from HGMD and the list of 499 predicted SSM-prone genes were analyzed for the enrichment of specific GO terms using the PANTHER GO-Slim Biological Process annotation data set provided by the PANTHER Classification System ., The list of cancer genes provided by the Catalogue of Somatic Mutations in Cancer ( COSMIC ) was downloaded and intersected with the list of HGMD genes ., A permutation test was then performed to determine if cancer genes were overrepresented in the SSM-prone genes ., ESS , ESE , and ESR’s were downloaded from published data 17 and the density was calculated by dividing the total number of regulatory elements by the length of the exonic sequences and averaging the density per gene ., SNP density was calculated using the list of common SNPs ( MAF > 0 . 01 ) provided by exome consortium 26 and dividing by the length of the exonic sequence ( ‘Exon SNP dens’ ) or the length of the gene ( ‘Gene SNP dens’ ) ., Conservation was scored using PhastCons46way placental for both the exonic sequences ( ‘Exon Cons’ ) and coding sequence ( ‘Gene Cons’ ) ., The free energy estimate ( ∆G ) was computed using RNAfold 27 , with default settings for both the exonic sequences ( ‘Exon ∆G’ ) and the for 70 nucleotides up- and down-stream of the splice-sites ( ‘SS ∆G’ ) ., Haploinsufficiency scores were obtained from a previous study that developed a haploinsufficiency prediction model using a large deletion data set ( Wellcome Trust Consortium ) 23 ., Splice site strength was calculated using perl scripts from the MaxEntScan 28 ., ExAC variant conservation was determined using the intersection of the ‘phastCons100way’ track with ‘ExAC Variant’ locations over each gene reported in the HGMD ., The intersection generated an average conservation score for the variant sites in each gene based on a zero to one scale ., R implementation of random forest , package ‘randomForest’ 29 , was used to determine the individual contribution of various functional genomic features ( see ‘Random forest predictor variables and features’ methods section ) in distinguishing SSM-prone genes from non-SSM-prone genes and to generate a predictive model ., ‘randomForest’ is a nonparametric ensemble learning method where individual trees ( kth trees ) in a forest are constructed based off a different sub-sample ( bootstrap sample ) from the original training set and then averaged to provide unbiased estimates of predicted values ., Two-thirds of the training set was used for the construction of the kth trees with the remaining one-third ( out-of-bag data ) used for cross-validation and estimates of variable importance ., Default parameters were used to construct the random forest model , with the exception that ‘strata’ was used to sample the majority class ( genes with the expected number of SSM ) to make the frequency of the expected class closer to the frequency of the rarest class ( genes with more SSM than expected ) ., Variable importance was measured by the degree of model accuracy decrease with the permutation of a single predictor variable ., The larger the mean decease in accuracy , the more important the variable is deemed in the classification of the data ., R implementation of logistic regression , ‘glm ( ) ’ function , was used to generate a predictive model for distinguishing SSM-prone genes from non-SSM-prone genes ., Logistic regression is a classification method that relies on fitting a regression curve given a set of pre

Introduction, Results, Discussion, Materials and methods

Substitutions that disrupt pre-mRNA splicing are a common cause of genetic disease ., On average , 13 . 4% of all hereditary disease alleles are classified as splicing mutations mapping to the canonical 5′ and 3′ splice sites ., However , splicing mutations present in exons and deeper intronic positions are vastly underreported ., A recent re-analysis of coding mutations in exon 10 of the Lynch Syndrome gene , MLH1 , revealed an extremely high rate ( 77% ) of mutations that lead to defective splicing ., This finding is confirmed by extending the sampling to five other exons in the MLH1 gene ., Further analysis suggests a more general phenomenon of defective splicing driving Lynch Syndrome ., Of the 36 mutations tested , 11 disrupted splicing ., Furthermore , analyzing past reports suggest that MLH1 mutations in canonical splice sites also occupy a much higher fraction ( 36% ) of total mutations than expected ., When performing a comprehensive analysis of splicing mutations in human disease genes , we found that three main causal genes of Lynch Syndrome , MLH1 , MSH2 , and PMS2 , belonged to a class of 86 disease genes which are enriched for splicing mutations ., Other cancer genes were also enriched in the 86 susceptible genes ., The enrichment of splicing mutations in hereditary cancers strongly argues for additional priority in interpreting clinical sequencing data in relation to cancer and splicing .

To understand the extent to which disrupted pre-mRNA splicing causes human disease , we re-analyzed coding mutations in MLH1 , one of the causal genes of Lynch Syndrome ., We found that a high fraction of the MLH1 coding mutations resulted in disrupted splicing ., To further investigate a more general role of defective splicing across human disease genes , simulation strategies were used to identify 86 disease genes prone to splice site mutations ., In these 86 genes , there was an enrichment of cancer genes including the three main casual genes of Lynch Syndrome ( MLH1 , MSH2 , and PMS2 ) ., Thus , it appears defective splicing may be the main driver of Lynch Syndrome and other cancers ., Genes prone to splicing mutations have certain features that allow for the comprehensive prediction of splicing-prone diseases genes in the human genome ., Our findings strongly argue for additional clinical sequencing prioritization in both cancer genes and genes prone to splice site mutations .

medicine and health sciences, genetic diseases, mutation, hereditary nonpolyposis colorectal cancer, forecasting, mathematics, statistics (mathematics), genome analysis, nonsense mutation, molecular biology techniques, autosomal dominant diseases, research and analysis methods, exon mapping, genome complexity, gene mapping, mathematical and statistical techniques, statistical methods, molecular biology, clinical genetics, point mutation, genetics, biology and life sciences, physical sciences, genomics, gene prediction, computational biology, introns

journal.pcbi.1005331

Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

In the life sciences , the abundance of experimental data is rapidly increasing due to the advent of novel measurement devices ., Genome and transcriptome sequencing , proteomics and metabolomics provide large datasets 1 at a steadily decreasing cost ., While these genome-scale datasets allow for a variety of novel insights 2 , 3 , a mechanistic understanding on the genome scale is limited by the scalability of currently available computational methods ., For small- and medium-scale biochemical reaction networks mechanistic modeling contributed greatly to the comprehension of biological systems 4 ., Ordinary differential equation ( ODE ) models are nowadays widely used and a variety of software tools are available for model development , simulation and statistical inference 5–7 ., Despite great advances during the last decade , mechanistic modeling of biological systems using ODEs is still limited to processes with a few dozens biochemical species and a few hundred parameters ., For larger models rigorous parameter inference is intractable ., Hence , new algorithms are required for massive and complex genomic datasets and the corresponding genome-scale models ., Mechanistic modeling of a genome-scale biochemical reaction network requires the formulation of a mathematical model and the inference of its parameters , e . g . reaction rates , from experimental data ., The construction of genome-scale models is mostly based on prior knowledge collected in databases such as KEGG 8 , REACTOME 9 and STRING 10 ., Based on these databases a series of semi-automatic methods have been developed for the assembly of the reaction graph 11–13 and the derivation of rate laws 14 , 15 ., As model construction is challenging and as the information available in databases is limited , in general , a collection of candidate models can be constructed to compensate flaws in individual models 16 ., For all these model candidates the parameters have to be estimated from experimental data , a challenging and usually ill-posed problem 17 ., To determine maximum likelihood ( ML ) and maximum a posteriori ( MAP ) estimates for model parameters , high-dimensional nonlinear and non-convex optimization problems have to be solved ., The non-convexity of the optimization problem poses challenges , such as local minima , which have to be addressed by the selection of optimization methods ., Commonly used global optimization methods are multi-start local optimization 18 , evolutionary and genetic algorithms 19 , particle swarm optimizers 20 , simulated annealing 21 and hybrid optimizers 22 , 23 ( see 18 , 24–26 for a comprehensive survey ) ., For ODE models with a few hundred parameters and state variables multi-start local optimization methods 18 and related hybrid methods 27 have proven to be successful ., These optimization methods use the gradient of the objective function to establish fast local convergence ., While the convergence of gradient based optimizers can be significantly improved by providing exact gradients ( see e . g . 18 , 28 , 29 ) , the gradient calculation is often the computationally most demanding step ., The gradient of the objective function is usually approximated by finite differences ., As this method is neither numerically robust nor computationally efficient , several parameter estimation toolboxes employ forward sensitivity analysis ., This decreases the numerical error and computation time 18 ., However , the dimension of the forward sensitivity equations increases linearly with both the number of state variables and parameters , rendering its application for genome-scale models problematic ., In other research fields such as mathematics and engineering , adjoint sensitivity analysis is used for parameter estimation in ordinary and partial differential equation models ., Adjoint sensitivity analysis is known to be superior to the forward sensitivity analysis when the number of parameters is large 30 ., Adjoint sensitivity analysis has been used for inference of biochemical reaction networks 31–33 ., However , the methods were never picked up by the systems and computational biology community , supposedly due to the theoretical complexity of adjoint methods , a missing evaluation on a set of benchmark models , and an absence of an easy-to-use toolbox ., In this manuscript , we provide an intuitive description of adjoint sensitivity analysis for parameter estimation in genome-scale biochemical reaction networks ., We describe the end value problem for the adjoint state in the case of discrete-time measurement and provide an user-friendly implementation to compute it numerically ., The method is evaluated on seven medium- to large-scale models ., By using adjoint sensitivity analysis , the computation time for calculating the objective function gradient becomes effectively independent of the number of parameters with respect to which the gradient is evaluated ., Furthermore , for large-scale models adjoint sensitivity analysis can be multiple orders of magnitude faster than other gradient calculation methods used in systems biology ., The reduction of the time for gradient evaluation is reflected in the computation time of the optimization ., This renders parameter estimation for large-scale models feasible on standard computers , as we illustrate for a comprehensive kinetic model of ErbB signaling ., We consider ODE models for biochemical reaction networks ,, x ˙ = f ( x , θ ) , x ( t 0 ) = x 0 ( θ ) , ( 1 ), in which x ( t , θ ) ∈ R n x is the concentration vector at time t and θ ∈ R n θ denotes the parameter vector ., Parameters are usually kinetic constants , such as binding affinities as well as synthesis , degradation and dimerization rates ., The vector field f : R n x × R n θ ↦ R n x describes the temporal evolution of the concentration of the biochemical species ., The mapping x 0 : R n θ ↦ R n x provides the parameter dependent initial condition at time t0 ., As available experimental techniques usually do not provide measurements of the concentration of all biochemical species , we consider the output map h : R n x × R n θ ↦ R n y ., This map models the measurement process , i . e . the dependence of the output ( or observables ) y ( t , θ ) ∈ R n y at time point t on the state variables and the parameters ,, y ( t , θ ) = h ( x ( t , θ ) , θ ) ., ( 2 ), The i-th observable yi can be the concentration of a particular biochemical species ( e . g . yi = xl ) as well as a function of several concentrations and parameters ( e . g . yi = θm ( xl1 + xl2 ) ) ., We consider discrete-time , noise corrupted measurements, y ¯ i j = y i ( t j , θ ) + ϵ i j , ϵ i j ∼ N ( 0 , σ i j 2 ) , ( 3 ), yielding the experimental data D = { ( ( y ¯ i j ) i = 1 n y , t j ) } j = 1 N . The number of time points at which measurements have been collected is denoted by N . Remark: For simplicity of notation we assume throughout the manuscript that the noise variances , σ i j 2 , are known and that there are no missing values ., However , the methods we will present in the following as well as the respective implementations also work when this is not the case ., For details we refer to the S1 Supporting Information ., We estimate the unknown parameter θ from the experimental data D using ML estimation ., Parameters are estimated by minimizing the negative log-likelihood , an objective function indicating the difference between experiment and simulation ., In the case of independent , normally distributed measurement noise with known variances the objective function is given by, J ( θ ) = 1 2 ∑ i = 1 n y ∑ j = 1 N y ¯ i j - y i ( t j , θ ) σ i j 2 , ( 4 ), where yi ( tj , θ ) is the value of the output computed from Eqs ( 1 ) and ( 2 ) for parameter value θ ., The minimization ,, θ * = arg min θ ∈ Θ J ( θ ) , ( 5 ), of this weighted least squares J yields the ML estimate of the parameters ., The optimization problem Eq ( 5 ) is in general nonlinear and non-convex ., Thus , the objective function can possess multiple local minima and global optimization strategies need to be used ., For ODE models multi-start local optimization has been shown to perform well 18 ., In multi-start local optimization , independent local optimization runs are initialized at randomly sampled initial points in parameter space ., The individual local optimizations are run until the stopping criteria are met and the results are collected ., The collected results are visualized by sorting them according to the final objective function value ., This visualization reveals local optima and the size of their basin of attraction ., For details we refer to the survey by Raue et al . 18 ., In this study , initial points are generated using latin hypercube sampling and local optimization is performed using the interior point and the trust-region-reflective algorithm implemented in the MATLAB function fmincon . m ., Gradients are computed using finite differences , forward sensitivity analysis or adjoint sensitivity analysis ., A näive approximation to the gradient of the objective function with respect to θk is obtained by finite differences ,, ∂ J ∂ θ k ≈ J ( θ + a e k ) - J ( θ - b e k ) a + b , ( 6 ), with a , b ≥ 0 and the kth unit vector ek ., In practice forward differences ( a = ϵ , b = 0 ) , backward differences ( a = 0 , b = ϵ ) and central differences ( a = ϵ , b = ϵ ) are widely used ., For the computation of forward finite differences , this yields a procedure with three steps: In theory , forward and backward differences provide approximations of order ϵ while central differences provide more accurate approximations of order ϵ2 , provided that J is sufficiently smooth ., In practice the optimal choice of a and b depends on the accuracy of the numerical integration 18 ., If the integration accuracy is high , an accurate approximation of the gradient can be achieved using a , b ≪ 1 . For lower integration accuracies , larger values of a and b usually yield better approximations ., A good choice of a and b is typically not clear a priori ( cf . 34 and the references therein ) ., The computational complexity of evaluating gradients using finite differences is affine linear in the number of parameters ., Forward and backward differences require in total nθ + 1 function evaluations ., Central differences require in total 2nθ function evaluations ., As already a single simulation of a large-scale model is time-consuming , the gradient calculation using finite differences can be limiting ., State-of-the-art systems biology toolboxes , such as the MATLAB toolbox Data2Dynamics 7 , use forward sensitivity analysis for gradient evaluation ., The gradient of the objective function is, ∂ J ∂ θ k = ∑ i = 1 n y ∑ j = 1 N y ¯ i j - y i ( t j , θ ) σ i j 2 s i , k y ( t j ) , ( 7 ), with s i , k y ( t ) : t 0 , t N ↦ R denoting the sensitivity of output yi at time point t with respect to parameter θk ., Governing equations for the sensitivities are obtained by differentiating Eqs ( 1 ) and ( 2 ) with respect to θk and reordering the derivatives ., This yields, s ˙ k x = ∂ f ∂ x s k x + ∂ f ∂ θ k , s k x ( t 0 ) = ∂ x 0 ∂ θ k s i , k y = ∂ h i ∂ x s k x + ∂ h i ∂ θ k ( 8 ), with s k x ( t ) : t 0 , t N ↦ R n x denoting the sensitivity of the state x with respect to θk ., Note that here and in the following , the dependencies of f , h , x0 and their ( partial ) derivatives on t , x and θ are not stated explicitly but have the to be assumed ., For a more detailed presentation we refer to the S1 Supporting Information Section 1 . Forward sensitivity analysis consists of three steps: Step 1 and 2 are often combined , which enables simultaneous error control and the reuse of the Jacobian 30 ., The simultaneous error control allows for the calculation of accurate and reliable gradients ., The reuse of the Jacobian improves the computational efficiency ., The number of state and output sensitivities increases linearly with the number of parameters ., While this is unproblematic for small- and medium-sized models , solving forward sensitivity equations for systems with several thousand state variable bears technical challenges ., Code compilation can take multiple hours and require more memory than what is available on standard machines ., Furthermore , while forward sensitivity analysis is usually faster than finite differences , in practice the complexity still increases roughly linearly with the number of parameters ., In the numerics community , adjoint sensitivity analysis is frequently used to compute the gradients of a functional with respect to the parameters if the function depends on the solution of a differential equation 35 ., In contrast to forward sensitivity analysis , adjoint sensitivity analysis does not rely on the state sensitivities s k x ( t ) but on the adjoint state p ( t ) ., The calculation of the objective function gradient using adjoint sensitivity analysis consists of three steps: Step 1 and 2 , which are usually the computationally intensive steps , are independent of the parameter dimension ., The complexity of Step 3 increases linearly with the number of parameters , yet the computation time required for this step is typically negligible ., The calculation of state and output trajectories ( Step 1 ) is standard and does not require special methods ., The non-trivial element in adjoint sensitivity analysis is the calculation of the adjoint state p ( t ) ∈ R n x ( Step 2 ) ., For discrete-time measurements—the usual case in systems and computational biology—the adjoint state is piece-wise continuous in time and defined by a sequence of backward differential equations ., For t > tN , the adjoint state is zero , p ( t ) = 0 . Starting from this end value the trajectory of the adjoint state is calculated backwards in time , from the last measurement t = tN to the initial time t = t0 ., At the time points at which measurements have been collected , tN , … , t1 , the adjoint state is reinitialised as, p ( t j ) = lim t → t j + p ( t ) + ∑ i = 1 n y ∂ h i ∂ x T y ¯ i j - y i ( t j ) σ i j 2 , ( 9 ), which usually results in a discontinuity of p ( t ) at tj ., Starting from the end value p ( tj ) as defined in Eq ( 9 ) the adjoint state evolves backwards in time until the next measurement point tj−1 or the initial time t0 is reached ., This evolution is governed by the time-dependent linear ODE, p ˙ = - ∂ f ∂ x T p ., ( 10 ), The repeated evaluation of Eqs ( 9 ) and ( 10 ) until t = t0 yields the trajectory of the adjoint state ., Given this trajectory , the gradient of the objective function with respect to the individual parameters is, ∂ J ∂ θ k = - ∫ t 0 t N p T ∂ f ∂ θ k d t - ∑ i , j ∂ h i ∂ θ k y ¯ i j - y i ( t j ) σ i j 2 - p ( t 0 ) T ∂ x 0 ∂ θ k ., ( 11 ), Accordingly , the availability of the adjoint state simplifies the calculation of the objective function to nθ one-dimensional integration problems over short time intervals whose union is the total time interval t0 , tN ., Algorithm 1: Gradient evaluation using adjoint sensitivity analysis % State and output Step 1 Compute state and output trajectories using Eqs ( 1 ) and ( 2 ) ., % Adjoint state Step 2 . 1 Set end value for adjoint state , ∀t > tN: p ( t ) = 0 . for j = N to 1 do Step 2 . 2 Compute end value for adjoint state according to the jth measurement using Eq ( 9 ) ., Step 2 . 3 Compute trajectory of adjoint state on time interval t = ( tj−1 , tj by solving Eq ( 10 ) ., end % Objective function gradient for k = 1 to nθ do Step 3 Evaluation of the sensitivity ∂J/∂θk using Eq ( 11 ) ., end Pseudo-code for the calculation of the adjoint state and the objective function gradient is provided in Algorithm 1 . We note that in order to use standard ODE solvers the end value problem Eq ( 10 ) can be transformed in an initial value problem by applying the time transformation τ = tN − t ., The derivation of the adjoint sensitivities for discrete-time measurements is provided in the S1 Supporting Information Section 1 . The key difference of the adjoint compared to the forward sensitivity analysis is that the derivatives of the state and the output trajectory with respect to the parameters are not explicitly calculated ., Instead , the sensitivity of the objective function is directly computed ., This results in practice in a computation time of the gradient which is almost independent of the number of parameters ., A visual summary of the different sensitivity analysis methods is provided in Fig 1 . Besides the procedures also the computational complexity is indicated ., The implementation of adjoint sensitivity analysis is non-trivial and error-prone ., To render this method available to the systems and computational biology community , we implemented the Advanced Matlab Interface for CVODES and IDAS ( AMICI ) ., This toolbox allows for a simple symbolic definition of ODE models ( 1 ) and ( 2 ) as well as the automatic generation of native C code for efficient numerical simulation ., The compiled binaries can be executed from MATLAB for the numerical evaluation of the model and the objective function gradient ., Internally , the SUNDIALS solvers suite is employed 30 , which offers a broad spectrum of state-of-the-art numerical integration of differential equations ., In addition to the standard functionality of SUNDIALS , our implementation allows for parameter and state dependent discontinuities ., The toolbox and a detailed documentation can be downloaded from http://ICB-DCM . github . io/AMICI/ ., For the comparison of different gradient calculation methods , we consider a set of standard models from the Biomodels Database 37 and the BioPreDyn benchmark suite 27 ., From the biomodels database we considered models for the regulation of insulin signaling by oxidative stress ( BM1 ) 38 , the sea urchin endomesoderm network ( BM2 ) 39 , and the ErbB sigaling pathway ( BM3 ) 40 ., From BioPreDyn benchmark suite we considered models for central carbon metabolism in E . coli ( B2 ) 41 , enzymatic and transcriptional regulation of carbon metabolism in E . coli ( B3 ) 42 , metabolism of CHO cells ( B4 ) 43 , and signaling downstream of EGF and TNF ( B5 ) 44 ., Genome-wide kinetic metabolic models of S . cerevisiae and E . coli ( B1 ) 45 contained in the BioPreDyn benchmark suite and the Biomodels Database 15 , 45 were disregarded due to previously reported numerical problems 27 , 45 ., The considered models possess 18-500 state variable and 86-1801 parameters ., A comprehensive summary regarding the investigated models is provided in Table 1 ., To obtain realistic simulation times for adjoint sensitivities realistic experimental data is necessary ( see S1 Supporting Information Section 3 ) ., For the BioPreDyn models we used the data provided in the suite , for the ErbB signaling pathway we used the experimental data provided in the original publication and for the remaining models we generated synthetic data using the nominal parameter provided in the SBML definition ., In the following , we will compare the performance of forward and adjoint sensitivities for these models ., As the model of ErbB signaling has the largest number of state variables and is of high practical interest in the context of cancer research , we will analyze the scalability of finite differences and forward and adjoint sensitivity analysis for this model in greater detail ., Moreover , we will compare the computational efficiency of forward and adjoint sensitivity analysis for parameter estimation for the model of ErbB signaling ., The evaluation of the objective function gradient is the computationally demanding step in deterministic local optimization ., For this reason , we compared the computation time for finite differences , forward sensitivity analysis and adjoint sensitivity analysis and studied the scalability of these approaches at the nominal parameter θ0 which was provided in the SBML definitions of the investigated models ., For the comprehensive model of ErbB signaling we found that the computation times for finite differences and forward sensitivity analysis behave similarly ( Fig 2a ) ., As predicted by the theory , for both methods the computation time increased linearly with the number of parameters ., Still , forward sensitivities are computationally more efficient than finite differences , as reported in previous studies 18 ., Adjoint sensitivity analysis requires the solution to the adjoint problem , independent of the number of parameters ., For the considered model , solving the adjoint problem a single time takes roughly 2-3-times longer than solving the forward problem ., Accordingly , adjoint sensitivity analysis with respect to a small number of parameter is disadvantageous ., However , adjoint sensitivity analysis scales better than forward sensitivity analysis and finite differences ., Indeed , the computation time for adjoint sensitivity analysis is almost independent of the number of parameters ., While computing the sensitivity with respect to a single parameter takes on average 10 . 09 seconds , computing the sensitivity with respect to all 219 parameters takes merely 14 . 32 seconds ., We observe an average increase of 1 . 9 ⋅ 10−2 seconds per additional parameter for adjoint sensitivity analysis which is significantly lower than the expected 3 . 24 seconds for forward sensitivity analysis and 4 . 72 seconds for finite differences ., If the sensitivities with respect to more than 4 parameters are required , adjoint sensitivity analysis outperforms both forward sensitivity analysis and finite differences ., For 219 parameters , adjoint sensitivity analysis is 48-times faster than forward sensitivities and 72-times faster than finite differences ., To ensure that the observed speedup is not unique to the model of ErbB signaling ( BM3 ) we also evaluated the speedup of adjoint sensitivity analysis over forward sensitivity analysis on models B2-5 and BM1-2 ., The results are presented in Fig 2b and 2c ., We find that for all models , but model B3 , gradient calculation using adjoint sensitivity is computationally more efficient than gradient calculation using forward sensitivities ( speedup > 1 ) ., For model B3 the backwards integration required a much higher number of integration steps ( 4 ⋅ 106 ) than the forward integration ( 6 ⋅ 103 ) , which results to a poor performance of the adjoint method ., One reason for this poor performance could be that , in contrast to other models , the right hand side of the differential equation of model B3 consists almost exclusively of non-linear , non-mass-action terms ., Excluding model B3 we find an polynomial increase in the speedup with respect to the number of parameters nθ ( Fig 2b ) , as predicted by theory ., Moreover , we find that the product nθ ⋅ nx , which corresponds to the size of the system of forward sensitivity equations , is an even better predictor ( R2 = 0 . 99 ) than nθ alone ( R2 = 0 . 83 ) ., This suggest that adjoint sensitivity analysis is not only beneficial for systems with a large number of parameters , but can also be beneficial for systems with a large number of state variables ., As we are not aware of any similar observations in the mathematics or engineering community , this could be due to the structure of biological reaction networks ., Our results suggest that adjoint sensitivity analysis is an excellent candidate for parameter estimation in large-scale models as it provides good scaling with respect to both , the number of parameters and the number of state variables ., Efficient local optimization requires accurate and robust gradient evaluation 18 ., To assess the accuracy of the gradient computed using adjoint sensitivity analysis , we compared this gradient to the gradients computed via finite differences and forward sensitivity analysis ., Fig 3 visualizes the results for the model of ErbB signaling ( BM3 ) at the nominal parameter θ0 which was provided in the SBML definition ., The results are similar for other starting points ., The comparison of the gradients obtained using finite differences and adjoint sensitivity analysis revealed small discrepancies ( Fig 3a ) ., The median relative difference ( as defined in S1 Supporting Information Section, 2 ) between finite differences and adjoint sensitivity analysis is 1 . 5 ⋅ 10−3 ., For parameters θk to which the objective function J was relatively insensitive , ∂J/∂θk < 10−2 , there are much higher discrepancies , up to a relative error of 2 . 9 ⋅ 103 ., Forward and adjoint sensitivity analysis yielded almost identical gradient elements over several orders of magnitude ( Fig 3b ) ., This was expected as both forward and adjoint sensitivity analysis exploit error-controlled numerical integration for the sensitivities ., To assess numerical robustness of adjoint sensitivity analysis , we also compared the results obtained for high and low integration accuracies ( Fig 3c ) ., For both comparisons we found the similar median relative and maximum relative error , namely 2 . 6 ⋅ 10−6 and 9 . 3 ⋅ 10−4 ., This underlines the robustness of the sensitivitity based methods and ensures that differences observed in Fig 3a indeed originate from the inaccuracy of finite differences ., Our results demonstrate that adjoint sensitivity analysis provides objective function gradients which are as accurate and robust as those obtained using forward sensitivity analysis ., As adjoint sensitivity analysis provides accurate gradients for a significantly reduced computational cost , this can boost the performance of a variety of optimization methods ., Yet , in contrast to forward sensitivity analysis , adjoint sensitivities do not yield sensitivities of observables and it is thus not possible to approximate the Hessian of the objective function via the Fisher Information Matrix 46 ., This prohibits the use of possibly more efficient Newton-type algorithms which exploit second order information ., Therefore , adjoint sensitivities are limited to quasi-Newton type optimization algorithms , e . g . the Broyden-Fletcher-Goldfarb-Shanno ( BFGS ) algorithm 47 , 48 , for which the Hessian is iteratively approximated from the gradient during optimization ., In principle , the exact calculation of the Hessian and Hessian-Vector products is possible via second order forward and adjoint sensitivity analysis 49 , 50 , which possess similar scaling properties as the first order methods ., However , both forward and adjoint approaches come at an additional cost and are thus not considered in this study ., To assess whether the use of adjoint sensitivities for optimization is still viable , we compared the performance of the interior point algorithm using adjoint sensitivity analysis with the BFGS approximation of the Hessian to the performance of the trust-region reflective algorithm using forward sensitivity analysis with Fisher Information Matrix as approximation of the Hessian ., For both algorithms we used the MATLAB implementation in fmincon . m ., The employed setup of the trust-region algorithm is equivalent to the use of lsqnonlin . m which is the default optimization algorithm in the MATLAB toolbox Data2Dynamics 7 , which was employed to win several DREAM challenges ., For the considered model the computation time of forward sensitivities is comparable in Data2Dynamics and AMICI ., Therefore , we expect that Data2Dynamics would perform similar to the trust-region reflective algorithm coupled to forward sensitivity analysis ., We evaluated the performance for the model of ErbB signaling based on 100 multi-starts which were initialized at the same initial points for both optimization methods ., For 41 out of 100 initial points the gradient could not be evaluated due numerical problems ., These optimization runs are omitted in all further analysis ., To limit the expected computation to a bearable amount we allowed a maximum of 10 iterations for the forward sensitivity approach and 500 iterations for the adjoint sensitivity approach ., As the previously observed speedup in gradient computation was roughly 48 fold , we expected this setup should yield similar computation times for both approaches ., We found that for the considered number of iterations , both approaches perform similar in terms of objective function value compared across iterations ( Fig 4a and 4b ) ., However , the computational cost of one iteration was much cheaper for the optimizer using adjoint sensitivity analysis ., Accordingly , given a fixed computation time the interior-point method using adjoint sensitivities outperforms the trust-region method employing forward sensitivities and the FIM ( Fig 4c and 4d ) ., In the allowed computation time , the interior point algorithm using adjoint sensitivities could reduce the objective function by up to two orders of magnitude ( Fig 4c ) ., This was possible although many model parameters seem to be non-identifiable ( see S1 Supporting Information Section 4 ) , which can cause problems ., To quantify the speedup of the optimization using adjoint sensitivity analysis over the optimization using forward sensitivity analysis , we performed a pairwise comparison of the minimal time required by the adjoint sensitivity approach to reach the final objective function value of the forward sensitivity approach for the individual points ( Fig 4e ) ., The median speedup achieved across all multi-starts was 54 ( Fig 4f ) , which was similar to the 48 fold speedup achieved in the gradient computation ., The availability of the Fisher Information Matrix for forward sensitivities did not compensate for the significantly reduced computation time achieved using adjoint sensitivity analysis ., This could be due to the fact that adjoint sensitivity based approach , being able to carry out many iterations in a short time-frame , can build a reasonable approximation of the Hessian approximation relatively fast ., In summary , this application demonstrates the applicability of adjoint sensitivity analysis for parameter estimation in large-scale biochemical reaction networks ., Possessing similar accuracy as forward sensitivities , the scalability is improved which results in an increased optimizer efficiency ., For the model of ErbB signaling , optimization using adjoint sensitivity analysis outperformed optimization using forward sensitivity analysis ., Mechanistic mathematical modeling at the genome scale is an important step towards a holistic understanding of biological processes ., To enable modeling at this scale , scalable computational methods are required which are applicable to networks with thousands of compounds ., In this manuscript , we present a gradient computation method which meets this requirement and which renders parameter estimation for large-scale models significantly more efficient ., Adjoint sensitivity analysis , which is extensively used in other research fields , is a powerful tool for estimating parameters of large-scale ODE models of biochemical reaction networks ., Our study of several benchmark models with up to 500 state variables and up to 1801 parameters demonstrated that adjoint sensitivity analysis provides accurate gradients in a computation time which is much lower than for established methods and effectively independent of the number of parameters ., To achieve this , the adjoint state is computed using a piece-wise continuous backward differential equation ., This backward differential equation has the same dimension as the original model , yet the computation time required to solve it usually is slightly larger ., As a result , finite differences and forward sensitivity analysis might be more efficient if the sensitivities with respect to a few parameters are required ., The same holds for alternatives like complex-step derivative approximation techniques 51 and forward-mode automatic differentiation 28 , 52 ., For systems with many parameters , adjoint sensitivity analysis is advantageous ., A scalable alternative might be reverse-mode automatic differentiation 28 , 53 , which remains to be evaluated for the considered class of problems .,

Introduction, Methods, Results, Discussion

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation ( ODE ) models has improved our understanding of small- and medium-scale biological processes ., While the same should in principle hold for large- and genome-scale processes , the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far ., While individual simulations are feasible , the inference of the model parameters from experimental data is computationally too intensive ., In this manuscript , we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks ., We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology ., Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis ., The computational complexity is effectively independent of the number of parameters , enabling the analysis of large- and genome-scale models ., Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods ., The proposed method will facilitate mechanistic modeling of genome-scale cellular processes , as required in the age of omics .

In this manuscript , we introduce a scalable method for parameter estimation for genome-scale biochemical reaction networks ., Mechanistic models for genome-scale biochemical reaction networks describe the behavior of thousands of chemical species using thousands of parameters ., Standard methods for parameter estimation are usually computationally intractable at these scales ., Adjoint sensitivity based approaches have been suggested to have superior scalability but any rigorous evaluation is lacking ., We implement a toolbox for adjoint sensitivity analysis for biochemical reaction network which also supports the import of SBML models ., We show by means of a set of benchmark models that adjoint sensitivity based approaches unequivocally outperform standard approaches for large-scale models and that the achieved speedup increases with respect to both the number of parameters and the number of chemical species in the model ., This demonstrates the applicability of adjoint sensitivity based approaches to parameter estimation for genome-scale mechanistic model ., The MATLAB toolbox implementing the developed methods is available from http://ICB-DCM . github . io/AMICI/ .

applied mathematics, simulation and modeling, algorithms, optimization, genomic databases, mathematics, genome analysis, research and analysis methods, genome complexity, biological databases, differential equations, biochemistry, biochemical simulations, database and informatics methods, genetics, biology and life sciences, physical sciences, genomics, computational biology

journal.pcbi.1002588

Modeling Within-Host Dynamics of Influenza Virus Infection Including Immune Responses

Despite vaccines and antiviral agents , influenza A virus infection remains a major public health problem worldwide ., Seasonal and pandemic influenza results in approximately 3 to 5 million cases of severe illness and approximately 250 , 000 to 500 , 000 deaths worldwide 1 ., Influenza viruses primarily infect and replicate in epithelial cells 2 ., The immune response to influenza virus infection plays an important role in controlling the virus within a host ., The nonspecific innate immune response provides the first line of defense , which reacts immediately upon infection and involves generating a variety of chemotactic , proinflammatory and antiviral cytokines 3 ., An important cytokine produced during the innate immune response is type I interferon ( mainly IFN-α/β ) ., IFN-α/β has been shown to stimulate resistance to infection in the neighboring cells by inducing the expression of many IFN-stimulated gene products , including antiviral proteins , such as protein kinase R , PKR 4 ., Depletion of key IFN signaling proteins in mice results in greater mortality , accompanied by systemic ( as opposed to respiratory-restricted ) infection 5 ., In addition , IFN is able to activate immune system cells , such as natural killer ( NK ) cells , during the early stage of infection , which can destroy infected cells 6–10 ., The secretion of IFN-α/β by infected epithelial cells is also important for the initiation of the antigen-specific adaptive immune response 11 , 12 , which in mice takes approximately 5 days to begin in the lung 13 ., The adaptive immune response mainly consists of cytotoxic CD8+ T cells eliminating infected cells and antibodies neutralizing the virus 11 ., It is important for clearing the virus and provides immunity against future influenza virus infections ., Because of limited information about influenza pathogenesis and the host immune response in humans , various animal models , such as mice , ferrets , and horses 14–17 , have been used to obtain a better understanding of the biological mechanisms underlying viral control ., A number of mathematical models have been developed to study the dynamics of influenza virus infection and immune responses 13 , 18–28 ( also see recent reviews in 29–31 ) ., By fitting a simple viral dynamic model to the data derived from 6 experimentally infected human volunteers , Baccam et al . 20 showed that target cell limitation can explain the kinetics of influenza A virus infection in humans ., Both innate 18 , 20 , 28 and adaptive immune responses 21 , 22 , 24 have also been incorporated into the basic model to evaluate the effect of immune responses on viral control ., In a recent study , Miao et al . 13 quantitatively investigated the innate and adaptive immune responses to primary influenza A virus infection in mice ., They compared the half-life of infected epithelial cells and free virus before and during a virus-specific immune response ( about 5 days post-infection ) ., Lee et al . 27 developed a two-compartment model to study the contributions of different factors , such as antigen presentation and activation of naive T and B cells , CD4+ T cell help , CD8+ mediated cytotoxicity , and antibody , to the control of influenza A virus infection ., These studies provide a quantitative understanding of the host immune response in controlling virus replication ., The relative contributions of target cell availability and immune responses to viral control remain unclear ., In a recent study , Saenz et al . 19 estimated the numbers of viral-antigen-positive cells in the lungs of ponies at days 2 . 5 , 4 . 5 , and 5 . 5 after challenge with equine influenza virus ( EIV ) ., The result indicated that up to 5% of bronchiole cells were infected at any one time , yielding an estimated total cell loss of about 27% by the end of the infection ., This suggests mechanisms for viral control in addition to target cell depletion 20 , and motivates the development of a model that includes a strong innate immune response to explain the clearance of virus during infection 19 ., However , the model in 19 is unable to capture a number of important features of the viral kinetics observed in 6 ponies , e . g . , the viral peak in most of the ponies , the rapid and substantial viral decline after the peak ( 2 to 4 log decline within 1 day ) , and a short plateau phase in which the viral load remained unchanged or even experienced a minor second peak in some ponies 19 ., In this study , we develop mathematical models based on several possible biological mechanisms that attempt to explain all of these observations ., Our objective is to investigate which biological parameters can give rise to the viral load change observed during an uncomplicated influenza virus infection ., The data we studied were from an experimental challenge of 6 unvaccinated ponies infected with EIV A/eq/Kildare/89 ( H3N8 ) 16 ., Nasal secretions ( NS ) were collected daily for 10 days post-challenge and number of copies of influenza virus RNA per milliliter ( ml ) was quantified ., Blood samples were also collected to quantify the fold changes in cytokine expression including IFN for days 1 through 5 post-challenge compared to the day prior to challenge ., We used both the viral load and the IFN fold change data in this study ., High antibody titers were detected by the single radial haemolysis ( SRH ) assay 14 days post-challenge in the horses ., Upon infection , the viral load increased rapidly and reached its peak at day 2 for all ponies ., There was a wide variation in the peak level ., The highest was approximately 108 copies of viral RNA/ml of NS ( pony 2 ) , while the lowest was 104 copies/ml of NS ( pony 6 ) ., After the peak , the viral load experienced a rapid and substantial decline ( about 2 to 4 logs within 1 day ) ., All the ponies had a viral plateau and some experienced a minor but obvious second peak ., After the viral plateau/second peak , there was a second viral decline starting around day 6 ., In 4 out of the 6 ponies , the viral load decreased to below the detection limit by day, 8 . The rest of the ponies had undetectable viral load at day, 9 . During the infection , IFN expression increased substantially reaching a peak on day 2 in 5 of the 6 ponies , followed by a rapid decrease to the pre-infection level 16 , 19 ., The peak of IFN-fold change ranged from approximately 1 ( pony 3 ) to more than 10 ( pony 6 ) ., We developed a model to study the within-host dynamics of EIV infection in horses ., It is described by the following system of equations ( 1 ) The model has five variables: target cells ( T ) , productively infected cells ( I ) , uninfected cells that are refractory to infections ( R ) because of IFN-induced antiviral effect 32 , free virus ( V ) , and IFN ( F ) ., The term βVT represents the rate of infection when virus encounters susceptible target cells ., IFN induces an antiviral effect and enables uninfected cells to become refractory to infection at rate ., Cells in the refractory state revert back to the susceptible state at rate ρ ., Infected cells are assumed to die at per capita rate δ ., Prior to the emergence of the antigen-specific adaptive immune response , we assume δ is a constant δI ., This rate ( δ ) becomes δA\u200a=\u200aδm- ( δm-δI ) e−σ ( t-μ ) after the adaptive immune response emerges , where μ is the time at which the adaptive immune response emerges , δm is the maximum death rate of infected cells in the presence of an adaptive immune response , and σ determines how fast the death rate increases from δI to the saturation rate δm ., Because we only model the dynamics for a few days after the adaptive immune response emerges , we modify the time-varying death rate to δA\u200a=\u200aδIeσ ( t-μ ) without using the maximum constant δm ., In this way , the number of parameters introduced is reduced by, 1 . Another method that explicitly includes the adaptive immune response as an additional variable in the model was also examined and the results are mentioned in the Discussion section ., In the early stage of influenza virus infection , NK cells can be activated by IFN to induce cytolysis of infected epithelial cells and play an important role in the innate immune response 6 , 7 , 8 , 9 , 10 ., Here , we assume the number of activated NK cells is proportional to the level of IFN and use the mass action term to represent the killing by NK cells ., Note that killing by NK cells is an important , but not the only factor leading to the loss of infected cells ., Cytokines or proteins released by other cells such as macrophages 33 during the innate immune response can also promote increased lung epithelial apoptosis following influenza virus infection 34 , 35 ., Infected cells are assumed to produce virus at rate p and free virus is cleared at rate c per virion ., As in the previous models by Baccam et al . 20 and Saenz et al . 19 , loss of virions due to infection has been neglected ., Since an infected cell may produce as many as 20 , 000 virions 36 , the loss of one virion to produce an infected cell can be neglected ., IFN is secreted by infected cells at rate q and decays at rate d ., A schematic diagram of Eq ., ( 1 ) is shown in Figure, 1 . Variables and parameters are summarized in Table, 1 . We fixed some parameters and estimated the rest by fitting the model to both the viral load and IFN data ., The lifespan of infected cells prior to the emergence of the adaptive immune response , 1/δI , was fixed to 0 . 5 days 31 , 37 , which is the value used in previous modeling studies 19 , 21 ., Because no CD8+ T cell data were obtained in this experiment , we chose the time at which the adaptive immune response emerges ( μ ) according to the second viral decline ., For example , we chose μ\u200a=\u200a7 days for pony 1 and μ\u200a=\u200a6 days for pony, 2 . A similar method has been used previously in analyzing acute HCV infection kinetics in chimpanzees 38 ., We also included a delayed adaptive immune response explicitly in the model and obtained similar results ( see Discussion ) ., The initial population of epithelial cells in the equine respiratory tract was fixed at T0\u200a=\u200a3 . 5×1011 cells 39 ., We assume all such cells are target cells , as used in Saenz et al . 19 , although H3N8 viruses prefer to infect α 2 , 3 sialic acid glycan-expressing cells 40 and thus the number of target cells could be less than assumed here ., We include sensitivity test to a number of parameters including the initial number of target cells below ., We set the initial population of infected cells and refractory cells to 0 , and the initial IFN fold change to 1 , i . e . , no change , as given in the data set ., The remaining parameters were estimated from data fitting ., Note that some parameters , such as the infection rate constant β and the viral production rate p , do not have physiological values because they are in the unit of ml of nasal secretions ., We fit the model to both the viral load and IFN data of each pony using the commercial software package Berkeley Madonna ( Version 8 . 3 . 18 ) ., The obtained parameter values were based on the best nonlinear least squares fit of the model equations to the data set , i . e . , the program minimized the root mean square ( RMS ) between data points and the corresponding model predictions , given by ( 2 ) where the number of viral load and IFN fold change measurements for an individual pony are denoted by nV and nF , respectively ., Viral load data is given by and the analogous value given by our model is Vi ., Similarly , the measured IFN fold change is and the corresponding model prediction is Fi ., The first data point below the detection limit ( 100 copies/ml of NS ) was assumed to be 1 copy/ml of NS ., Other values , such as half of the detection limit , can also be used 41 , which will affect the estimate of the parameter σ in this study ., There are also other approaches to incorporating left-censored measurements 42 ., We did not include the viral load data under the detection limit after the first undetectable data point ., Equal weights for both viral titer and IFN data were employed because they are approximately in the same range ., Using different weights or normalized data ( each value is divided by the maximum ) generates a similar fit , although the estimates of parameter values can be different ., The target cell limited model was used in 20 and described by the following equations: dT/dt\u200a=\u200a−βVT , dI/dt\u200a=\u200aβVT-δI , and dV/dt\u200a=\u200apI-cV ., Assuming tpeak is the time at which the viral load achieves its peak , we have pI\u200a=\u200acV at t\u200a=\u200atpeak ., Thus , I ( tpeak ) =\u200acV ( tpeak ) /p ., Because target cells are nearly depleted around the peak of infection in this model 20 , we assumed T≈0 for a short time period after tpeak , and solved for I ( t ) ., This assumption was also used in 23 to obtain an approximation for the decay after the peak using the model with an eclipse phase ., The solution is ., Substituting this into the V ( t ) equation and solving for V ( t ) , we have ., Thus , the predicted viral load reduction 1 day after the peak is ., As c is typically much larger than δ ( Table 2 ) , this ratio is mainly determined by the value of δ ., For δ in the range of ( 0 , 4 . 5 ) day−1 , which covers most of the estimates in the literature 20 , the ratio is always greater than 0 . 01 for any positive value of c ., This implies that for any value of δ<4 . 5 day−1 , the target cell limited model generates <2 log decline within 1 day after the peak ., The actual viral load reduction predicted by the model should be less than this approximation because we assumed T≈0 over the interval tpeak , tpeak+1 ., Numerical results show that to obtain a 3 log decline within 1 day after the peak , c should be >12 day−1 and δ needs to be >8 day−1 ., To attain a 4 log decline , c should be >18 day−1 and δ needs to be >10 day−1 ., To statistically compare the best fits using model 1 ( Eq ., ( 1 ) ) and model 2 ( setting κ to 0 in model 1 , i . e . , no killing of infected cells by NK cells ) , we performed an F-test ., An F-test is used to compare two nested models used to fit the same data set to determine whether the model with more parameters statistically improves the fit ., The improvement is considered to be statistically significant if the p-value is less than 0 . 05 ., We begin with the calculation of the F-value as follows:where RSS is the sum of squared residuals between model predictions and data ., The RMS value generated from Berkeley Madonna is the root of the mean squared residuals ., Hence , RSS\u200a=\u200an• ( RMS ) 2 , where n is the number of data points ., The subscripts 1 and 2 represent model 1 and model 2 , respectively ., The degree of freedom associated with RSS is df\u200a=\u200an-m , where m is the number of fitted parameters ., Note that μ , the time at which the adaptive immune response emerges , was counted as a fitted parameter although we fixed it according to the second viral decline ., To compute the p-value , we calculated the F distribution evaluated at the F-value with ( df2-df1 , df1 ) degrees of freedom ., Comparison between models was performed individually for all the ponies ., We fit the predicted values of V ( t ) and F ( t ) in Eq ., ( 1 ) to the viral load and IFN ( fold change ) kinetic data , respectively , of each pony ., The best fits , shown in Figures 2 ( red solid ) and 3 ( blue solid ) , indicate that Eq ., ( 1 ) agrees with both the viral load and IFN data well ., Parameter values corresponding to the best fits are given in Table 2 ., Note that the estimates of some parameters , such as the infection rate β and the viral production rate p , have large variations ., This is expected because there is a large variation ( up to 4 logs ) in the peak viral load of the 6 ponies ., We also fit the model to the average data of the 6 ponies ( Figures 2 and 3 ) ., The average data show similar kinetic changes of viral titer and IFN , and the best-fit model agrees well with the data ., For comparison , we also plotted the best fits ( dashed lines in Figures 2 and, 3 ) of the Saenz et al . model 19 to the same viral load and IFN data ., Our model improves the viral load data fits in several aspects ., First , our fits capture the viral peak in all 6 ponies ., Second , the fits achieve the rapid and substantial viral decline within 1 day after the peak in all ponies ., Third , the fits generate a period of viral plateau and/or a second peak ., Lastly , our fits generate the rapid second viral decline to below the detection limit in all 6 ponies ., Detailed explanations and possible biological mechanisms for these viral load changes are given below ., The viral loads in all 6 ponies experienced a 2 to 4 log decline within 1 day after the peak 16 , 19 ., Similar viral declines were also observed in 6 volunteers experimentally infected with influenza A virus 20 ., What causes such a rapid and substantial viral decline within a short period of time ?, The data fits using both the target cell limited model in 20 and the modified model in 19 did not capture this feature ., In fact , using the target cell limited model we can derive an approximation of the viral load reduction 1 day after the peak ( see Materials and Methods ) ., For most of the estimates of the infected cell death rate in the literature , the target cell limited model cannot generate a >2 log decline within 1 day after the viral peak ., This suggests that other factors not included in the target cell limited model may be responsible for this dramatic viral decline ., We tested different models based on several possible biological mechanisms ( see below ) and found that the model shown in Eq ., ( 1 ) can reproduce the viral load change observed in the 6 ponies ., The rapid viral decline after the peak is mainly due to the combination of two factors: the decline of target cells because of their conversion to the refractory class ( in Eq ., 1 ) by IFNs antiviral effect , and the killing of infected epithelial cells ( in Eq . 1 ) , possibly mediated by IFN activated NK cells during the innate immune response ., We plotted the changes of uninfected target cells ( solid blue ) , infected cells ( solid green ) , refractory cells ( dashed red ) , and total cells ( dotted black ) in Figure 4 ., The number or percentage of infected epithelial cells is low compared to the prediction of the target cell limited model 20 ., In contrast with the predictions of the Saenz et al . model 19 , the level of uninfected target cells remains high ( >1010 cells ) for all the ponies during the entire infection course ., The reversion of cells from the refractory to the susceptible class ( ρR ) prevents uninfected target cells from decreasing to a very low level ., This suggests that in addition to target cell depletion , cytolysis of infected cells mediated by IFN activated cells such as NK cells during the innate immune response may be responsible for the viral decline during the early stage of influenza virus infection ., To further test if a model that only includes the refractory class without NK cell-mediated infected cell killing ( in Eq ., 1; referred to as model, 2 ) can explain the first rapid viral decline , we fit model 2 to the same experimental data ( dashed lines in Figure 5 for viral load and Supporting Figure S1 for IFN fold change ) ., We found model 2 cannot generate the rapid viral load decline after the peak ., We also tested a model assuming that IFN only reduces the viral production rate ( i . e . , assuming and replacing p with in Eq ., ( 1 ) ; this is referred to as model, 3 ) and found this model could not generate the first rapid viral decline either and yielded dynamics very similar to model 2 ( dotted lines in Figure 5 ) ., Thus , the cell-mediated lysis of infected cells during the innate immune response plays a critical role in generating the first rapid viral decline in our model ., We calculated the error between modeling predictions and experimental data ( RMS ) for different models ., The RMS values are given in Table 3 ., Model 1 generated the smallest error for each pony ., We compared the best fits of using model 1 and model 2 by performing an F-test , which determines which one of the two nested models provides a better data fit from a statistical standpoint ( Materials and Methods ) ., The results given in Table 3 show that model 1 provides significantly better fits for ponies 2 and 3 ( with the p-value<0 . 05 ) ., For the other ponies , the F-test shows that there is a statistical trend supporting model 1 ( with the p-value from 0 . 1 to 0 . 4 ) ., We also compared the best fits using the modified Akaike Information Criterion ( AICc ) ( Supporting Text S1 ) ., Model 1 is supported over model 2 for each pony ( Table S4 ) ., We did not statistically compare the fits of model 1 with the Saenz et al . fits 19 because the objective functions minimized during data fitting are different ., Saenz et al . 19 incorporated the percentage of infected cells in their fitting ., We did not include this because the data of the percentage of infected cells were from a different study 43 ., The errors listed in Table 3 and the fitted curves ( Figures 2 and, 3 ) show that our fits improve those using the Saenz et al . model ., The phenomenon of bimodal viral titer peaks in most ponies 16 was also observed in other studies with influenza virus infection 44 , 45 , 46 ., The target cell limited model 20 and the Saenz et al . model 19 cannot generate bimodal virus titer peaks ., Adding the effect of IFN and a time delay in its production into the target cell limited model was shown to be able to generate bimodal peaks 20 ., However , the fits obtained by Baccam et al . 20 using this model did not agree well with the data ., Our fits using model 1 generated an obvious bimodal behavior ( Figure 2 ) ., The level of IFN peaked around day 2 and then declined rapidly ( Figure 3 ) , concordant with the emergence of viral plateau/second peak ( Figure 2 ) ., Thus , the viral plateau and the second viral titer peak can be explained by the loss of the IFN-induced antiviral effect ( in Eq . 1 ) ., Increased availability of susceptible cells due to reversion from the refractory state ( ρR in Eq ., 1 ) can also contribute to the viral plateau/second peak ., From our data fits we estimated that the rate ( ρ ) at which refractory cells ( R ) revert from the refractory to the susceptible state is on average 2 . 6 per day ., The reversion rate is also important in preventing uninfected target cells from decreasing to a very low level ., Sensitivity tests of the model predictions to a number of parameters , including and ρ , are given below ., We examined the sensitivity of the predicted viral load of pony 1 to several parameters , including , ρ , κ , and p ( Figure 6 ) ., More sensitivity tests of the predicted viral load and IFN to other parameters and contour plots are presented in Supporting Figures S2 , S3 , S4 , S5 , S6 , S7 , S8 ., Sensitivity tests show that the IFNs antiviral efficiency ( ) and the reversion rate ( ρ ) are important in generating the viral plateau and the second peak ( Figure 6A , B ) ., A large value of can also yield a rapid first viral decline ., However , this will eliminate the viral plateau and the second peak ( Figure 6A ) ., Increasing the infected cell killing rate constant alone will decrease the first viral peak and increase the second peak ( Figure 6C ) ., A large value of the viral production rate p ( Figure 6D ) or the infection rate β ( Figure S2 ) can achieve the first viral peak ., However , they will significantly reduce the time for the viral titer to reach the peak ., These sensitivity tests suggest that the cell-mediated lysis of infected cells ( κ ) and the IFNs antiviral effect ( ) during the innate immune response are the major factors responsible for the first rapid viral decline and subsequent viral plateau/second peak ., Since the initial number of target cells of H3N8 virus infection could be less than 3 . 5×1011 cells ( T0 ) , the estimate of total epithelial cells in the equine respiratory tract 39 , we reduced it from T0 to 75% or 50% of T0 ., The simulation in which the other parameters are assumed to be unchanged shows that a small initial number of target cells can delay the time to reach the first viral peak , reduce the magnitude of the peak viremia , and eliminate the viral plateau ( Figure S2 ) ., However , data fitting using 75% and 50% of T0 still generates good fits to the experimental data ( see Figure S2 for the fit to the viral load data of pony 1 ) ., The biological factors responsible for viral control during influenza virus infection remain unclear ., Earlier work 20 suggested that the viral decline after the peak could be explained by a limitation in the availability of target cells ., However , a recent study by Saenz et al . 19 estimated that <5% of epithelial cells are infected at any one time and that the total epithelial cell loss is <30% by the end of the infection ., They modified the target cell limited model by including an IFN-induced antiviral state of uninfected cells 19 ., However , their modified model is still essentially a target cell limited model — uninfected target cells move to the refractory class , causing the depletion of susceptible cells and hence the viral titer declines after reaching the peak ., Numerical simulations also confirmed this prediction ( Figure 3 in 19 ) ., As we analytically showed in Materials and Methods , the target cell limited model cannot generate a rapid and substantial viral decline after the peak unless a very large death rate of infected cells is chosen ., However , only increasing the death rate of infected cells will decrease the first peak and eliminate the viral plateau/second peak , which is observed in all the 6 ponies ., In this paper , we developed a new model ( Eq ., ( 1 ) ) and showed that cytolysis of infected cells mediated by cytokines and cells such as NK cells during the innate immune response , can explain the rapid viral decline after peak ., During an early stage of infection , NK cell activity contributes to a rapid termination of many virus infections , including influenza , before the onset of the adaptive immune response 11 , 47 , 48 , 49 , 50 ., Several studies in mice have illustrated that depletion of NK cells resulted in increased morbidity and mortality from influenza infection 51 , 52 , 53 ., In humans , severe/lethal 2009 H1N1 influenza virus infection in 3 cases was associated with reduction of NK cells rather than effector CD8+ T cells 54 , and influenza vaccination led to increased levels of NK cells with activation markers CD56 and CD69 55 ., NK cells are not only responsible for producing antiviral cytokines , but they are also directly involved in destroying virus-infected cells via the recognition by the natural cytotoxicity receptors ( NCR ) NKp46 ( NCR1 in mice 6 ) and NKp44 7 , 8 , 9 , 10 ., Gazit et al . 6 showed that influenza virus infection was lethal in mice when the NK receptor NCR1 was knocked out ., In our model , we assumed that the level of activated NK cells is proportional to that of IFN , whose levels were measured in the study 16 ., There is evidence supporting that NK cells have similar dynamics to IFN and virus during influenza virus infection ., For example , an experimental study on murine influenza virus infection 56 showed that the effector cells with the properties of NK cells had very similar dynamics to the IFN level changes , i . e . , peaked at 1–2 days post-infection and decreased to low levels by day 6 ., In mice that were inoculated intranasally with the mouse-adapted strain of human influenza A/PR/8/34 ( H1N1 ) virus , the timing of viral peak and subsequent decline was consistent with that of NK cell-mediated cytolysis 57 ., Another study 58 also showed that the peak of NK cells occurred within the first several days after influenza virus infection in mice , consistent with the timing of IFN production ., In addition to the killing by IFN activated NK cells , high expression of cytokines during the innate immune response may also lead to infected cell death 34 ., For example , influenza A virus-stimulated apoptosis was shown to be enhanced by IFN α/β and by increased expression of the antiviral protein PKR 35 ., Macrophage-derived TRAIL ( tumor necrosis factor-related apoptosis-inducing ligand ) also plays an important role in promoting epithelial cell apoptosis 33 ., We used IFN as a proxy of the innate immune response to model the cell-mediated lysis of infected epithelial cells and the antiviral effect ., This may not be accurate because a number of other cytokines are involved in the innate immune response ., Dendritic cells ( DCs ) and macrophages produce large amounts of antiviral and immunostimulatory cytokines in response to influenza virus infection 2 , 4 , 59 , 60 , 61 ., We assumed that IFN is secreted by epithelial cells once they are infected ., Other cells , such as monocytes , macrophages , and plasmacytoid DCs , can also contribute to IFN production 4 , 37 , 62 ., Further , there may exist a time delay in IFN production , as observed in pony 1 ( Figure 3 ) in which viral titer/infected cells peaked at day 2 post-infection while IFN peaked at day 3 post-infection ., A similar time lag was observed in mice with influenza virus infection 63 ., Moltedo et al . 63 showed that the initiation of lung inflammation ( generation of IFNs , cytokines , chemokines , etc ) did not begin until almost 2 days after infection , when virus replication reached its peak ., This delay may be mediated by the influenza-encoded NS1 protein 63 , which can act to block IFN production in influenza infected cells 48 , 64 , 65 ., The burst of IFN production after day 2 might be explained by activation of plasmacytoid DCs or other uninfected cells in the lung , which are activated to a degree that correlates with viral titer or number of infected cells ., Future comprehensive models may wish to take macrophages , DCs and other cytokines into account ., However , more complicated models should be accompanied with appropriate data for model verification ., After the rapid post-peak decline of viral titer , we observed a plateau phase and/or the second viral peak ., Although a number of models have been developed to study within-host influenza virus dynamics , very few models can generate the second peak ., As the innate immune response weakens ( Figure 3 shows that a rapid IFN decay was observed in all ponies even when the viral load was still high ) , the killing of infected cells ( ) lapses in our model ., Thus , the level of infected cells can remain unchanged for a while or even increase ., This can explain the viral plateau and the second viral increase ., Another factor leading to the second peak is the augmented availability of target cells ., The rapid IFN decay significantly reduces the conversion of susceptible cells to the refractory class ., Because cells are most likely unable to maintain the antiviral state for a long time without continued IFN signaling , those cells that are already in the refractory class will revert back to the susceptible state and become the target of virus infection again ., This will enhance the viral production ., Some other factors may also contribute to the second peak ., For example , when virus spreads to a previously uninvolved site in the lung or respiratory tract as discussed in 20 , viral infection and production will increase and may lead to a second viral load increase ., After reaching the second peak around day 6 post-infection , the viral titer underwent a rapid second viral decline to below the detection limit ., We showed that this second viral decline can be generated by

Introduction, Materials and Methods, Results, Discussion

Influenza virus infection remains a public health problem worldwide ., The mechanisms underlying viral control during an uncomplicated influenza virus infection are not fully understood ., Here , we developed a mathematical model including both innate and adaptive immune responses to study the within-host dynamics of equine influenza virus infection in horses ., By comparing modeling predictions with both interferon and viral kinetic data , we examined the relative roles of target cell availability , and innate and adaptive immune responses in controlling the virus ., Our results show that the rapid and substantial viral decline ( about 2 to 4 logs within 1 day ) after the peak can be explained by the killing of infected cells mediated by interferon activated cells , such as natural killer cells , during the innate immune response ., After the viral load declines to a lower level , the loss of interferon-induced antiviral effect and an increased availability of target cells due to loss of the antiviral state can explain the observed short phase of viral plateau in which the viral level remains unchanged or even experiences a minor second peak in some animals ., An adaptive immune response is needed in our model to explain the eventual viral clearance ., This study provides a quantitative understanding of the biological factors that can explain the viral and interferon kinetics during a typical influenza virus infection .

Influenza , commonly referred to as the flu , is a contagious respiratory illness caused by influenza virus infections ., Although most infected subjects with intact immune systems are able to clear the virus without developing serious flu complications , the mechanisms underlying viral control are not fully understood ., In this paper , we address this question by developing mathematical models that include both innate and adaptive immune responses , and fitting them to experimental data from horses infected with equine influenza virus ., We find that the innate immune response , such as natural killer cell-mediated infected cell killing and interferons antiviral effect , can explain the first rapid viral decline and subsequent second peak viremia , and that the adaptive immune response is needed to eventually clear the virus ., This study improves our understanding of influenza virus dynamics and may provide more information for future research in influenza pathogenesis , treatment , and vaccination .

nonlinear dynamics, mathematics, theoretical biology, viral transmission and infection, population modeling, virology, immunology, biology, computational biology, microbiology, viral load, immune response

journal.pntd.0001876

Controlling Dengue with Vaccines in Thailand

Dengue is a mosquito-borne disease , caused by a flavivirus with four serotypes , responsible for an estimated 500 , 000 hospitalizations and 20 , 000 deaths per year , mostly in the tropics 1 , although these are probably conservative estimates ., The toll of dengue may rise with the increasing range of its primary vectors , Aedes aegypti and A . albopictus , because of climate change and increasing urbanization in the developing world ., Severe dengue cases ( i . e . , dengue shock syndrome ( DSS ) and dengue hemorrhagic fever ( DHF ) ) occur primarily among children 2 ., Although the mortality rate for dengue cases is low , even uncomplicated dengue fever causes considerable suffering and loss of productivity despite its short duration 3–5 ., Because vector control has achieved only limited success so far in reducing the transmission of dengue 6–8 , an effective tetravalent vaccine against all four dengue serotypes may be the only means to effectively control dengue ., Such a vaccine could drive dengue rates to very low levels , as has the vaccine against yellow fever , which is also caused by flavivirus 9 ., Since urban and sylvatic dengue transmission are not tightly linked 10 , it is not inconceivable that dengue could be eliminated in urban areas with the targeted use of a highly efficacious vaccine ., Several dengue vaccine candidates are currently in development or in clinical trials 11–13 ., Once vaccine becomes available , initially there will not be sufficient quantities to cover the up to 2 . 5 billion people at risk 1 ., Vaccine will need to be introduced gradually , allowing evaluation of vaccine effectiveness and safety 14 ., To reduce disease burden most efficiently with a limited supply of vaccine , it may be necessary to prioritize certain geographic regions or age groups for vaccination while taking into account the constraints of government vaccination programs and finances ., However , with up to four competing dengue serotypes 15–17 , seasonal vectors 18 , 19 , complex and potentially harmful immune responses to infections with heterologous serotypes 20–22 , and the difficulty in formulating a tetravalent vaccine that protects against all four serotypes 13 , 23 , it is important to anticipate how the deployment of such vaccines will affect dengue virus transmission , and morbidity and hospitalizations caused by the disease 23–25 ., Here , we investigate the potential effectiveness of different dengue vaccination strategies using a model of dengue transmission in a Thai population ., The individual-level stochastic model was developed to match the epidemiology of dengue in a population in semi-rural Thailand that has experienced hyperendemic dengue transmission for many years ., We modeled both single-year campaigns , in which part of the population is vaccinated well before the dengue season , and multi-year roll-outs , in which young children are vaccinated first and progressively older individuals are vaccinated in subsequent years as part of a catch-up campaign ., We developed an agent-based model of dengue transmission ., The model is described in detail in Text S1 ., In brief , the model uses a synthetic population based on the demography of Ratchaburi , Thailand ., In the model , individual humans spend time at home , work , or school , and can be susceptible , exposed , infectious , or recovered with respect to each of the four dengue serotypes ., Uninfected mosquitoes , which can not transmit dengue , reside in buildings until they become infected by biting a viremic human host , at which point the mosquito may travel among nearby buildings ., Exposed mosquitoes become infectious to humans after an extrinsic incubation period and remain infectious until they die ( Figure 1A ) ., Humans are immune to all serotypes for 120 days after recovering from infection ., After 120 days , they are susceptible to serotypes to which they had not been exposed 26 ., Secondary cases may have severe outcomes ( i . e . , DSS/DHF ) at an age-specific proportion ( Text S1 ) ., Secondary infections are otherwise treated the same as primary infections in the model except that viremia resolves one day faster 27 ., We describe the synthetic population created for the model in detail in Text S1 ., Briefly , the model represents a 2030 km area surrounding Bang Phae , Ratchaburi , Thailand ( Figure 1B ) ., We populate each square kilometer with households to match population density estimates 28 ., The households are randomly drawn from the household microdata from the census of Ratchaburi province ., By drawing households from census microdata , we obtain realistic age and gender distributions both within the households and in the overall population ( Text S1 ) ., The synthetic population has 207 , 591 individuals ., Within each square kilometer , individual households , schools , and workplaces are assigned random locations ., Children of the appropriate age are sent to the elementary school ( ages 5 to 10 years ) , lower secondary school ( ages 11 to 14 years ) , or upper secondary school ( 15 to 17 years ) ., People of the appropriate age are assigned workplaces according to a gravity model in which people tend to commute to locations that are nearby and have a relatively high population density ., Workplaces have an average of 20 workers , who occupy the same location during the workday ., During the morning and evening hours , people are at home , and they may go to school or work during the rest of the day ( Figure 1C ) ., Individuals symptomatically infected with dengue may stay at home until they recover ., One consequence of this behavior is that there is more dengue transmission in households than at workplaces when dengue is symptomatic ., Mosquitoes tend to stay in the same location ( i . e . , house , workplace , or classroom ) , but may migrate to adjacent locations with a fixed probability per day ( Figure 1D and Text S1 ) ., Occasionally , the simulated infected mosquitoes will migrate to a random distant location to account for occasional long-distance travel ., Because simulated mosquitoes migrate to adjacent locations with the same probability regardless of distance , they will travel farther in more sparsely inhabited regions ., To simulate multi-year epidemics , we make two simplifying assumptions:, 1 ) there is no correlation of prior exposure to dengue within households and, 2 ) household structures do not change over time ., After simulating a single year of dengue transmission , we “age” the population by setting the immune status ( both prior infections and vaccination ) of all individuals of age to that of randomly selected individuals of age and resetting the immune status ( to nave ) of all people less than 1 year old ., In other words , the population and households stay constant , while the immune statuses of individuals are transferred or reset each year ., Thus , we account for the fact that older people will have more exposure to dengue as the simulation runs over multiple years ., This approach introduces a few potential problems ., One might expect changes in population structure , that could lead to the an age shift in dengue cases 29 ., Therefore , to minimize the effects assuming a constant population structure , we do not run the model beyond ten years ., The advantage of our approach is that the complex dynamics of household structures such as births , deaths , and marriages do not need to be included in the model ., These processes are extremely difficult to simulate realistically but would be required to maintain plausible age distributions within households , schools , and the workforce ., Also , the correlation of immune statuses within households and within geographic areas is disrupted in the multi-year model 30 ., It also makes it impossible to trace the immune history of an individual person , since an individuals prior exposure to dengue and vaccination history will be copied from a randomly selected younger person each year ., However , the population-level history of exposure to the circulating strains of dengue will be correct ., In the model , individuals are assigned to have immunity from prior exposure to the four serotypes of dengue based on their age ., The age-specific immune profile is based on two sources of data on the prevalence of serotypes in Thailand ., Thailands Ministry of Public Health releases an “Annual epidemiological surveillance report” that summarizes dengue serotype surveillance data ., Reports from 2000–2009 are available at epid . moph . go . th , which we summarize in Table S1 ., For 1973–1999 , we use data from a surveillance study based on children hospitalized at the Queen Sirikit National Institute of Child Health in Bangkok , as published in 31 ( Table S2 ) ., Although we should be cautious about concatenating data from different sources , many of the cases reported to the Ministry of Public Health are 10–14 years old , so the populations in these two datasets are reasonably comparable ., We estimate the age-specific immunity to the four dengue serotypes in our model ., We assume that the level of exposure to dengue each year was such that 11% of nave individuals would be infected , based on studies in nearby Vietnam 32 , 33 ., To determine the contribution of the four serotypes to this constant annual exposure to infection , we estimate the relative prevalence of the 4 serotypes by combining the Thailands Health Ministrys national data from 2000–2009 ( available at http://epid . moph . go . th ) and Queen Sirikit National Institute of Child Health in Bangkok from 1973–1999 31 ( Figure 2 ) ., For each of the years for which we have serotype prevalence estimates , we randomly selected 11% of the population who was alive in that year ( i . e . , was 0 years old or older ) to be exposed to dengue , and for each individual simulated exposure to a single serotype drawn from that years prevalence data ., Individuals exposed to a serotype are considered to be permanently immune ., For years before 1973 , we performed the same procedure , except that we assumed that the serotype prevalence was the mean serotype prevalence from 1973–2009 ., The mean serotype prevalences are 9 . 8% , 14 . 6% , 7 . 5% , and 5 . 2% for DENV-1 , DENV-2 , DENV-3 , and DENV-4 , respectively ., In other words , we assumed that there was a constant 11% exposure to dengue ( sufficient to infect ) for all individuals , regardless of age or immune status , and that exposure to a serotype at any point in an individuals past grants sterilizing immunity to that serotype ., In other words , each person who is exposed to dengue each year is exposed to exactly one serotype of dengue , and he or she gains sterilizing immunity to that serotype if he or she was not already immune from prior exposure ., Because the four serotypes have different symptomatic fractions , surveillance data give a skewed representation of the number of individuals infected by each serotype ., We re-scaled the number of cases for each of the four serotypes in the historical data as described in Text S2 ., By scaling the historical surveillance data , the population-level immunity to the four serotypes changes , with increased levels of immunity to less pathogenic serotypes than if the unadjusted surveillance data were used ., Figure 3 shows the age-specific immunity to dengue in the synthetic population ., We simulated a single year of dengue transmission in Ratchaburi , Thailand ( Figure 1B ) ., Dengue seasonality was simulated by modeling the monthly mosquito population to conform to mosquito count data from Thailand ( Text S2 ) ., To seed the epidemic , we randomly selected two people to expose to each of the four dengue serotypes for each simulation day ( i . e . , eight total per day , or 1 . 4% of the population per year ) ., Pre-existing immunity protects many of these individuals ( Figure 3 ) , so only a few actually become infected each day ., This constant seeding represents the repeated introduction of dengue from neighboring unvaccinated regions and prevents dengue from being eradicated in the model ., Simulated epidemics peak in July–August ( Figure 4A ) , about two months later than the peak in the mosquito population , which is in May–June ( Text S2 ) ., This delay of dengue activity after mosquito activity is consistent with observations 34–36 ., The lag is caused by the long mean generation time , i . e . , time between when one human infects another through infected mosquitoes , of 24 days ( Text S2 ) ., The simulated dengue season produced a 5% infection attack rate with some stochastic variation among runs ( Table 1 ) ., Because of age-specific immunity from prior exposure ( Figure 3 ) , most of the infections occur in children ( Figure 5A ) ., The 1 . 7% dengue illness attack rate is consistent with the estimated 2% observed in children in Ratchaburi in the 2006–2007 season 37 , 38 ., There were 39 severe cases requiring hospitalization per 100 , 000 individuals in a simulated dengue season , primarily among school-aged children ( Figure 5C ) ., The age distribution of severe cases is largely a consequence of the high inherent risk of severe outcome upon secondary infection for this age group as described in Text S1 ) ., We report the total number of uncomplicated and severe ( DSS/DHF ) cases produced by our model assuming perfect surveillance ., Estimates of reporting rates would be needed to compare our modeling results with actual surveillance data ., Wichmann et al . estimated that , among children , total dengue cases in Thailand may be underreported by a factor of 8 . 7 and severe ( inpatient ) dengue cases by 2 . 6 , with less underreporting in school-aged children than in younger children 37 ., Underreporting among adults is likely higher 39 but is difficult to quantify due to the lack of prospective cohort or active surveillance studies that include adults 25 , 40 ., The age distribution of symptomatic cases produced by our model is older than we had anticipated ( Figure 5B ) ., This discrepancy may be due to underreporting of adult dengue cases by routine surveillance , which would skew the age distribution downward ., It is also possible that the model overestimates cases among older individuals ., Antibodies from exposure to multiple serotypes may be cross-protective , so third and fourth dengue infections may be rare or only mildly symptomatic 41 ., The model is sensitive to changes in the maximum permissible infection parity ( Text S3 ) ., Reducing the maximum infection parity to two or three not only greatly reduces the attack rate , but also shifts the age distribution of cases downward ., During the simulated seasonal peak of dengue transmission , a single person infected an average of 1 . 9 to 2 . 3 others , depending on the serotype ( Text S2 ) ., This is the reproductive number , , a measure of transmissibility that takes the background of immunity from prior exposure into account ., A rough estimate of the critical vaccination fraction to stop transmission in the population , assuming a randomly mixing population , is , where is the vaccine efficacy against infection 14 ., For example , a vaccine with 70% for all four serotypes would have a critical vaccination fraction of about 80% , while a vaccine with 90% would have a fraction of 60% ., Although these figures give a crude starting point for thinking about what level of vaccine coverage may be needed to eliminate dengue in a population , more detailed calculations are needed , as described below ., We simulated vaccinating the population to protect them before a single dengue season ., Recently , an observer-blind , randomized , controlled , phase 2b vaccine trial was conducted with a tetravalent dengue vaccine 38 ., The serotype-specific estimated vaccine efficacy for confirmed dengue illness ranged from 55–90% for serotypes 1 , 3 and 4 , but was close to 0% for serotype 2 ., Partially based on this , we investigate the with a point estimate of 70% for all four serotypes , and we assumed that vaccine-derived immunity does not wane ., We do sensitivity analyses with the ranging for 50–90% and with the set to zero for a single serotype ., This vaccine candidate has been tested in 1–45 year-olds and requires three courses administered over the course of one year 12 ., If one conservatively assumes that an individual is only protected after receiving all three doses , then only those 2 years and older could be protected by vaccine ., Therefore , in the simulation results presented below , we simulated the vaccination of individuals between 2 and 46 years old ., Vaccinating 70% of children 2 to 14 years old would reduce the number of dengue infections by 48% , uncomplicated dengue fever cases by 41% , and severe dengue cases ( DSS/DHF ) by 54% in a single year ( Table 1 and Figure 4B ) ., The proportion of uncomplicated cases prevented is lower than the proportion of infections because infected children are less likely to become symptomatic with dengue fever than adults ( Text S1 ) , but the proportion of severe cases prevented is higher than the proportion of infections because children are more likely to develop DSS or DHF upon secondary infection than adults ( Figure 5 and Text S1 ) ., Because children from ages 2 to 14 years comprise only 22 . 2% of the population , vaccinating them does not reach the estimated 80% coverage required to control dengue ., Extending the vaccination to include adults up to 46 years old reduced the number of infections by 82% , dengue fever cases by 81% , and severe cases by 83% ( Table 1 ) ., Vaccinating 70% of individuals aged 2 to 46 years would result in 52% coverage of the total population ., Thus , vaccinating 70% of this population greatly reduces the seasonal peak ( Figure 4C ) , while vaccinating a smaller fraction of this population is less effective ., Simulations in which the vaccine had higher efficacy produced better , but similar , results ( Text S4 ) ., However , a vaccine that protects against only three of the four serotypes is substantially less effective than one that offers good protection against all four ( Text S4 ) ., Because the four serotypes compete in our model , reduction in the circulation of three of the serotypes could result in increased transmission of the remaining serotype , at least in the short term ., Those who are not vaccinated receive indirect protection when enough of the remaining population is vaccinated ., In our simulations , those who are over 46 years old are never vaccinated , but people in this age group were 44% , 61% , and 71% less likely to become infected when 30% , 50% , and 70% of those from ages 2 to 46 years were vaccinated ., Unvaccinated individuals from ages 2 to 46 were 60% , 80% , and 91% less likely to become infected when 30% , 50% , and 70% of this age cohort were vaccinated ., Certain age groups could be prioritized to receive vaccine ., Younger people have the least prior exposure , so they would be the most likely to become infected with and transmit dengue ., Simulations demonstrated that vaccinating children ( 2–14 years old ) would reduce dengue infections in the total population more than using the same number of doses to cover both children and adults ( 2–46 years old ) ( Figure 6A ) ., However , dengue is more likely to be symptomatic in older individuals than younger ( Text S1 ) ., Thus , the advantage of concentrating vaccine in children was less pronounced when observing symptomatic dengue ( Figure 6B ) ., Children are more likely than adults to have severe outcomes ( DSS/DHF ) upon secondary dengue infection ( Text S1 ) , and vaccinating children was more effective in reducing severe cases than vaccinating adults ( Figure 6C ) ., For example , vaccinating 70% of children from ages 2 to 14 years would reduce the overall severe case rate to 18 . 0 per 100 , 000 , compared to 22 . 8 per 100 , 000 if the same number of individuals from ages 2 to 46 years were vaccinated ., Vaccinating 70% of those from ages 15 to 46 years would reduce the overall severe case rate to 13 . 7 per 100 , 000 , compared to 10 . 6 per 100 , 000 if the same number of individuals from ages 2 to 46 were vaccinated ., In other words , concentrating vaccine among children should reduce hospitalizations more than vaccinating both children and adults ., Due to limited vaccine availability and the logistics of mass vaccination programs , dengue vaccine will probably be deployed in multi-year vaccine roll-out campaigns 42 ., We simulated a vaccine roll-out that covers only children , reaching 70% of children from ages 2 to 14 years within three years , after which point only 2-year-olds are vaccinated ., Specifically , we simulated the vaccination of 70% of 2 to 4 year olds in the first year , 2 year olds and 6 to 9 year olds in the second year , 2 year olds and 11 to 14 year olds in the third year , then only 2-year-olds for the following six years , as shown in Figure S1A ., The incidence of dengue infections drops sharply for the first three years , after which incidence declines slowly ( Figure 7B ) ., We also simulated a vaccine roll-out that extended the catch-up to include adults up to age 46 ., This roll-out targets the same age groups for the first three years as the previously described roll-out , but after this point both 2-year-olds and the youngest four unvaccinated age cohorts are vaccinated , as shown in Figure S1B ., Including young adults in the catch-up caused the incidence of dengue to continue dropping rapidly after children were covered by the third year ( Figure 7C and Table 2 ) ., For the roll-out that includes adults , 7 , 699 uncomplicated cases and 217 severe cases per 100 , 000 people at risk would be averted by vaccination over a ten-year period ., We used a dengue simulation model to estimate that vaccination of 50% of the population of rural Thailand could be sufficient to reduce local dengue transmission to low levels ., Based on our modeling study , we conclude that at least 70% efficacy against infection for all serotypes is desirable if one wants to control dengue in a hyperendemic area , and a higher efficacy vaccine would require less careful targeting of vaccine to reduce community-wide transmission of dengue ., We further showed that vaccinating children is the most efficient use of vaccine to reduce cases and hospitalizations , but control of dengue transmission would also require vaccinating adults ., In addition , both vaccinated and unvaccinated people would receive protection from mass vaccination because of the considerable indirect effects of dengue vaccination ., A vaccine that only protects against only three serotypes could lead to a significant reduction in overall vaccine effectiveness ., Further work will be needed in order to understand how to use vaccines that may not protect against all four serotypes ., Using a detailed model of dengue transmission allows one to explore strategies that target vaccines most efficiently ., To capture the complex interactions required to evaluate the effectiveness of mass vaccination with tetravalent dengue vaccines , the model includes vector population seasonality 34 , 43 , human mobility 44 , 45 , population heterogeneities , and individual vectors 19 ., Thus , we have a coherent framework for modeling both dengue transmission and the effects of vaccination in a complex population ., The model by necessity includes a number of assumptions and simplifications , such as the model structure , parameterization , and vaccine efficacy ., The model may be sensitive to assumptions we made regarding unresolved questions about dengue immunology , such as the susceptibility of individuals after sequential infection by more than two serotypes ( Text S3 ) ., Although our model qualitatively captures the epidemic dynamics of a single season of dengue in semi-rural Thailand , there are complex multi-year dynamics that we can only approximate ., More realistic modeling of the prevalence cycles of the four dengue serotypes would require more complex and calibrated inter-serotype interactions ( e . g . , 15 , 46 ) , and further studies are needed to quantify these effects ., Furthermore , our results apply to dengue transmission in a hyperendemic area , which has a high incidence of dengue and multiple circulating serotypes ., In regions with lower transmission , the levels of population immunity to the various serotypes and the force of infection would be lower , resulting in different effectiveness of mass vaccination ., Models that require a great deal of regional data such as ours may need to be adapted to the specific regions of interest to produce useful results ., However , our model agrees with previous model-based estimates that 50–85% of a population need to be vaccinated to reduce transmission to negligible levels 47 ., Therefore , our model produces results qualitatively similar to those from simpler models that assume homogeneous mixing of the human population ., An estimated 40% of the worlds population is at risk of dengue infection 48 , and vaccinating this population is not feasible in the short term ., The greatest need for dengue control is in areas where dengue disease is hyperendemic , primarily South-east Asia , Latin America , and the Caribbean and Pacific Islands ., A coalition of non-governmental organizations , national health ministries , and vaccine manufacturers could establish priorities for allocating vaccine in publicly funded mass vaccination campaigns ., Private demand might be sufficient to cover enough of the remaining population to reduce dengue transmission to manageable levels 49 ., Large-scale vaccination campaigns would be both challenging and costly but could be more cost-effective than relying solely on vector control and other non-pharmaceutical interventions 50 , 51 ., Vaccination would not only reduce local disease burden , but may reduce the rate of evolution of dengue viral genetic changes ., Education campaigns and aggressive vector control measures could complement vaccination if it is not feasible to vaccinate enough individuals to eliminate local dengue transmission ., However , such non-pharmaceutical strategies are difficult to sustain , and there have been doubts about their effectiveness 6–8 , 52 ., Novel vector control strategies that involve releasing parasitic bacteria or genetically engineered mosquitoes are promising 53–56 , but their deployment may be controversial ., Given the difficulty of controlling dengue with currently available technologies , we believe that vaccination will become an essential component of dengue reduction efforts .

Introduction, Methods, Results, Discussion

Dengue is a mosquito-borne infectious disease that constitutes a growing global threat with the habitat expansion of its vectors Aedes aegyti and A . albopictus and increasing urbanization ., With no effective treatment and limited success of vector control , dengue vaccines constitute the best control measure for the foreseeable future ., With four interacting dengue serotypes , the development of an effective vaccine has been a challenge ., Several dengue vaccine candidates are currently being tested in clinical trials ., Before the widespread introduction of a new dengue vaccine , one needs to consider how best to use limited supplies of vaccine given the complex dengue transmission dynamics and the immunological interaction among the four dengue serotypes ., We developed an individual-level ( including both humans and mosquitoes ) , stochastic simulation model for dengue transmission and control in a semi-rural area in Thailand ., We calibrated the model to dengue serotype-specific infection , illness and hospitalization data from Thailand ., Our simulations show that a realistic roll-out plan , starting with young children then covering progressively older individuals in following seasons , could reduce local transmission of dengue to low levels ., Simulations indicate that this strategy could avert about 7 , 700 uncomplicated dengue fever cases and 220 dengue hospitalizations per 100 , 000 people at risk over a ten-year period ., Vaccination will have an important role in controlling dengue ., According to our modeling results , children should be prioritized to receive vaccine , but adults will also need to be vaccinated if one wants to reduce community-wide dengue transmission to low levels .

An estimated 40% of the worlds population is at risk of infection with dengue , a mosquito-borne disease that can lead to hospitalization or death ., Dengue vaccines are currently being tested in clinical trials and at least one product will likely be available within a couple of years ., Before widespread deployment , one should plan how best to use limited supplies of vaccine ., We developed a mathematical model of dengue transmission in semi-rural Thailand to help evaluate different vaccination strategies ., Our modeling results indicate that children should be prioritized to receive vaccine to reduce dengue-related morbidity , but adults will also need to be vaccinated if one wants to eliminate local dengue transmission ., Dengue is a challenging disease to study because of its four interacting serotypes , seasonality of its transmission , and pre-existing immunity in a population ., Models such as this one are useful coherent framework for synthesizing these complex issues and evaluating potential public health interventions such as mass vaccination .

medicine, public health and epidemiology, viral vaccines, infectious disease epidemiology, applied mathematics, immunology, microbiology, computerized simulations, vaccines, mathematics, population biology, vaccination, epidemiology, biology, computer science, clinical immunology, immunity, virology

journal.pcbi.0040038

Modeling an Evolutionary Conserved Circadian Cis-Element

In flies and mammals , circadian timing is controlled via interlocked transcriptional feedback loops that rely on basic helix-loop-helix ( bHLH ) , PAS domain transcription factors 1 , 2 ., In both fly and mammalian systems an evolutionary conserved bHLH heterodimer acts as the central transcriptional activator ., The pair is called CLOCK 3 and CYCLE 4 in Drosophila , while the mammalian orthologues are CLOCK 5 and BMAL1 6 ., In mammals the CLOCK paralog NPAS2 can substitute for CLOCK function in the suprachiasmatic nucleus 7 , 8 ., Like most transcription regulators of the bHLH family members , DNA binding of the CLK/CYC or CLOCK/BMAL1 pairs has been shown to involve canonical CANNTG E-box sequences 9–11 both in flies and mammals 6 , 12 ., However , the low information content of this motif does not provide a sufficient explanation for the specificity of gene induction by the CLOCK transcription factor , nor does it allow to build a model that can predict clock regulated transcripts on a genome-wide scale ., Both the possibility of informative nucleotides flanking the E-boxes or the possibility that a combination of closely spaced partner signals could contribute cooperatively to the specificity was considered in flies and mammals 13 , 14 ., Either mechanism can in theory significantly increase binding affinity of CLK/CYC to DNA , e . g . an increase in total ΔG0 of 1 kcal/mol from one additional good hydrogen bond raises binding affinity by a factor of 5 ., In Drosophila , the best-studied enhancer is that of the period ( per ) gene where a 69-bp fragment upstream of the transcription start site ( TSS ) drives circadian gene expression 9 ., This enhancer depends on a canonical E-box , but it was also shown that its immediate 3 flank contributes to drive large amplitudes and tissue specific expression 15 ., Interestingly , the fly enhancer can also be activated by the murine CLOCK/BMAL1 complex 6 ., The next best studied enhancer is that of the timeless ( tim ) gene 11 which harbors closely spaced E and TER boxes , the latter being a variant of the consensus E-box which coincides with the mammalian E-box 16 ., In the mouse , well-studied CLOCK/BMAL1 elements include the Per1 6 , Per2 17 , Avp 14 and Dbp 18 genes ., A study of the Avp promoter suggested that CLOCK/BMAL1 enhancers use a combination of a canonical E-box and a second more degenerate version thereof 14 ., More recently a pyrimidine-rich 22 nucleotides sequence was found to cooperate with the core E-box in the Avp promoter 19 ., So far , however , it was not possible to compile this information to build a predictive algorithm for CLK/CYC or CLOCK/BMAL1-activated enhancers ., Computational strategies for the optimal discovery of cis-elements from genomic sequence pose formidable algorithmic challenges 20 ., Among the many ways to model transcription factor binding sites , position weight matrices ( PWMs ) reflect most closely the biophysics of protein-DNA interactions 21–23 ., Recent algorithms that exploit phylogeny to infer PWMs apply probabilistic ( Gibbs ) sampling to evolutionary models 24–26 , or implement expectation maximization to optimize scoring schemes that incorporate phylogeny 27–30 ., Most of these methods allow for relatively simple model architectures , mostly single block motifs or symmetric structures 31 ., Hidden Markov Models ( HMMs ) 32 and their phylogenetic extensions 33 , 34 are best suited for more complex model structures like the one we use ., The phylogenetic HMMs currently focus on optimizing trees 33 rather than motif identification; the latter would require optimizing the state dependent equilibrium frequencies ., However conventional HMMs , for which motif training is well established , can be supplemented with a weighting scheme approximating the phylogenetic dependencies 35 , 36 , which is what we will use here ., Our analysis starts with the five known CLK/CYC targets among the clock genes in Drosophila , per 9 , 10 , 37 , tim 38 , vrille ( vri ) 39 , Par-domain protein 1 ( Pdp1 ) 40 , and clockwork orange ( cwo ) ( formerly CG17100 ) 38 , 41 , 42 ., Starting from the 69-bp enhancer in the period gene , we found a cis-element that is both common to all five genes and highly conserved among Drosophila species ., This enhancer , which we validate using functional data , not only refines the core circadian E-box ( E1 ) , but also incorporates a flanking partner element ( E2 ) that resembles the more degenerate E-box discussed above , and which is found at a very specific distance of the core E-box with an uncertainty of one nucleotide ., While such structures are not implemented in common motif discovery programs , they are conveniently modeled with hidden Markov models ( HMMs ) 32 ., We thus trained such an HMM model from the available fly sequences ., Remarkably , the Drosophila model was able to predict many known mammalian CLOCK/BMAL1 targets without modification and with high specificity ., A deeper phylogenetic analysis revealed the presence of the cis-element throughout insects and vertebrates ., This shows that despite important differences in the organisms clock architectures , e . g . , rhythmic mRNA accumulation of Clock in flies versus Bmal1 in mammals , an ancient element in the circadian cis-regulatory code has been maintained since their common ancestor 500 million years ago ., The 69-bp enhancer upstream of the per promoter in D . melanogaster was discovered and dissected in great detail 9 ., Using genome sequences from 12 Drosophila species 43 , 44 , we searched for presence of this enhancer in this clade ( Figure 1 ) ., Although not immediate to find ( in current UCSC alignment the enhancer is absent in half of the species ) , we identified sequences in all species that show remarkable conservation in a ∼25 bp subfragment tightly collocated around the central canonical E-box motif ( Figure 1A ) ., The subfragment harbors a half E-box ( GTG ) located 9 bp to the right of the central E-box in the species close to D . melanogaster , and 10 bp for more remote clade members , e . g . D . grimshawi ., Moreover the subfragment contains the 18 bp E-box 10 and the 3 flanking regions showing the strongest attenuation in activity upon deletion 15 ., We then searched for similar flanking signals in the vicinity of other conserved E-boxes near promoters of validated CLK/CYC targets ., We noticed that all five known target genes contain such dimeric signals that can be aligned with the per enhancer ( Figure 1B ) , and also that this particular signal is conserved in all species considered ., To make this more systematic we focus on the vicinity of all conserved E-boxes that can be found around the TSSs of the circadian transcripts per-RA , tim-RA , Pdp1-RD , vri-RA , and cwo-RA ., We used multiple alignments from the UCSC browser ( http://genome . ucsc . edu/ ) and considered all islands of ± 30 bp around degenerate CANNGT sequences that were present at least in the subclade consisting of D . melanogaster , D . yakuba , D . simulans , D . sechellia , and D . erecta ( in total about 660 nucleotides per gene for each species , available at http://circaclock . epfl . ch/training_seqs . fa ) ., While conservation often extends to all 12 species , sub-optimal alignments required that we apply this milder criterion ( cf . alignment of per , Figure S3A ) ., A preliminary motif finding analysis of this restricted set of sequences based on the MEME algorithm 45 ( using motifs length of 7 ) confirmed the presence of E-box-like dimers in these sequences ( Figure S1 ) ., These were spaced with an accuracy of plus or minus one base pair as in the per enhancer ( Figure 1A ) ., To model this configuration we implement a HMM reflecting the dimer structure ( Figure 2A ) , and train the emission probabilities from the example sequences using the Baum-Welsh algorithm 32 ., The model is cyclic so that several instances of the motifs can occur per sequence , we also allow to by-pass E2 in the case that it would not be sufficiently supported by the training sequences ., We seeded the model only with one E-box ( Figure 2B , left ) flanked by a weak T nucleotide to break the palindrome symmetry of the bare E-box , while the putative partner site ( E2 ) is initialized with a fully uninformative model ., Only the emissions are trained while the transition probabilities p1 from background to E1 , and p2 form E1 to E2 are held fixed ( Methods ) ., These transitions tune the stringency of the E1 and E2 parts , and reflect the chemical potential of the regulators that would bind to the E1 and E2 boxes 23 ., We varied p1 and p2 over a wide range and retained the combination that maximizes the enrichment of hits among genes that show induction by CLK in functional genomics assays ( Figure 3 ) ., Importantly , despite the uninformative seed and large search space , converged models do reflect the right flank described above for a wide range of transitions , the combination retained ( p1=2−11 , p2=2−4 ) show a AACGTG right consensus ., Apart from details in the emission probabilities , this model is quite stable for a range of p1 and p2 values ( Figure S2 ) ., Inspection of the converged model indicates that effectively 15 high scoring instances of E1 box were used , and 6 for the E2 box ., The latter were from vri ( 2–3 instances ) , per ( 1–2 ) , tim ( 1 ) , Pdp1 ( 1 ) and cwo ( 1 ) ., In these five genes it is noticeable that multiple E1-E2 copies are found , and that E1 also often occurs alone ( Figure S3 ) ., For instance , the second conserved site in the per intron ( Figure S3A ) could provide an explanation for the promoterless per allele found to cycle in a restricted part of the nervous system 46 ., Thus , the converged model is consistent with the attenuated CLK/CYC activation in mutated 69-bp enhancers with deletions that are immediately 3 of the right central E-box 15 ., Furthermore the model captures the mammalian architecture in which a canonical and a fuzzier E-box are juxtaposed 14 ., Training a model on five genes raises the question about its generalization to further putative CLK/CYC targets ., To address this we used several microarray datasets that measure ‘CLK targetness 38 ( Methods ) and assessed correlation with sequence match from our model ., Windows of ±2500 bp around all annotated TSSs were scanned with our HMM model , in which the five training genes were found among the first 13 highest scores ( Figure 3B ) ., Recently a glucocorticoid receptor-CLK fusion protein ( GR-CLK ) was used in S2 cells and cultured fly heads to induce CLK targets under cycloheximide treatment 38 ., In this assay new protein synthesis is blocked to minimize indirect effects ., Even though it is not formally excluded that the fusion protein could interfere with partner complexes , this experiment is best suited to test the specificity of the sequence model ., We show that highly induced genes in the GR-CLK experiment are significantly enriched in high scoring hits from the sequence model , so that we can identify a set of ∼30 genes among the top 57 induced genes which show highly significant 2- to 6-fold enrichment in sequence specificity ( Figure 3A and Table S1 ) ., Importantly , the five training genes are excluded from the set of positives in this analysis ., When testing how much E2 contributes to the observed enrichment , we found that it contributes only marginally: it reduces specificity for low sensitivities and increases specificity at higher sensitivities ( Figure S4A ) ., Nonetheless , several of the highly induced genes in the GR-CLK experiment , e . g . , CG13624 , show presence of E1-E2 ., Moreover , these sites show highly increased conservation profiles specifically at the predicted locations including the E2 site ( Figure S3F and S3G ) ., Below we show that increased specificity from E2 is most important in mammals ., We also considered expression levels in ClkJrk flies 47 , 48 since CLK/CYC targets are predicted to be down-regulated in this mutant ., Moreover we tested cycling transcripts in light-dark ( LD ) and dark-dark ( DD ) conditions with phases that are compatible with known CLK/CYC targets , i . e . , peak time accumulations in windows ZT6–20 ( Methods ) ., No signature of enriched E1-E2 motifs was detected in either the ClkJrk or cycler datasets ( Figure S4 ) ., This can be expected since both differential expression in ClkJrk mutants , or rhythmic mRNA accumulation , also reflect indirect mechanisms downstream of the CLK/CYC transcription factor ., We extensively searched whether other p1 and p2 parameters would detect enrichment without success ., Consistently , we do not detect enrichment of the motif in mouse transcripts showing differential expression in a recent mRNA profiling of Clock mutants 49 ( Figure S5 ) ., Similarly , in an early study of rhythmic transcript profiles in fly heads , we did not detect enrichment of consensus E-boxes in the vicinity of periodic transcripts 50 ., Further annotating the list of 57 GR-CLK induced genes with the sequence score from the E1-E2 model , the 24-hour periodicity and phase of the transcripts in LD and DD , or with the differential regulation in ClkJrk flies show that some genes qualify as CLK/CYC regulated genes according to several independent criteria ( Table S1 ) ., Among those , the C2H2 zinc finger transcription factor cbt , CG3348 , CG11050 , CG8008 are the most noticeable ., From the purely genomic side , conserved E1-E2 sites are enriched in D . melanogaster when compared to permuted E1-E2 matrices ( Figure S6A and S6B ) ., From the likelihood scores of known examples , we estimate about one hundred genes to be potentially controlled by medium to high affinity E1-E2 sites ( Figure S6C , gene lists in at http://circaclock . epfl . ch/fly_conserved_16 . txt ) ., Even though the model was derived from fly sequences , the core E-box shows similarities to the brain-specific in vitro measured NPAS2/BMAL1 binding consensus GGGTCACGTGTTCCAC ( underlined bases are consistent with our model ) 51 ., Scanning the mouse genome with the full E1-E2 model taken straight from the flies revealed that many common circadian transcripts show instances of this signal that are highly conserved in mammals ( Figure S7 ) ., Several of these genes also contain multiple instances of the motif , as in the flies ., With few exceptions , sites are found in the vicinity of the core promoter ( e . g . , Per2 , Tef ) or in the introns ( Dbp , Cry2 , RevErbα ) ., Given the much greater complexity of mammalian genomes as compared to insects , it comes as a great surprise that the fly model predicts known circadian genes in mouse with highly enriched specificity ( Figure 4 ) ., Among the 13 common circadian genes used as a test set , we find 7 among the top 1% of predictions when we would expect none ( p < 10−12 , binomial distribution ) ., In addition the restriction to sites that are highly conserved in mammals ( measured using PhastCons 52 ) increases the specificity ( compare Figures 4 and S8 ) ., From the scores of known examples , we thus estimate in the order of hundred CLOCK/BMAL1 binding sites in mouse ( Figure S6D ) ., Finally , the two spacer lengths were about equally represented among the conserved hits with scores above 15 bits ( given at http://circaclock . epfl . ch/bedFiles ) ., Importantly , while the E2 sequence played a marginal role in the specificity analysis of the GR-CLK data in flies , it plays a much more prominent role in mouse ., For example , the Dbp site ranks only at position 804 and that of Per2 at position 3021 when E2 is not used in the prediction ( Figure S8 , right ) ; overall the 13 test genes are clearly shifted to the bulk of scores ., The conservation pattern of many of these hits shows tight increase around the E1-E2 sequences ( Figure S7 ) , which further supports the functional role of the predicted loci ., Moreover , several of these predictions coincide with known CLOCK/BMAL1 functional circadian enhancers , e . g . , those in the Per1 6 , Per2 17 or Dbp 18 genes ., As with the Drosophila ClkJrk data , putative CLOCK/BMAL1-induced genes identified from a Clock mutant array experiments in mice 49 did not show enriched E1-E2 boxes presumably due to indirect effects , except perhaps for a weak tendency in the liver ( Figure S5 ) ., Consistent with our model , recent circadian band shift assays with mouse liver extracts indicate that a sequence closely related to the E1-E2 site is able to shift the CLOCK/BMAL1 complex more specifically than single E-boxes 53 ., Finally , the phase distribution among the conserved hits that cycle in liver 54 shows a clear phase preference around ZT12 , as expected for CLOCK/BMAL1 targets ( Figure 4B ) ., We first provide a phylogenic analysis of the activators CLOCK/BMAL1 binding E1 in mammals , birds , frogs , fishes , flies , mosquito and honey bee ., Beyond these species , notably in the nematodes , no orthologues can be found ., Both CLOCK and BMAL1 harbor two conserved PAS domains , in addition to the preserved DNA binding bHLH domain ( Figure 5A; full-length protein alignments are given at http://circaclock . epfl . ch/jarFiles ) , whose conservation exceeds by far the bHLH consensus motif 55 , 56 ., As the complex is expected to bind the E1 site , its conservation is consistent with the high information content ( 11 . 0 bits ) of the E1 motif ., To track the presence of the E1-E2 motif in a broader set of species , we consider two gene families among the best conserved circadian CLK/CYC or CLOCK/BMAL1 targets ., First , the Period genes are primary targets of CLOCK/BMAL1 whose genes products function as repressors of CLOCK/BMAL1 , hence closing a negative feedback loop at the core of the circadian oscillator ., While flies have a single period gene , vertebrates have multiple copies , e . g . , three in mammals ., The presence of E1-E2 signals near promoters of period genes generalizes beyond flies and mammals to a broad set of species including birds , frogs , fishes , flies , mosquito and honey bee ( Figure 5B ) ., While the mammalian site is at the TSS and that of fly is around −500 bp , the fish promoter is unannotated and the site is at 2 . 6 kbp upstream of the annotated PER3 protein ., Interestingly the mammalian E2 motif shares many similar bases with the fish ., Even though nematodes have a putative period homologue ( lin-42 ) , we could not detect presence a proximal E1-E2 in C . elegans and C . briggsae , which is both consistent with the absence of CLOCK/BMAL1 and the still uncertain existence of circadian rhythms in nematodes 57 ., Second , the PAR-domain basic leucine zipper ( PAR bZip ) transcription factors Tef/Hlf/Dbp ( mouse ) are homologues of the fly circadian gene Pdp1 ( PAR domain protein 1 ) and are prominent clock output genes directly regulated by E-box motifs 12 , 18 ., Their function is to mediate rhythmic physiology in organs such as the liver and kidney , where they induce , e . g . , the cytochrome P450 enzymes 58 ., Among the three murine paralogues , Tef is the most ancient representative with putative orthologues in most vertebrates and insects ., In few species , e . g . , in zebrafish and Xenopus tropicalis , full-length mRNA are available for Tef , elsewhere we relied on annotations inferred from a combination of ESTs and proteins ( from other species ) to genome alignments provided in the UCSC web browser ., We could find E1-E2 elements in the vicinity of the Tef promoter in most of the vertebrates and insects , some harboring several copies ( Figure 5C ) ., Interestingly , the locations of the instances of the E1-E2 motif shows a typical conservation structure ( in the PhastCons scores ) in subgroups where non-coding sequences can be multiply aligned , i . e . , the mammals , the fishes , and the flies ., Even if the exact position of the TSS is poorly documented in many of these species , we find that more than 85% of the shown sequences for both the Period and Tef genes occur within 1 . 5 kbp of an annotated start ., Furthermore , 75% ( respectively 25% ) of the likelihood scores are above 15 . 1 bits ( respectively 19 . 5 bits ) and the median score is at 17 . 1 bits ., Using background statistics for the E1-E2 likelihood score computed as in 23 ( Figure S9 ) , we estimate that the probability per position to find a motif having a likelihood score greater than 17 bits is 5 × 10−7 , or 2 × 10−6 for scores of 15 bits ., Assuming independent positions , we estimate that the probability p to find conserved hits ( PhastCons > 0 . 5 ) in regions of 1 . 5 kbp around the mammalian , fish and insect promoters is p = 2 × 10−9 for 17 bits hits and p = 10−7 for 15 bits ., Here we used that the genomic fraction of conserved sites ( PhastCons > 0 . 5 ) is 10% in mammals ( UCSC mm8 assembly , PhastCons score based on 18 species ) , 23% in fish ( fr2 assembly , 4 species ) , and 40% flies ( dm3 , 15 species ) ., This simple calculation thus suggests that the conserved configurations found for the Period and Tef genes are highly unlikely due to chance ., Even though novel post-transcriptional mechanisms regulating the circadian clockworks are regularly uncovered 59 , transcriptional control remains an essential ingredient of molecular clocks that is particularly relevant for relaying circadian output functions 2 ., Output genes can be induced by the transcription factors of the core oscillator , or via tissue specific effectors such as Dbp , Hlf and Tef in mouse , which are themselves direct CLOCK/BMAL1 targets 58 ., This layered design complicates the interpretation of experiments such as mRNA steady state time courses , particularly if one is interested in deciphering new direct targets of the core regulators ., This task can be greatly facilitated using functional experiments like the glucocorticoid-CLK fusion experiments , which have improved specificity compared with the profiling of mutants , and accurate models for the cis-regulatory sequences bound by the regulators ., Presently the mechanisms that facilitate the recruitment to DNA and subsequent trans-activating activity of the main circadian regulator CLK/CYC or CLOCK/BMAL1 are not fully understood ., Likely though , this situation will evolve rapidly , helped by approaches such as large-scale chromatin immuno-precipitation analyses or comparative genomics ., We used the latter to derive a probabilistic model for CLK/CYC-regulated circadian enhancers consisting of two partner signals , E1 and E2 , linked by a spacer that can tolerate a variability of one nucleotide ., E1 has an E-box core flanked by informative Ts ( or A on the reverse strand ) , while the second half is more degenerate and resembles previously reported TER boxes 11 or E boxes 16 ., The close proximity of the two sites suggests a cooperative binding of two partner complexes , one of which is the CLK/CYC heterodimer , while the second possibly identical factor needs to be identified ., To validate the predictive power of the model in Drosophila , we analyzed a recent study in which a GR-CLK fusion was used to induce CLK/CYC targets in S2 cells ., We found an unusual number of high sequence scores among the highest induced genes , even though the E2 part did not contribute a large improvement in this case ., This could reflect two scenarios: either the fusion protein interferes with a putative E2 binding complex , or it could simply be that the list of highest affinity CLK/CYC targets does not extend much beyond the list of known five , even though we identified several strong candidates that harbor the expected cis-element ( Figure S3 and Table S1 ) ., Consistent with the first functional study of the period enhancer 9 we find no preferential orientation of the E1-E2 elements ., Anecdotally , it is interesting that the double E1-E2 site around −2 . 5 kb in the vrille promoter ( Figure S3C ) is located on a fragment that is inverted in D . grimshawi only ( Figure S10 ) ., Having built the model from Drosophila sequences only , it was quite remarkable that the unchanged E1-E2 model identified high scoring hits in the majority of known CLOCK/BMAL1 targets in mouse ., Among genes with putative E1-E2 elements , many instances of the motif are highly conserved , and the conservation patterns are often concentrated just on top of the identified elements while rapidly decreasing outside of it ., Unlike in flies , the E2 element appears to be a determinant for specificity in mouse ., Given that tissue-specific expression analyses 60 , 61 revealed largely non-overlapping circadian regulation programs , it is not excluded that future analyses will reveal enhancer elements permitting tissue specific predictions ., We showed that our model predicted peak expression phases in mouse liver that were preferentially centered around ZT12 ( Figure 4B ) , which is consistent with an induction by CLOCK/BMAL1 ., It might be possible to find subclasses in the E1-E2 model that drive expression with more specific phases , e . g . , by modifying the binding affinity of the E2 element ., There should nevertheless be limits to this undertaking as mRNA accumulation is also influenced by processes downstream of transcription ., Noticeably , many of our predicted CLOCK/BMAL1 targets show non-cycling steady state mRNA abundances , at least when assessed in liver 54 ., It is likely that some will cycle in other tissues , however , long mRNA half-lives can easily mask rhythmic transcription rates as has been reported for the albumin gene 62 ., In conclusion we built a probabilistic sequence model , termed E1-E2 , that predicts enhancers driven by the bHLH proteins CLK/CYC in insects and CLOCK/BMAL1 in mammals ., This model not only refines the circadian E-box beyond its core nucleotides but also emphasizes the role of a flanking partner motif that may involve binding of a novel co-regulator complex ., A deeper phylogenetic analysis showed that conserved instances of E1-E2 are found both in promoters of core circadian clock genes , and in genes mediating circadian output ., E1-E2 seems to occur in vertebrates and insects but not in nematodes ., This is perhaps not surprising as the existence of circadian behavior in nematodes is still controversial 57 ., Absence of E1-E2 could also reflect the Coelomata hypothesis that groups arthropodes with chordates in a monophyletic clade 63 ., In this perspective our findings would suggest that the CLOCK/BMAL1 based oscillator evolved after the nematodes separated from a common ancestor ., Alternatively , the nematodes could have lost some oscillator components as a result of their live style in the soil , which largely shields them from daily light cues ., Our report is not the first example of an ancient linkage between bHLH regulators and companion cis-elements ., An even deeper conservation of a cis-regulatory element has been reported in proneural genes controlled by bHLH factors of the Hes family 64 ., Several reasons , e . g . , the necessity to maintain highly stable key developmental programs , were proposed to explain such unusually high conservation ., Here , it is interesting that the BMAL1 protein , unlike genes in the Period or Crytochromes families , stands out as the only circadian component in the murine clock with no functionally redundant paralogues ., The high degree of conservation in its target sites is thus consistent with the unique function of BMAL1 ( CYC ) as the master activator in the circadian network ., We surely expect that comparative genomics combined with functional datasets will allow further dissecting the circadian and other cis-regulatory codes ., MultiZ 65 Multiple alignments were downloaded from the UCSC table browser ( Multiple alignments of 14 insects with D . melanogaster , dm3 , April 2006 , but we restricted these to Drosophila species ) ., We used the Drosophila melanogaster genome and annotations version r5 . 1 to analyze windows of ±2 , 500 bases around all annotated transcripts ., These sequences were used to identify flanking sequences around conserved CANNGT motifs in the five training genes; for the period gene we added the 69-bp enhancer from the species missed in the multiple alignment ( Figure 1A and Figure S3A ) ., Model training ., The sequences used for the model training are given at http://circaclock . epfl . ch/training_seqs . fa ., We implemented a standard Baum-Welsh optimization in which each sequence is independent ( no explicit use of the multiple alignments is made ) ., We took into account phylogenetic relationships by attributing a geometric weighting reminiscent of 35 reflecting the Drosophila species tree ( Figure 1C ) : droGri2: weight = 1/8 , dp4: 1/8 , droYak2: 1/16 , droEre2: 1/16 , droPer1: 1/8 , droWil1: 1/4 , droSim1: 1/32 , dm3: 1/16 , droAna3: 1/8 , droSec1: 1/32 , droMoj3: 1/16 , droVir3: 1/16 ., Thus each gene is counted as one and we used fixed pseudo-count of 0 . 3 for each nucleotide ., Species identifiers are those used in the UCSC alignments ., Training is done on both strands simultaneously with tied ( reverse complemented ) emission probabilities using a custom HMM implementation following 32 ., We scanned ( decoded ) windows of ±2 , 500 bp for all annotated transcripts ( r5 . 1 ) with the cyclic E1-E2 model ., The converged HMM model is provided at http://circaclock . epfl . ch/Models/M_11_4_0 . 3_3_2_13_0_1 . mod , while the seed model is http://circaclock . epfl . ch/Models/seed . M_11_4_3_2_13_0_1 . mod ., We used posterior decoding to compute the posterior state probabilities Psi for state s at position i ( Figure S3 ) , and the expected likelihood ( EL ) for a sequence is computed as, ( es ( Oi ) ) minus the likelihood of the background ( Figure 3 ) ., Here , es ( Oi ) is the ( emission ) probability to observe nucleotide Oi at position i in the state s ., In the case of multiple transcripts , the highest score was used as the gene score ., Correspondence between Affymetrix oligos and genes was done with the Annotations provided at NetAffx . com for the DrosGenome1 and Drosophila_2 arrays ( July 2007 versions ) ., To scan the full mm8 mouse genome ( from the UCSC genome browser ) we extracted the two weight matrices from the Drosophila HMM ( given at http://circaclock . epfl . ch/Models/M_11_4_0 . 3_3_2_13_0_1 . p1 . mat and http://circaclock . epfl . ch/Models/M_11_4_0 . 3_3_2_13_0_1 . p2 . mat ) , and computed the standard likelihood ( LL ), ( wi ( Oi ) /b ( Oi ) ) for the chained matrices at each genomic position ., Here wi ( Oi ) is the probability to observe nucleotide Oi at position i and b ( Oi ) is the background probability for nucleotide Oi ., As in flies we allow for a zero or one nucleotide spacer and consider the maximum of the two scores ., We used a single nucleotide background ( 0-th order ) with 29% of A and Ts , and 21% of C or Gs ., To filter for conservation ( Figures 4 and S8 ) , we average PhastCons scores 52 ( from alignments with 17 vertebrates , UCSC genome browser ) at the positions of the hit ( 25 or 26 bases depending on spacer ) ., Hits are mapped to genes when they occur in windows of ±2 kb of the transcription units from the affyMOE430 table at UCSC ., The latter was used for easy comparison with expression data ., A set of 15 known circadian genes was used to test the specificity of prediction in mouse: Cry1 , Cry2 , Per1 , Per2 , Per3 , Dbp , Tef , Hlf , Wee1 , Bhlhb2 ( Dec1 ) , Bhlhb3 ( Dec2 ) , Nr1d1 ( RevErbα ) , Nr1d2 ( RevErbβ ) , Bmal1 ( Arntl ) , and Clock , of which the latter two are not expected to be self-induced ., Two ClkJrk mutant time series of 12 time points each 47 , 48 were used to quantify differential regulation induced by the mutation , we applied a one-sample t-test to the 24 merged log2-expression ratios at each time point ., GR-CLK induction data was from 38; replicated conditions were averaged and the fold induction between stimulated and un-stimulated cells was computed separately for the S2 cells and the cultured fly heads ., The two were then summed to make a single score for each gene

Introduction, Results, Discussion, Methods

Circadian oscillator networks rely on a transcriptional activator called CLOCK/CYCLE ( CLK/CYC ) in insects and CLOCK/BMAL1 or NPAS2/BMAL1 in mammals ., Identifying the targets of this heterodimeric basic-helix-loop-helix ( bHLH ) transcription factor poses challenges and it has been difficult to decipher its specific sequence affinity beyond a canonical E-box motif , except perhaps for some flanking bases contributing weakly to the binding energy ., Thus , no good computational model presently exists for predicting CLK/CYC , CLOCK/BMAL1 , or NPAS2/BMAL1 targets ., Here , we use a comparative genomics approach and first study the conservation properties of the best-known circadian enhancer: a 69-bp element upstream of the Drosophila melanogaster period gene ., This fragment shows a signal involving the presence of two closely spaced E-box–like motifs , a configuration that we can also detect in the other four prominent CLK/CYC target genes in flies: timeless , vrille , Pdp1 , and cwo ., This allows for the training of a probabilistic sequence model that we test using functional genomics datasets ., We find that the predicted sequences are overrepresented in promoters of genes induced in a recent study by a glucocorticoid receptor-CLK fusion protein ., We then scanned the mouse genome with the fly model and found that many known CLOCK/BMAL1 targets harbor sequences matching our consensus ., Moreover , the phase of predicted cyclers in liver agreed with known CLOCK/BMAL1 regulation ., Taken together , we built a predictive model for CLK/CYC or CLOCK/BMAL1-bound cis-enhancers through the integration of comparative and functional genomics data ., Finally , a deeper phylogenetic analysis reveals that the link between the CLOCK/BMAL1 complex and the circadian cis-element dates back to before insects and vertebrates diverged .

Life on earth is subject to daily light/dark and temperature cycles that reflect the earth rotation about its own axis ., Under such conditions , organisms ranging from bacteria to human have evolved molecularly geared circadian clocks that resonate with the environmental cycles ., These clocks serve as internal timing devices to coordinate physiological and behavioral processes as diverse as detoxification , activity and rest cycles , or blood pressure ., In insects and vertebrates , the clock circuitry uses interlocked negative feedback loops which are implemented by transcription factors , among which the heterodimeric activators CLOCK and CYCLE play a key role ., The specific DNA elements recognized by this factor are known to involve E-box motifs , but the low information content of this sequence makes it a poor predictor of the targets of CLOCK/CYCLE on a genome-wide scale ., Here , we use comparative genomics to build a more specific model for a CLOCK-controlled cis-element that extends the canonical E-boxes to a more complex dimeric element ., We use functional data from Drosophila and mouse circadian experiments to test the validity and assess the performance of the model ., Finally , we provide a phylogenetic analysis of the cis-elements across insect and vertebrates that emphasizes the ancient link between CLOCK/CYCLE and the modeled enhancer ., These results indicate that comparative genomics provides powerful means to decipher the complexity of the circadian cis-regulatory code .

biochemistry, fish, computational biology, drosophila, mammals

journal.pcbi.1004677

Residual Viremia in Treated HIV+ Individuals

Antiretroviral therapy ( ART ) effectively controls HIV infection , suppressing HIV viral loads to below detectable levels in most patients ., However , infection remains: cessation of treatment is usually followed by HIV rebound to high levels 1 ., Ultra-sensitive assays , with detection thresholds as low as 0 . 3 virions per mL of plasma , reveal the presence of viremia in patients on treatment 2 ., What is unclear is the source of this persistent , low-level viremia; does it derive from ongoing rounds of viral replication , or activation of infected cells in the latent reservoir , or some combination of the two 3 ., Our aim is to employ simple mathematical models to gain insight into the source of residual viremia in HIV-infected patients ., HIV cell infection is usually followed by virus production and cell death ., However , a small fraction of infected cells instead enter a state of latent infection 4 , 5 , in which HIV has integrated into the host cell DNA but there is little , if any , virus production ., The virus’ cytopathic effects seem negligible , and these cells seem unaffected by therapy or host immune responses ., The reservoir of these cells is established early during primary infection 6–8 ., While in a latent state infected cells may undergo homeostatic proliferation 9 , which promotes reservoir stability ., The latent reservoir represents only a very small fraction of the total CD4+ T cell population but it is very long-lived; patients on treatment show a decaying reservoir with a half-life estimated to be between 6 and 44 months on average , so the time to complete eradication may be up to 70 years 10 ., Eradication of the latent reservoir is considered to be one of the major hurdles to curing HIV infection 11 ., Importantly , for our purposes , upon latent cell activation , viral production and ensuing cell death resume 12 ., Mechanisms for the generation and maintenance of latency and subsequent activation remain unclear 4 , 13 , 14 ., The evidence supporting latent cell activation as the only source of residual plasma viremia is as follows: ( 1 ) Intensification of ART , by adding an additional drug , has no appreciable impact on CD4 counts 15 or viral load 16 ., ( 2 ) During suppressive ART , plasma virus shows little or no development of drug resistance mutations 17 , 18 ., ( 3 ) Clonal sequences of plasma virus indicate a close relationship with virus archived in the latent reservoir 19 , 20 ., ( 4 ) HIV envelope proteins in gut-associated lymphatic tissue show no evidence of evolution in patients on ART initiated during primary infection 21 ., ( 5 ) Genotypic studies of pre- and post-treatment virus show a too-close relationship for the source of rebound virus to be ongoing viral replication 22 ., However , there is also evidence supporting the notion of ongoing replication ., For example , a genotypic study of episomal HIV cDNA collected prior to viral rebound showed evidence of recent evolution 23 , suggesting that fresh rounds of cell infection with HIV contribute to residual viremia ., Also , while the level of residual plasma viremia has been shown to correlate with the size of the CD4+ T cell viral reservoir in patients on ART , it does not correlate with markers of immune activation , suggesting that reactivation of the latent viral reservoir may not be the sole source of residual plasma viremia 24 ., Residual viral replication may also occur in productively infected CD4+ T cells in various lymphoid tissues , without being reflected in plasma viremia 24 , 25 ., The mathematical modeling work below reconciles these contradictory observations ., We make two underlying assumptions: that latent cell activation does occur in patients , and that R , the reproductive ratio , i . e . , the average number of new cell infections induced by a single infected cell , during suppressive ART is less than 1 ., We show that , even though R < 1 , the contribution of viral replication to residual viremia can be non-negligible if therapy is not sufficiently potent ., Further , we shall show that , although the contribution of viral replication to residual viremia can be significant in such cases , low genetic variability can still be maintained , consistent with de novo emergence of drug resistance being very rare ., Thus , recent evolution is possible , matching the observation in 23 , but long term evolution is unlikely , matching the observations in 15 , 17 , 19 , 20 ., The reproductive ratio R = ( 1 − ε ) pβT/δ ( c + βT ) is a key parameter in our model in determining the amount of residual replication ., The fraction f determines the level of predicted latent reservoir re-seeding in patients on treatment , which can be significant if R is large ., These parameters are therefore central in characterizing ongoing viral dynamics in patients on treatment ., We now discuss realistic ranges for those parameters ., Our primary results below rely upon the reproductive ratio R = ( 1 − ε ) pβT/ ( c + βT ) δ only , since f is small ., However , for the purposes of illustrative simulation , we input the parameters individually rather than as the group parameter R . Where possible , model parameter estimates are taken from the literature 2 , 10 , 35–40 , as listed in Table 1 ., For most antiretroviral therapy , the associated in vivo drug efficacy ε is poorly characterized ., Recently raltegravir , an integrase inhibitor , has been estimated to have efficacy 0 . 94 in a combination therapy including emtricitabine and tenofovir disoproxil fumarate , and 0 . 997 during monotherapy 41 ., Integrase inhibitors are not yet included in most recommended antiretroviral therapy combinations 27 , but combination therapy with raltegravir seems to be no more effective than other types of drugs in treatment-naïve patients 42 , 43 ., We therefore choose for our baseline net drug efficacy ε = 0 . 99 , slightly better than the efficacy of raltegravir in the combination therapy used by Andrade et al . ( 2015 ) 41 ., We will use this drug efficacy to fix the latently infected cell activation rate , next , assuming the viral load on long-term therapy V0 = 3 . 1 copies/mL 2 ., With a fixed at this value we will then explore the sensitivity of our results to drug efficacies in the range ε = 0 . 9–0 . 999 ., Beyond the net measured latent reservoir half-life , t 1 / 2 L = ln ( 2 ) / η 2 , model parameters relating to latent reservoir dynamics , η1 and a , remain poorly understood ., However , the largest negative eigenvalue in our model ( 4 ) should correspond to the observed long term decay of latently infected cells , η2 ., We choose the latent reservoir decay rate in the absence of replenishment by de novo infection , η1 , as a function of this net latent reservoir decay η2 ., As shown in Sec ., B . 3 in S1 Text ,, η 1 = η 2 - a δ f R η 2 - δ ( 1 - ( 1 - f ) R ) ., ( 5 ), We choose the latent cell activation rate a so that in model ( 4 ) , at some arbitrary time after being on therapy for a long period , designated t = 0 , the latent reservoir size L0 and viral load V0 are in quasi-equilibrium , i . e . ,, a = δ ( 1 - ( 1 - f ) R ) - η 2 c V 0 p L 0 , ( 6 ), see Sec ., B . 3 in S1 Text for details ., Note that this approach imposes an additional constraint on our parameters; a > 0 requires that δ 1 − ( 1 − f ) R > η2 ., We interpret this constraint as the net decay rate of productively infected cells in the presence of new infections but in the absence of new latent cell activations , δ 1 − ( 1 − f ) R ( c . f . Eq ( 4 ) ) must be more rapid than the net decay rate of the latent reservoir , η2 ., Assuming a drug efficacy of ε = 0 . 99 , an on-therapy quasi-steady state viral load V0 = 3 . 1 copies/mL , and corresponding latent reservoir size L0 = 1 per 106 cells , we obtain a baseline activation rate of a = 1 . 74 × 10−3 day−1 , which corresponds to an average time of activation for a single latently infected cell of 575 days ., This is about 3 . 5 times the estimated lifespan of a human memory CD4+ T-cell 44 , so only a minority of latently infected cells are expected to become activated before they die ., Nonetheless , we estimate that there are aL ≈ 174 latent cell activations per day , assuming 1011 CD4+ T-cells body-wide ., Pinkevych et al . ( 2015 ) estimated that on average , after therapy is interrupted , active viral replication is initiated once every 6 days ., This does not imply that there is an average of one new latent cell activation every six days , as there also needs to be ensuing rounds of viral replication following the activation of a latently infected cell that cause viral rebound , rather than a chain of infection that ultimately dies out ., Therefore , the actual value of aL remains unclear ., We use aL ≈ 174 latent cell activations per day but our qualitative results are not sensitive to this choice , see Sec ., E in S1 Text ., When investigating viral dynamics under drug efficacies ε ≠ 0 . 99 we recalculate the associated reproductive ratio , R = ( 1 − ε ) R* and then re-compute the associated on-therapy quasi-steady state viral load , V0 = apL0/c ( δ ( 1 − ( 1 − f ) R ) − η2 ) from Eq ( 6 ) ., We have presented a simple HIV viral dynamics model , extended from the standard model 29 , that recapitulates the following features of HIV infection in patients on suppressive therapy: Our primary assumption is that latent cell activation drives viral dynamics on therapy ., This assumption is supported by the observation that clonal sequences of plasma virus indicate a close relationship with virus archived in the latent reservoir 19 , 20 , and is an increasingly well-accepted hypothesis 26 , 34 , 51 , 54 ., An important aspect of our analysis is that our results rely primarily on the with ( i . e . change in to within ) in-host basic reproductive ratio of HIV in patients on effective therapy , R . In particular , since the fraction of new infections that result in latency , f , is very small 34 , the fraction of residual viremia attributable to viral replication in patients on suppressive therapy is approximately R , Eq ( 12 ) ., Further , the probability distribution on the number of rounds of replication achievable after the activation of a latently infected cell , before the lineage dies out , is a function of R only ., We made a reasonable choice of R but no clear estimate exists for patients on suppressive therapy ., Our model predicts that estimation of the reproductive ratio of a patient on therapy , rather than individual parameters that make up the ratio ( e . g . viral production rate p , drug efficacy ε ) would allow us to effectively characterize ongoing replication in patients on therapy , analyzing for example the probability of emergent drug resistance across different individuals ., The implication of our modeling on the low probability of emergent drug resistance re-enforces results from Ribeiro et al . ( 2000 ) 55 ., There the authors argued that , since the proportion of infected cells produced over time in patients on ART is very small relative to the number of infected cells in patients pre-therapy , for drug resistant variants to emerge , they most likely already exist in the infected cell population at initiation of therapy ., To this argument we add the fact that the proportion of infected cells in patients on therapy that have resulted from any viral replication is approximately R , the viral reproductive ratio in patients when on therapy , further reducing the probability of drug resistance emerging from ongoing viral replication ., The assumption of high drug efficacy implies that patients are adherent to therapy , which may not always be the case 56 ., Patients who are not adherent , or patients who have developed some resistance to therapy , may have low drug efficacy ., In that case we would expect a high reproductive ratio R near 1 , and therefore a high proportion ( approximately R ) of residual viremia to be associated with ongoing viral replication ., We used ε = 0 . 6 as an illustrative example of this case , with R = 0 . 92 and therefore 92% of residual viremia due to ongoing viral replication ( see Fig 2c ) ., Although a latent cell activation would be followed , in this case , by more rounds of viral replication than for higher drug efficacy , ultimately the lineage would still die out ( see Fig 3b and 3c ) ., More rounds of viral replication implies more chances for a drug resistant variant to emerge , but the probability is still small; there are too few rounds of replication to be assured of the right mutation ( see Fig 4 ) ., It is important to note however , that ε = 0 . 6 > εc , the critical drug efficacy below which therapy is not suppressive ., Our modeling predictions are contingent on R < 1 ., They are not valid , for example , for cases where adherence to therapy in a patient is such that average drug efficacy dips below this critical value εc , which gives R > 1 ., Our model suffers from a number of other limitations ., Importantly , we model dynamics of latent cell activation very simply; we assume no clonal expansion , which may occur since latently infected cells are mainly memory cells 9 , 51 , 57–61 , and we assume that an activated latently infected cell is the same as a productively infected cell , which may not be the case ., We also assume new latently infected cells decay at the same average rate as pre-existing latent populations ., In these pre-existing latent populations , activation by common cognate antigens likely already occurred , yielding a slow activation rate; new latently infected cells may still be specific to common antigens and hence have a more rapid activation rate ., It is also a one-compartment model , that is , we do not model dynamics in different tissues individually , in particular lymphatic tissue where drug concentrations may be lower than in blood 25 , and where residual replication may occur in productively infected CD4+ T cells without being reflected in plasma viremia 24 , 25 ., Viral and cell transport between tissues may play an important role in promoting HIV infection in patients on therapy 3 , 24 ., In spite of these limitations , we have shown that our models , with relatively few parameters , recapitulate HIV viral dynamics observed in patients on suppressive therapy ., We used a variant of the model to predict that viral replication cannot replenish the reservoir in a patient on therapy ., Current strategies for HIV functional cure target the latent reservoir , with reservoir eradication as the goal ., Our prediction implies that these reservoir eradication strategies will not be obstructed by latent reservoir replenishment in HIV+ patients on effective therapy .

Introduction, Models, Discussion

Antiretroviral therapy ( ART ) effectively controls HIV infection , suppressing HIV viral loads ., However , some residual virus remains , below the level of detection , in HIV-infected patients on ART ., The source of this viremia is an area of debate: does it derive primarily from activation of infected cells in the latent reservoir , or from ongoing viral replication ?, Observations seem to be contradictory: there is evidence of short term evolution , implying that there must be ongoing viral replication , and viral strains should thus evolve ., However , phylogenetic analyses , and rare emergent drug resistance , suggest no long-term viral evolution , implying that virus derived from activated latent cells must dominate ., We use simple deterministic and stochastic models to gain insight into residual viremia dynamics in HIV-infected patients ., Our modeling relies on two underlying assumptions for patients on suppressive ART: that latent cell activation drives viral dynamics and that the reproductive ratio of treated infection is less than 1 ., Nonetheless , the contribution of viral replication to residual viremia in patients on ART may be non-negligible ., However , even if the portion of viremia attributable to viral replication is significant , our model predicts ( 1 ) that latent reservoir re-seeding remains negligible , and ( 2 ) some short-term viral evolution is permitted , but long-term evolution can still be limited: stochastic analysis of our model shows that de novo emergence of drug resistance is rare ., Thus , our simple models reconcile the seemingly contradictory observations on residual viremia and , with relatively few parameters , recapitulates HIV viral dynamics observed in patients on suppressive therapy .

In HIV+ individuals , antiretroviral therapy ( ART ) effectively controls HIV viral loads to below levels detectable by routine tests ., However , more sensitive tests can detect some residual viremia ., The source of this virus is a matter of debate: does it derive from ongoing viral replication , or from viral production following activation of latently infected cells ?, Experimental observations support both sides of the argument: in patients on therapy , HIV shows no long-term evolution , and emergence of drug-resistant mutants is rare , implying no ongoing viral replication , but there remains short-term evolution , implying the opposite ., To reconcile these observations , we propose a mathematical model of latently and productively infected cells and virus ., Using our models we predict that , in patients on suppressive ART , the contribution of viral replication to residual virus , while small , yields short term-evolution ., But even if the contribution is large , for example if adherence to therapy is poor , long-term evolution can still be limited , and de novo emergence of drug resistance is rare ., Thus , our simple models reconcile the seemingly contradictory observations on residual viremia .

journal.pcbi.1004612

Computational Model of MicroRNA Control of HIF-VEGF Pathway: Insights into the Pathophysiology of Ischemic Vascular Disease and Cancer

When cells are exposed to low oxygen tension , cellular adaptation occurs by transcriptionally activating a variety of genes that participate in pathways involving angiogenesis , metabolism and proliferation/survival 1 ., The oxygen-sensitive transcription factor HIF-1 ( hypoxia-inducible factor 1 ) , with over 1000 putative targets in human , is the master mediator of this response 2 , 3 ., HIF-1 is a heterodimer of HIF-1β subunit , which is constitutively expressed regardless of O2 availability , and HIF-1α subunit , whose expression is highly dependent on O2 levels ., In normoxia , HIF-1α protein levels are undetectable 4; they are rapidly hydroxylated by PHDs ( prolyl hydroxylases ) and FIH-1 ( factor inhibiting HIF-1 , abbreviated throughout this paper as FIH ) , followed by polyubiquitination by pVHL ( von Hippel-Lindau ubiquitin E3 ligase ) complex that marks HIF-1α for proteasomal degradation 1 , 5 ., In hypoxic conditions , HIF-1α protein is stabilized and it translocates from the cytoplasm into the nucleus , where it binds to HIF-1β to form the heterodimer HIF-1 complex ., The dimer complex associates with HREs ( hypoxia-responsive elements ) located in the promoters of target genes and tethers transcriptional coactivators , such as CBP ( CREB-binding protein ) and p300 , to activate gene expression ., Many of the genes targeted by HIF-1 encode proangiogenic factors including VEGF-A ( vascular endothelial growth factor A , abbreviated throughout this paper as VEGF ) and EPO ( erythropoietin ) 1 , 6 , 7 ., MicroRNAs ( miRs ) are endogenous , small , non-coding RNA molecules ( ~22 nt ) that mediate gene expression at the post-transcriptional level ., RNA polymerases II and III participate in the transcription of microRNA genes to produce miR primary transcripts ( pri-miRs ) that are usually several hundred nucleotides in length and contain conserved stem loops 8–10 ., These pri-miRs are processed by the RNase III enzyme Drosha into stem-loop intermediates ( ~60–70 nt ) which are termed precursor miRs ( pre-miRs ) , and pre-miRs are actively transported out of the nucleus via a nucleocytoplasmic shuttler Exportin-5 ( XPO-5 ) assisted by the GTP binding nuclear protein Ran 11 , 12 ., The pre-miRs in the cytoplasm are cleaved by another RNase III enzyme Dicer to become miR duplexes which are then incorporated into the miR-induced silencing complex ( miRISC ) 11 ., Within the miRISC , proteins of the argonaute family ( AGO ) are essential for miR function in human as they facilitate the activation of miRISC by catalyzing the dissociation of miR guide strand ( mature miR ) from the passenger strand ( cleaved later ) ; only AGO1 and AGO2 , among the eight AGO proteins in human , can mediate such strand dissociation during miR maturation 13 , 14 ., AGO1 is identified to be associated with miR-mediated translational repression; however , only AGO2-containing RISC is capable of catalyzing the cleavage of target mRNAs 15 , 16 ., Although previous research has extensively investigated the role of AGO in coordinating miRISC activities , very limited knowledge exists about the expression of AGO in response to cellular stress and its physiological importance in the remodeling of vasculature ., Many miRs have been confirmed to have links with the pathophysiology of various cardiovascular diseases ., Since endothelial cells ( ECs ) control the formation of new blood vessels ( angiogenesis ) which is critical for vascular homeostasis , dysfunction of ECs in response to adverse hemodynamic alterations and pathological stimuli , such as inflammation or chronic hypoxia , would lead to inadequate or anomalous angiogenesis that predisposes to the development of many vascular diseases including PAD ( peripheral arterial disease ) and CAD ( coronary artery disease ) 17 ., MicroRNA let-7 ( lethal-7 ) family is among the most promising miR candidates as novel regulators of angiogenesis considering its high expression in ECs and it directly targets several angiogenesis-related factors such as TSP-1 ( thrombospondin 1 ) , TIMP-1 ( tissue inhibitor of metalloproteinases 1 ) and TGFBR1 ( transforming growth factor beta receptor 1 ) 18–20 ., A recent work by Chen et al . revealed that the HIF-1-let-7-AGO1-VEGF signaling pathway is essential in the control of EC angiogenesis in hypoxia 21 ., Members of the let-7 miR family are identified as HRMs ( hypoxia-responsive microRNAs ) whose levels are robustly upregulated by HIF-1 transcription factor in hypoxia ., Mature let-7 targets the mRNA of AGO1 and reduces the level of miRISC formed by AGO1 and other miRs that target VEGF , therefore freeing VEGF from translational repression to promote angiogenesis ( Fig 1 ) ., Validated by in vitro and in vivo experiments , these findings supported the argument of an important angiogenic axis connecting HIF , miRs and AGO1 in ECs that may potentially serve as a valuable target for pro- and anti-angiogenic therapies 21 ., Though many of the molecular components that are involved in the miR control of the HIF-VEGF pathway in ECs have been characterized , the detailed dynamics of how they mechanistically interact with each other within the signaling network are barely understood ., In this sense , a computational model constructed from the perspective of systems biology would provide dynamic understanding and mechanistic insights of the complex cellular response to hypoxia , as the model relies on basic biophysical principles and biochemical reactions to describe relevant molecular interactions within a cell 22 ., However , mathematical models of miRs are very limited in literature; the available models , such as the model of miR-193a in ovarian cancer and the model of miR control circuits in epithelial-mesenchymal transition , focused on predicting connections between certain expression patterns of miR-related molecules and disease-related physiological phenotypes 23 , 24 ., On the other hand , Kim et al . integrated the miR-451-mTOR signaling pathway into a multiscale hybrid model that described the complex processes of glioma cell proliferation and migration in great detail 25 ., Most of these recent models have not considered time-course experimental data available from related studies in their validations and predictions , which may undermine the predictive power of computational models since any important details hidden in the dynamical responses would be easily overlooked ., In this study , we have developed a mechanistic model describing the miR regulation of the HIF-VEGF signaling pathway that , for the first time at the molecular level , unveils the critical role of miR in the complex process of hypoxia-driven angiogenesis ., The model incorporates biophysical details of miR biogenesis and considers cellular compartmentalization that were absent in previous miR pathway models ., We have employed the model to study how different gene overexpression/silencing strategies in ECs would affect the overall cellular adaptation to hypoxia , quantitatively and qualitatively , by analyzing the dynamics of several signature proteins ., Assisted by the model , we have identified various characteristics in cellular oxygen sensing mechanisms and in miR regulatory network that controls the canonical HIF-VEGF pathway ., The model predicts that HIF-1α stabilization obeys a hypothetical switch-like mode and it is negatively regulated by an mRNA destabilizer in hypoxia 26 ., To address a major focus of the study , we show that let-7 and AGO1 are the initiators and coordinators of VEGF release , whereas they negatively exert feedback control on each other and are capable of minimizing the impact of possible outside perturbations; we also illustrate the role of miR-15a as a final effector molecule which is under control of AGO1 , since abundance of miR-15a directly determines how much VEGF mRNA is available for translation ., From these observations , we propose a potential mechanism that may contribute to impaired angiogenesis during recovery in patients with peripheral arterial disease ., Another key focus of the study is the extension of our model analysis into potential clinical settings , where we evaluate different pathway-based therapeutic strategies designed to differentially regulate angiogenesis in highly hypoxic conditions , which are commonly observed in tumor and ischemic tissues ., Together , these findings reveal an integrated image of multiple miRs , each with different targets , that work cohesively with miR-processing proteins ( e . g . Dicer , AGO1 ) to counteract adverse physiological stresses by promoting VEGF synthesis and angiogenesis ., This study should stimulate future research to investigate , both experimentally and computationally , the mechanistic signaling networks that contribute to the dysregulated miR expressions in cancer and in ischemic vascular disease ., The model we constructed , as shown in Fig 2 , describes the regulation and coordination by miRs in in vitro hypoxia-induced HIF stabilization and VEGF synthesis in ECs ., The model consists of a cytoplasmic and a nuclear compartment , and it is functionally divided into four modules: oxygen sensing/HIF stabilization , HIF dependent gene transcription , miR-15a targeting of VEGF , and let-7 biogenesis/targeting ., The oxygen sensing module is a selected integration of two established HIF models: Qutub and Popel’s work which considers the participation of iron and 2-oxoglutarate ( 2-OG ) during HIF stabilization , and the model by Nguyen et al . that includes FIH-mediated events in HIF hydroxylation 27 , 28 ., Although the ascorbate binding suggested by Qutub and Popel as well as the process of nuclear HIF stabilization are not included in order to maintain a moderate complexity , our carefully integrated model is able to capture the essential oxygen-sensing behaviors of ECs that are needed to address our research focus ., For the same reason we do not include the effects of reactive oxygen species and succinate on HIF-1α considered in subsequent papers of Qutub and Popel 29 , 30 ., The influence of HIF/PHD feedback is not considered since the model assumes that PHD2 concentration is in excess 31 ., Although this feedback mechanism is absent , the model with the current parameter set and reactions is able to capture the core dynamics of the distinct HIF-1α behaviors in normoxia and in hypoxia; according to model simulations presented in this work , the high initial concentrations of PHD2 contributes to the early suppression of HIF-1α after its rapid induction in hypoxia , which agrees with an assumption made by Bruning et al . about the temporal role of HIF/PHD feedback loop 32 ., To describe the negative feedback control of HIF in hypoxia , however , we included the mechanism of HIF-1α mRNA destabilization by TTP ( tristetraprolin ) identified by Chamboredon et al . and assumed a HIF-dependent TTP production , since hypoxic exposure is experimentally shown to induce TTP 33 , 34 ., Similarly , VEGF protein synthesis is also downregulated by TTP accumulated in hypoxia 35 ., The module describing HIF activation of its targets , including the genes of VEGF and let-7 , details the process of stabilized HIF-1α being transported into the nucleus , dimerizing with HIF-1β subunit and promoting the transcription of these genes containing HREs 21 , 36 ., We assumed that the step of HIF-1 complex binding with the coactivators CBP/p300 was included in the process of HIF-1α/HIF-1β dimerization ., Interestingly , HIF-2α ( hypoxia-inducible factor 2 alpha ) is also shown by Chen et al . to transcriptionally induce the same group of HRMs upon its induction in hypoxia , and it is known that HIF-1α and HIF-2α share not only highly similar protein structures but also various common target genes including VEGF 21 ., In skeletal muscle and especially ECs , HIF-2α signaling seems to be rather ancillary to the predominant regulatory potential of HIF-1α that primarily modulates the cellular angiogenic and migratory activities 37 , 38 ., Therefore , for the scope of this study we decided not to distinguish between these two molecules in the model but to represent them both in terms of HIF-1α , which is more prevalently expressed across different cell types than HIF-2α 39 ., The model currently considers let-7 and miR-15a as key miR regulators of the hypoxia-driven VEGF desuppression process ., The production of miRs in the model followed a well-established miR biogenesis pathway that undergoes transcription , nuclear-cytoplasmic transport , endonucleolytic processing and miRISC loading 11 ., The model combines Drosha processing and XPO-5 transport into a one-step reaction , and miRISC formation along with miR duplex dissociation is simplified as one reversible association process between AGO1 protein and miR ., The complex formed by AGO1 protein and let-7 can travel back into the nucleus and promote the processing of pri-let-7 which constitutes a positive auto-regulatory loop 40 ., Let-7 represses the translation of two confirmed targets , AGO1 and Dicer , and this silencing negatively feeds back to the maturation and stabilization of let-7 21 , 41 ., The mRNAs of AGO1 and Dicer are processed by the let-7 miRISC and directed to cytoplasmic domains called p-bodies 42 , 43 ., Since p-bodies are found to be involved in general mRNA turnover , we assumed that once mRNAs entered the p-bodies , they would be stored , inaccessible to translation with a significantly slower degradation rate compared to that of cytoplasmic mRNAs , while a very small fraction of them could still exit p-bodies and re-enter the translational machinery 44 ., Since the let-7/AGO1 axis would influence the expression of a group of miRs leading to altered dynamics of many target genes , we selected VEGF , due to its crucial importance in angiogenesis and extensive literature data support , as an epitome gene to demonstrate mechanistically the details of how this cascade controls specific gene expression during a pro-angiogenic response ., For the current purposes of the model , miR-15a is selected to represent a group of VEGF-targeting-miRs as miR-15a has been experimentally validated to directly repress VEGF synthesis and markedly affect angiogenesis in ECs 45 ., In addition , hypoxia is shown to weaken the association of AGO1 with many VEGF-targeting miRs including miR-15a and cause significant downregulation of these miRs; these evidence further links the dynamics of miR-15a to the coordination by the let-7/AGO1 axis 21 , 46 ., VEGF mRNAs targeted by miR-15a also undergo a series of steps including p-body storage similar to the mechanism of let-7-mediated mRNA silencing ., Details including the mathematical formulation of the biochemical reactions in the model and parameter optimization are discussed in the Methods section ., The simulations of the model were compared with the data from independent experiments performed by different research groups ., The first form of validation focused on the oxygen sensing module and compared the model’s prediction of HIF-1α accumulation in hypoxia with the quantified Western blot data in ECs ( Fig 3A ) 21 ., Experimental data suggest that HIF-1α accumulation in vitro is most significant at O2 levels between 0 . 5–6% , and this is reflected in the model by setting the initial O2 concentration to 19 . 9 μM which corresponds to 2% ambient oxygen 27 , 47 ., The level of HIF-1α predicted by the model was the sum of both the free form and bounded proteins ., HIF-1α concentration at time zero was taken as a reference measure which represents the normoxic ( 21% O2 ) steady state level ., In agreement with the experimental result , the simulation showed a quick induction of HIF-1α during the first few hours followed by a gradual decrease to the steady state level ., Similarly , Western blot data of HIF-1α in different cell types from other research groups also indicate that , in hypoxia , HIF-1α protein is induced rapidly while its expression peaks and gradually decreases after a few hours , suggesting that the cascade of TTP-mediated HIF-1α mRNA destabilization , as one of the major mechanisms which downregulate HIF-1α via negative feedback , should be incorporated as a fundamental part into the model 33 , 48 , 49 ., In addition , the predicted time course abundance of AGO1 protein was compared with the experimental quantification using Western blot in ECs in hypoxia ( Fig 3B ) 21 ., The predicted AGO1 level was also a summation computed in a similar way of how HIF-1α was defined above ., To obtain the relative expression over time , the absolute level of AGO1 was normalized with respect to its initial concentration ( steady state level in normoxia ) , which was obtained by simulating the model at an O2 concentration 209 μM ( 21% O2 ) for a long enough time span 27 ., After an initial delay during which let-7 was accumulated in hypoxia and let-7 miRISC were formed , AGO1 level started to decrease because of a rapid decline in the amount of AGO1 mRNAs that were available for translation ( S1 Fig ) ., Chen et al . also demonstrated that this response was not specific to ECs: cells from different organs/tissues including liver , kidney and muscle all displayed significant AGO1 downregulation in response to hypoxia 21 ., The VEGF protein production curve predicted by the model was compared with experimental data from two different groups ., Although endothelial cells , compared to other cell types , may not be the biggest contributor of VEGF secretion in response to low oxygen tension , adequate autocrine VEGF signaling was proven to be critical in the maintenance of vascular homeostasis 52 ., Zhou et al . measured the VEGF protein expression at different time points in whole-cell extract of SHEP cells ( a human neuroblastoma cell line ) that were cultured in hypoxic conditions ( 1% O2 ) 50 ., A two-fold increase in VEGF level was predicted after a simulated 8-hour exposure to 1% O2 tension ( Fig 3C ) ., Liu et al . analyzed the impact of CoCl2-induced hypoxia on the expression of VEGF proteins using Western blot in HepG2 cells ( a human hepatocellular carcinoma cell line ) 51 ., CoCl2 ( cobalt chloride ) is one of the hypoxia-mimetic agents; it stabilizes HIF-1α in normoxia by directly inhibiting the process of PHD/FIH-mediated HIF-1α hydroxylation ( see reactions in Fig 2 ) 53 , 54 ., The model assumes an initial CoCl2 concentration of 200 μM in the simulation and the relative expression was normalized to VEGF level at time zero ( Fig 3D ) ., Since the current model reactions and parameters are established to describe signaling events specifically in ECs , the fit between model predictions and experimental results in Fig 3C and 3D does not imply that ECs , HepG2 and SHEP cells have the same dynamics of intracellular signaling and VEGF production ., Likely , different parameter sets would be needed in order for the model to make more accurate predictions of VEGF synthesis in other cell types ( e . g . tumor , muscle , stromal cells ) as they are the more significant sources of VEGF secretion compared to ECs 55 ., Previous studies have quantified the induction of VEGF in stromal and tumor cells and found a 2 to 6 fold increase of VEGF in stromal cells and a 3 fold increase in a breast cancer cell line after 24 hours of hypoxia treatment; our EC-based model predicts a 3 . 5 fold increase in the intracellular VEGF level after 24 hours of simulation at 2% O2 ( S2C Fig ) 56 , 57 ., In this sense , the model described in this work is able to predict VEGF dynamics that are comparable , both quantitatively and qualitatively , to biological VEGF data in response to hypoxia in different types of cells , which allows for further extension of the HIF-let-7-AGO1-VEGF framework in other cell models ., The step of oxygen sensing determines how much HIF-1α will be stabilized and then dimerize with HIF-1β to form active transcription factors at different O2 levels ., Initially , HIF-1α expression is low in normoxia and transcriptional activities of let-7 , VEGF and TTP are insignificant ., As oxygen availability decreases , hydroxylation of HIF-1α by PHDs and FIH is also reduced , allowing more HIF-1α to escape from VHL-mediated degradation and enter the nucleus 1 ., Fig 4A shows the overall oxygen dependent response of HIF-1α ., For small enough oxygen levels 2% and 1% in Fig 4A , accumulated HIF-1 reaches a maximum ( overshoot ) at around 10 hours and then slowly declines to a steady level ., Since the model assumes that initial PHD2 concentration is in excess in order to capture switch-like responses in HIF-1α hydroxylation ( Fig 4B ) , changes in PHD2 dynamics when hypoxia is sensed should take place very quickly at a time point much earlier than the overshoot 26 , 27 ., The same reason justifies that FIH does not cause the overshoot , so we hypothesize that it is TTP which creates the initial overshoot , since hypoxia promotes TTP synthesis which destabilizes the mRNAs of HIF-1α and downregulates its translation 34 ., Results in Fig 4C show that in silico knockdown of TTP mRNA effectively prolongs the initial overshoot in HIF-1α stabilization curves ., The time course profiles of HIF-1α also strongly depend on the rate of cytoplasmic-nuclear trafficking ( Fig 4D ) ., As the forward rate of HIF-1α shuttling from cytoplasm into nucleus increases , more HIF-1 transcription factor complex is formed , which promotes the synthesis of various molecules including VEGF that facilitates cellular adaptation to hypoxia by improving angiogenesis and TTP that feeds back to inhibit HIF-1α production ( Fig 4E ) ., Consistent with our prediction , Ahluwalia et al . overexpressed importin-α , a nuclear importer of HIF-1α , in GMECs ( gastric mucosal endothelial cells ) of aging rats and observed a significant increase in the binding of HIF to the VEGF gene promoter region 58 ., Smaller forward rate of HIF-1α nuclear import leads to a lower HIF-1α baseline in normoxia and reduces the overall HIF-1α level in hypoxia , since the majority of the stabilized HIF-1α accumulates within the cytoplasm ( Fig 4F ) , where HIF-1α is unable to dimerize with HIF-1β and is more susceptible to degradation ., This suggests that cells with impaired HIF-1α nuclear import are correlated with reduced HIF-1 transcription activity and poorer pro-angiogenic adaptation in hypoxia , which is consistent with the finding that reduced importin-α level in senescent GMECs hindered the induction of VEGF expression and angiogenesis in response to hypoxia 58 ., We are interested in the mechanistic interactions between let-7 , AGO1 and miR-15a and their roles in the control of subsequent VEGF mRNA release , since AGO1-associated miRs ( e . g . miR-15a ) that are capable of silencing VEGF were shown to be less abundant following hypoxia treatment 21 ., To better understand this key feature in our model , we approach the analysis in two steps by looking at the direct interactions between let-7 and AGO1 that influence free form VEGF mRNA level at different O2 tensions , as well as the downregulation of miR-15a availability as a result of upstream let-7/AGO1 control ., A number of in vitro and in vivo studies have been performed to characterize the therapeutic values of different miRs in treating cancer and cardiovascular disease; in some of these the goal was to uncover the entire regulatory events that give rise to the miR dysregulation 20 , 65 , 66 ., Abnormal profiles of AGOs that disrupt the balance between pro- and anti-angiogenic miR expression are among the essential reasons behind the aberrant disease-related angiogenic activities of ECs 67 , 68 ., Deriving and testing potential miR-based therapeutics in different diseases in silico , given the substantial analysis performed to understand our proposed pathway , are of crucial significance to future research in the field ., We performed modular sensitivity analysis on four key species in the pathway: cytoplasmic HIF-1α , free form AGO1 , free form let-7 and VEGF ., The analysis was accomplished in MATLAB SimBiology toolbox ( see Methods ) , assuming 2% hypoxia as O2 initial condition ., Fig 8A–8D display the local sensitivity of the four species with respect to different set of selected kinetic parameters , in which direct production and degradation rates of each species were excluded since their contributions are too evident to produce valuable insights ., Not surprisingly , HIF-1α is very sensitive to its affinity with PHD2-O2-Fe-DG or FIH-O2-Fe-DG complexes which will subsequently mark it for hydroxylation ( Fig 8A ) ., Also , the idea we proposed in previous results that the strength of association between let-7 and AGO1 controls the expression of both molecules , was again corroborated by sensitivity analysis ( Fig 8B and 8C ) ., It is noteworthy that in the sensitivity analysis for VEGF , varying TTP synthesis was as influential as varying the VEGF silencing efficiency of miR-15a RISC directly ( Fig 8D ) ., Since the major conclusions of this work relate closely to the qualitative dynamics of AGO1 and VEGF , the more influential parameters identified from modular sensitivity analysis were subjected to additional evaluations ( S7 Fig ) ., Motivated by the sensitivity analysis , we suggested that overexpressing TTP could be a potential anti-angiogenic therapy since it inhibits VEGF synthesis by decreasing HIF-1 signal and directly destabilizing VEGF mRNA ., Since TTP suppression in cells has been associated with pro-tumorigenic phenotypes , stimulation of its expression might be an effective way to shrink the synthesis of pro-angiogenic ( e . g VEGF ) and pro-inflammatory cytokines in tumor ( Fig 8E ) 75 , 76 ., In addition to the sensitivity analysis , we tried to look for core reactions and parameters that are responsible for the radical differences between system behaviors in hypoxia and in normoxia ., HIF-1α and AGO1 are both master coordinators for a series of signaling events in the model , and their time course total expressions have been experimentally measured 21 ., Increasing the affinity between oxygen and PHD2-Fe-DG or FIH-Fe-DG complex significantly reduces the relative HIF induction in hypoxia ( Fig 8F ) ., A reason for that is the extra amount of PHD2/FIH-Fe-DG-O2 that has been stabilized in normoxia due to enhanced O2 binding , which speeds up HIF hydroxylation in hypoxia ( S8 Fig ) ., A higher rate of HIF-1α translocation also strongly influences HIF induction because it temporarily pushes more HIF-1α into the nucleus and protects it from degradation , but this would later lead to a big drop in total HIF-1α because of HIF-induced TTP production ( Fig 8F ) ., For AGO1 , its downregulation is necessary to induce sufficient VEGF desuppression ., Like let-7 , in the model AGO1 is prevented from degradation when associated with miRs ., Since more let-7-AGO1 is formed because of either increased let-7 or stronger binding , the extra amount of AGO1 that is stabilized elevates its baseline level in hypoxia; furthermore , higher level of let-7-AGO1 reduces AGO1 level in normoxia due to translational repression ( Fig 8G ) ., In this study , we presented the first mass-action based computational model of miRs in a comprehensive , whole-cell signaling network that is closely related to angiogenesis ., Our model is also the first one to have described in details the regulatory process that controls miR biogenesis , such as Dicer cleavage , AGO1 loading and p-body localization , and considered these reactions as an essential module of the model framework 11 , 42 ., The reason why these elements are important in the formulation of our model is more than the fact that they represent the real biological process; for example , AGO1 is commonly considered as part of miRISC for universal miR-mediated silencing activities , but in our pathway of interest it also happens to be a key factor ., Challenges follow as always when researchers map signal transduction pathways , and we aim to make a model that contains enough biochemical and biophysical details to address different experimental findings while its complexity remains manageable ., With a few Michaelis-Menten or Hill kinetics and more than 80% of the reactions based on simple first and second order kinetics , the model is equipped with both modular flexibility and solid biochemical background ., Since most miR research only focuses on the influence of one specific miR , we decided to take a novel approach and investigate the miR-dependent regulation of miRs in hypoxia coordinated by miR-processing molecules AGO1 and Dicer , as the core theory of our model has been experimentally validated by the work by Chen et al 21 ., However , current experimental data on miR signaling in the control of angiogenesis is very thin , which leaves a large space for our model to be further validated and refined ., A fundamental goal of this work is to guide and stimulate more miR research that would investigate not only the function of an individual miR but also the interconnections between abnormal profiles associated with a group of miRs in vascular diseases ., By the same reasoning , topics such as the time course dynamics of molecules participating in p-body configuration during miR-induced mRNA silencing or the characterization of individual miR activities via AGO1- versus AGO2-mediated regulation may also bear research values in the miR field , and these data , if available from future studies , would then significantly complement the accuracy and reliability of our model ., Overall , the extensive simulations performed in this study identified and reasoned that overexpressing AGO1 , TTP or antagonizing let-7 are effective strategies to suppress VEGF production in tumor , and that miR-15a antagonists alone , compared to other proposed strategies , could most potently enhance VEGF synthesis in simulated PAD conditions ., Our model will advance the current understanding of how different miRs are regulated to affect angiogenesis when cells are actively adapting to hypoxia; it will also provide valuable insights into the future research in the pathophysiology of cancer and ischemic cardiovascular disease , including PAD , as well as the development of miR-based therapeutics that target other related pathways ., Sensitivity analysis reveals that the degree of HIF-1α stabilization is firmly controlled by the binding between O2 and hydroxylase enzymes ( e . g . PHD2 , FIH ) ., The stronger O2 binds hydroxylase enzyme , the less HIF-1α is sustained both in normoxia and hypoxia ., For tumor this change could be disadvantageous , since cells in hypoxia would fail to accumulate enough HIF-1α in order to trigger adequate pro-angiogenic adaptations ., On the other side , inhibiting the interaction between O2 and hydroxylase enzyme increases HIF-1α signals and the expression of its targets , which potentially benefits patients with ischemic arterial disease 77 ., We also pinpointed the molecule TTP , which destabilizes mRNA of multiple signaling molecules including its own , and we demonstrated in silico that it holds therapeutic value in tumor for its anti-angiogenic property 75 , 78 ., Although TTP overexpression seems more direct than miR-based therapies in terms of its mechanism to inhibit VEGF production , its mRNA-destabilizing activity is repressed upon phosphorylation by the mitogen-activated protein kinase 79 , 80 ., TTP is linked to miR as a direct target of miR-29a validated in cancer epithelial cells , which adds another layer of complexity to the delicate regulation of VEGF expression in hypoxic environments 81 ., Speaking of potential model applications in the context of disease pathology , since the prominence of AGO1 and let-7 profiles in cancer has already been validated , the next step is to connect our model with miR features in vascular disease 21 ., In the limited literature that studies miR dysregulation in PAD , circulating let-7 and miR-15a are shown to be downregulated in patients with PAD compared with healthy controls 82 ., Interestingly , these miRs happened to be included in our model for their importance in the modulation of VEGF synthesis in hypoxia , and this motiva

Introduction, Results, Discussion, Materials and Methods

HRMs ( hypoxia-responsive miRNAs ) are a specific group of microRNAs that are regulated by hypoxia ., Recent studies revealed that several HRMs including let-7 family miRNAs were highly induced in response to HIF ( hypoxia-inducible factor ) stabilization in hypoxia , and they potently participated in angiogenesis by targeting AGO1 ( argonaute 1 ) and upregulating VEGF ( vascular endothelial growth factor ) ., Here we constructed a novel computational model of microRNA control of HIF-VEGF pathway in endothelial cells to quantitatively investigate the role of HRMs in modulating the cellular adaptation to hypoxia ., The model parameters were optimized and the simulations based on these parameters were validated against several published in vitro experimental data ., To advance the mechanistic understanding of oxygen sensing in hypoxia , we demonstrated that the rate of HIF-1α nuclear import substantially influences its stabilization and the formation of HIF-1 transcription factor complex ., We described the biological feedback loops involving let-7 and AGO1 in which the impact of external perturbations were minimized; as a pair of master regulators when low oxygen tension was sensed , they coordinated the critical process of VEGF desuppression in a controlled manner ., Prompted by the model-motivated discoveries , we proposed and assessed novel pathway-specific therapeutics that modulate angiogenesis by adjusting VEGF synthesis in tumor and ischemic cardiovascular disease ., Through simulations that capture the complex interactions between miRNAs and miRNA-processing molecules , this model explores an innovative perspective about the distinctive yet integrated roles of different miRNAs in angiogenesis , and it will help future research to elucidate the dysregulated miRNA profiles found in cancer and various cardiovascular diseases .

Cells living in a hypoxic environment secrete signals to stimulate new blood vessel growth , a process termed angiogenesis , to acquire more oxygen and nutrients ., Hypoxia-inducible factor 1 ( HIF-1 ) accumulates in hypoxia and expedites the release of pro-angiogenic cytokines such as vascular endothelial growth factor ( VEGF ) , a prime inducer of angiogenesis ., The intermediate signaling events connecting HIF-1 and VEGF are tightly controlled by microRNAs ( miRs ) , which are endogenous , non-coding RNA molecules and powerful regulators in cancer and cardiovascular disease ., Given the importance of angiogenesis in tumor development and post-ischemia reperfusion , it holds great basic research and therapeutic value to investigate how miRs modulate intracellular VEGF synthesis to control angiogenesis in hypoxia ., We present a computational model that details the interactions between miRs and other key molecules which make up different hierarchies in HIF-miR-VEGF pathway ., Based on simulation analysis , new potential therapies are introduced and tested in silico , from which the strategies that most effectively reduce VEGF synthesis in cancer , or enhance VEGF release in ischemic vascular disease are identified ., We conclude that in hypoxia different miRs work consonantly to fine-tune the cellular adaptations; when a master miR alters its expression , dynamics of other miRs vary accordingly which together contribute to aberrant RNA/protein profiles observed in the pathophysiology of multiple diseases .

journal.pcbi.1000294

Agent-Based Model of Therapeutic Adipose-Derived Stromal Cell Trafficking during Ischemia Predicts Ability To Roll on P-Selectin

Intravenous ( i . v . ) delivery of therapeutic stem cells is a promising option for the treatment of ischemic injuries ., Therapeutic cells that reside and persist ( i . e . , incorporate ) in the injured extravascular space have been shown to aid recovery of tissue perfusion and function , although low rates of incorporation currently limit the safety and efficacy of these therapies 1 ., It follows that methods to increase the number of incorporated cells may lead to better clinical outcomes ., To achieve this , we submit that a better understanding of stem cell trafficking through the microvasculature prior to incorporation is necessary and that computational modeling techniques could speed the rate of discovery; current investigations however are hampered by the processes inherent complexity ., Towards this end , we present an agent-based computational model ( ABM ) of therapeutic stem cell trafficking during skeletal muscle ischemia that is capable of generating new hypotheses quickly and cost-effectively ., The process by which endogenous circulating cells traffic to sites of general injury and gain access to the extravascular tissue space is referred to as the adhesion cascade 2 ., Most often studied in leukocyte subpopulations ( e . g . , neutrophils and monocytes ) , it is applicable to the mobilization of circulating stem cells to sites of injury 3 ., Depicted in Figure 1 , the adhesion cascade consists of a series of sequential events including margination , rolling , integrin activation , firm adhesion , and trans-endothelial migration ( extravasation ) ., This complex process is mediated by the interplay between cellular adhesion molecule ( CAM ) expression , chemokines and cytokines , and hemodynamics 4–14 ., During skeletal muscle ischemia , tissue damage and dysfunction results from decreases in a tissues blood supply if nutrient and oxygen delivery cannot meet metabolic demand ., As the blood supply decreases , reductions in blood volume flow rates through the downstream vasculature lead to reduced wall shear stresses ( WSS ) , blood flow velocities , and hydrostatic pressures ., Conversely , arterial and collateral vessels up-stream of the ligation experience increases in blood volume flow rates , as blood flow is re-routed through intact vasculature ( causing vessel swelling and increases in WSS and hydrostatic pressures ) 15 ., Altered blood flow profiles activate endothelial and perivascular cells through changes in WSS and circumferential stress , respectively , which initiate a complex cascade of events to mobilize circulating cells to the site of injury ., Activated endothelial cells ( ECs ) increase their surface expression of key CAMs , including the selectins ( e . g . , E-selectin and P-selectin ) to support rolling , and the integrins to support firm adhesion and extravasation of circulating cells to the endothelium 15 ., The major integrin receptors preferentially expressed on ischemic endothelium include vascular cell adhesion molecule-1 ( VCAM-1 ) and intracellular adhesion molecule-1 ( ICAM-1 ) ., In concert , activated ECs and perivascular cells also secrete numerous growth factors , chemokines , and cytokines , such as monocyte chemoattractant protein-1 ( MCP-1 ) , tumor necrosis factor-α ( TNF-α ) , and interleukin-1β ( IL-1β ) to increase binding affinities and ensure a higher probability of circulating cell adhesion to the endothelium 16 ., Through these mechanisms and others , circulating cells are able to home to ischemic injury sites , adhere to the endothelium , extravasate , and incorporate into the injured tissue ., This paper focuses on therapeutically delivered stem cell populations , or cells that are injected i . v . in order to repair or regenerate injured tissues ., It has been hypothesized that i . v . -injected stem cells adhere to activated endothelium using mechanisms that are similar to those of endogenous leukocytes ., Moreover , it has been proposed that these adhesive interactions are rate-limiting for an effective therapeutic response 1 ., We have chosen to investigate hASCs primarily for their widespread availability and potential clinical impact ., Similar to other stem cell populations , hASCs are characterized by a high proliferation and differentiation potential 17 and express many known stem cell-associated markers 18 , 19 ., They are isolated from adipose tissue following routine intra-operative suction lipectomy or panniculectomy procedures and have shown effectiveness in restoring perfusion to ischemic tissue following i . v . delivery 20 , 21 ., hASC homing and trafficking capabilities 22 , however , are relatively under-studied , and this paper presents a novel computational platform for investigation into this important , but currently ambiguous research area ., We utilized ABM techniques to interrogate therapeutic hASC trafficking during ischemia ., Adopting an agent-oriented approach to study the stem cell-specific adhesion cascade has been proposed previously by us 23 and others 24 , 25 ., Here , we expanded the scope of our prior ABM of monocyte trafficking in healthy skeletal muscle microvasculature to include therapeutically delivered stem cells , tissue-resident macrophages , circulating monocytes , endothelial cells , ischemic injury , and a larger microvascular network ., The adhesion cascade was simplified to three primary parameters: CAM expression , hemodynamic forces , and chemokine and cytokine secretion and exposure ., A network blood flow analysis program 26 , instituted in MATLAB , calculated pressure , flow velocities , and wall shear stresses within the simulated microvascular network , while the other parameters were governed in the Netlogo software program 27 by over 150 rules derived from independent peer-reviewed literature ( Text S1 ) ., Model verification was performed by comparing simulation results to data from independent peer-reviewed literature including: ( 1 ) monocytes ability to extravasate independent of selectin-mediated rolling; ( 2 ) the preferential upregulation of endothelial CAMs implicated in circulating cell adhesion; ( 3 ) the increased secretion of inflammatory chemokines and cytokines by ischemic endothelium; ( 4 ) increased WSS and network flow rates in collateral microvascular networks; and ( 5 ) rolling distances of circulating monocytes ., Following model verification , the i . v . delivery of therapeutic hASCs was simulated ., Early simulations showed unexpected levels of hASC extravasation , which prompted a re-examination of the rule-set and the formulation and testing of a new hypothesis ., Rolling via the selectins has been shown to be critical for the homing of other circulating cell populations and is the rate-limiting step during neutrophil trafficking 2 , 28 ., hASCs do not possess the dominant ligand for the selectins ( P-selectin glycoprotein ligand-1 ( PSGL-1 ) ) , but we hypothesized that they were able to roll on selectins independent of PSGL-1 expression and that this may be a rate-limiting step , as well ., Systematic knockout experiments in silico supported this hypothesis , and we subsequently validated our model predictions in vitro in parallel plate flow chamber assays ., In this way , the model was used as a tool and led to new understandings after it behaved unexpectedly ., Furthermore , the work suggests that selectin interactions are an important mediator of therapeutic hASC trafficking ., This work is particularly illustrative of the power and benefit of agent-based modeling in biology ., When one examines or studies a biological phenomenon , it is necessary to determine the essential parameters , species , molecules , and behaviors that are necessary or sufficient to account for that phenomenon ., Agent-based models can help to formalize this process , and ABMs , like all models ( including conceptual models ) , are necessarily incomplete ., Nonetheless , their use helps facilitate rapid discovery; model-aided hypothesis generation provides a systematic means of determining the next steps in the discovery process by identifying what constitutes “sufficient” ., Two scales of biological organization were present within the model: ( 1 ) the cellular-scale encoded at the level of literature-based agent rules ( Text S1 ) ; and ( 2 ) the tissue-level scale represented by the overall model , which was not explicitly programmed and whose observables represent global measurements ., Model verification was performed by examining higher order system behaviors ( tissue-level scale ) and comparing them to corresponding tissue-level measurements from independent wet-lab experimentation ., This is , in effect , a test of the effectiveness of the translation of knowledge between the two scales that existed in the model 29 ., Specifically , the model was verified by examining both monocyte trafficking ability and key aspects of acute skeletal muscle ischemia , prior to conducting simulations of hASC trafficking ., The model reproduced three properties of ischemic injuries including: ( 1 ) increases in WSS and network flow rates; ( 2 ) up-regulation of key CAM expressed by injured endothelium; and ( 3 ) increased secretion of chemokines and cytokines ., Similarly , two properties of monocytes were reproduced in silico including their independence from selectin-mediated rolling and their average rolling distances during trafficking ., These higher order system behaviors were the aggregate result of thousands of interactions between individual agents , their neighbors , and their local environment as they responded to separate dynamic cues outlined in the literature-based rule-sets ( cellular-scale ) ., Ischemic injury was simulated by increasing the pressure at the feeding arteriole by 25% , after which resultant hemodynamic properties were re-calculated ( Table 1 ) ., Network flow rates were elevated 17% , and average WSS was elevated in arterioles ( 187% ) , capillaries ( 177% ) , and venules ( 48% ) ., These hemodynamic changes are characteristic of a collateral microvascular network ( adjacent to an injured network ) during acute skeletal muscle ischemia ., This portion of the network represents one that remains patent following injury and would be capable of supporting the trafficking and extravasation of circulating therapeutic cells ( i . e . , we are not simulating the vessels downstream of an obstruction that would receive decreased or no blood flow ) ., During acute skeletal muscle ischemia in vivo , P-selectin , E-selectin , VCAM-1 , and ICAM-1 are upregulated on the luminal vessel wall of the endothelium ., In our ABM , all of these CAMs were upregulated by EC agents after the simulated ischemic injury ( injury induced at time\u200a=\u200a300 seconds ) ., Extravasation of simulated monocytes gradually increased throughout the simulations as increased CAM expression facilitated increased extravasation ( Figure 2 ) ., Adhesion molecule expression prior to the simulation of the ischemic injury ( time\u200a=\u200a0–300 seconds ) was relatively stable , as expected , and remained stable indefinitely in the absence of an ischemic event ( data not shown ) ., Specifically , simulations in “healthy microvascular networks” were performed for 2 , 400 seconds with and without simulated hASC populations ., Acute ischemic injuries are characterized by alterations in circulating chemokine and cytokine levels in animal models and in humans ., Implicated chemokines and cytokines during vascular injury include stromal cell-derived factor-1alpha ( SDF-1α ) , interleukin-1beta ( IL-1β ) , interleukin-8 ( IL-8 ) , interleukin-10 ( IL-10 ) , monocyte chemoattractant protein-1 ( MCP-1 ) , tumor necrosis factor-alpha ( TNF-α ) , transforming growth factor-beta ( TGF-β ) , and nitric oxide ( NO ) , which formed the basis for their inclusion in the rule-sets 4 , 5 , 30 ( Text S1 ) ., In ABM simulations , the secretion of inflammatory chemokines and cytokines by ECs was increased ( Figure 3 ) ., Furthermore , circulating monocytes were increasingly exposed to circulating chemokines and cytokines ( Figure 4 ) , as expected ., Unfortunately , in vivo data for the absolute amount of inflammatory chemokines and cytokines being secreted by ECs during ischemia is inconsistently reported in the literature , and so it could not serve as comparison here ., Rather , their increased ( relative ) presence serves as verification ( Text S1 ) ., Consistent with an earlier ABM 23 , monocyte extravasation was shown to be unaffected in E-selectin knockouts , P-selectin knockouts , and only marginally in double selectin knockouts ( 10% decrease ) ., Triple-selectin knockouts virtually eliminated instances of rolling and extravasation ( 78% reduction; Figure 5 ) ., This agrees with experimental literature showing that monocytes are able to proceed through the adhesion cascade non-sequentially and incorporate into extravascular spaces without rolling on the selectins prior to firm adhesion ( Table 2 ) ., Simulating a knockout of ICAM-1 significantly inhibited monocyte extravasation ( 55% reduction; Table 2 ) , and it appears that rolling behavior was affected , as well ( Figure 5 ) ., Average monocyte rolling distances ranged from 72 . 6±14 . 7 to 198±57 . 5 µm in injured and healthy tissue , respectively ., The rolling distances of monocytes that eventually extravasated did not significantly differ from that of monocytes that failed to extravasate , in either healthy or ischemic tissue ( Figure 6 ) ., In vivo , leukocyte rolling distances have been reported to range from 30–400 µm ( Table 2 ) ., Within the rule-set , adhesion molecule expression for CD15s , CD34 , CD65 , E-selectin ( CD62e ) , P-selectin ( CD62p ) , L-selectin ( CD62L ) and PSGL-1 ( CD162 ) were included for hASC populations ( Table 3 and Text S1 ) because these molecules have all been shown or proposed to support selectin-mediated rolling ., Based on independent in vivo experiments wherein hASCs were delivered intravenously to ischemic mouse hindlimbs and a review of relevant stem cell literature ( Text S1 ) , we expected the efficiency of hASC incorporation into ischemic tissue to be approximately 10% of the total cells delivered , which would also be 3–5× that of levels quantified in non-ischemic tissue ., Simulations of hASC trafficking to non-ischemic tissue produced an incorporation efficiency of 1 . 38±0 . 43% ( i . e . , approximately 1–2% of the total injected circulating cells extravasated into the injured extravascular space ) ., Therefore , we anticipated simulations of hASC trafficking to ischemic tissue to produce an incorporation efficiency of 3–10% in silico ., The rule-sets , as instituted in prototype models , however failed to reproduce this in silico; incorporation efficiency was less than 5% ( 4 . 49±0 . 88; Figure 7 ) and thus at the lower-end of expected values ., This inconsistency indicated that there likely existed an additional adhesion molecule in vivo that was not accounted for by the rules in our initial ABM ., We hypothesized that an unknown selectin-binding molecule , similar to PSGL-1 , could account for this disparity , and conducted simulations to test this hypothesis by including additional rules in the ABM rule-set ., Rules for the hypothesized CAM , termed “selectin binding molecule-X” or SBM-X , included the ability to bind all of the selectins and a 75% probability of expression by hASCs ( Text S1 ) ., This increased the simulated levels of hASC extravasation to 8 . 9±1 . 47% of those delivered ( Figure 7 ) , which was in the `expected range ., Conversely , knocking out CD34 expression on circulating hASCs in silico had no significant effect on extravasation in ischemic tissue ( Figure 7 ) , nor did knocking out SBM-X expression on circulating hASCs have a statistically significant effect in healthy tissue ( data not shown ) ., Our ABM simulations , therefore , predicted the necessity and ability of therapeutically delivered hASCs to roll on the selectins , independent of PSGL-1 ., The ABM simulations predicted the importance of selectin-mediated rolling for hASC trafficking ( Figure 7 ) , despite the fact that hASCs do not express the main ligand for the selectins , PSGL-1 ., This hypothesis was tested in vitro using a parallel plate flow chamber ., In the laminar flow assay , where hASCs were perfused over substrates containing immobilized P-selectin , a small percentage of hASCs ( <1% ) were observed interacting with and slowly rolling on immobilized P-selectin ., Rolling speeds as low as 2 µm/s were quantified at two levels of WSS ( 0 . 5 dyne/cm2 and 1 . 0 dyne/cm2 ) ( Figure, 8 ) and were below that measured for the negative controls ( Tween 20 for non-specific adhesion; Fc IgG for human IgG control ) and below 20% of the free-stream velocity ., The incubation of hASCs with competitive antibodies to PSGL-1 had no effect on hASC rolling speeds ( white squares ) ., However , incubating substrates with competitive antibodies to P-selectin eliminated instances of rolling ( data not shown ) , thus confirming that rolling in this assay was mediated by the immobilized P-selectin ., Results were consistent across multiple donors and passages ( Figure 8 ) ., Plots of the instantaneous speeds of rolling hASCs ( Figure, 9 ) were typical of rolling leukocytes 31 , specifically the characteristic stop-and-go behavior ., Flow cytometry analysis showed positive surface expression of CD24 by early , middle , and late-passage hASCs ( Table 4 ) ., Expression was variable and may have been a function of donor and/or passage number ., Pooling all data , mean expression level was 6 . 65±6 . 7% positive ( min\u200a=\u200a0 . 77% , max\u200a=\u200a21 . 1% ) ., Selectin-mediated rolling via CD24 has been shown to occur in other cell populations ., This remains to be determined for an hASC population , although constitutive CD24 gene expression has been previously reported 32 ., A better understanding of the homing processes used by therapeutic cells will lead to the development of new and more effective cell-based therapies ., For example , the identification of a bottleneck in the adhesion cascade could inform molecular targeting strategies to increase trafficking efficiencies of hASCs following their i . v . delivery during treatment of ischemic injury ., And with this study , we now know that approximately 1% of hASCs are capable of slowly rolling on P-selectin , which may limit their homing in pre-clinical studies ., Furthermore , this 1% may account for the majority of the cells that are able to successfully home in vivo , in which case methods to enhance these P-selectin adhesive interactions should be developed ., These findings need to be verified in vivo and their mechanisms of action more clearly elucidated before new therapies can be developed ., The development of this and other strategies , however , has been hampered by the complexity of circulating cell homing and the fields incomplete understanding of governing mechanisms ., It is true that detailed mechanistic data on the leukocyte adhesion cascade is available , but it is still unclear if the same governing principles universally apply to circulating hASCs ., This combined with the fact that investigation of all relevant parameters individually in vitro was not immediately feasible ( time and resource-prohibitive ) , prompted us to undertake a computational approach in our study of hASC homing ., We developed an ABM of acute skeletal muscle ischemia and simulated the therapeutic delivery of hASCs ., For model verification , we performed a series of in silico knockout experiments ., Data indicated a prominent role for ICAM-1 in adhesion and extravasation of monocytes , and a less important role for the selectins , which was consistent with independent experimental findings ., Furthermore , the model reasonably reproduced key aspects of skeletal muscle ischemia and monocyte behavior , including expected changes in hemodynamics , upregulation of key adhesion molecules on the endothelium , enhanced secretion of inflammatory chemokines and cytokines , and monocyte rolling distances ., Successful ABM verification prompted a series of systematic knockouts in silico aimed at identifying bottlenecks in the hASC-specific adhesion cascade ., Simulations predicated the necessary expression of an unknown selectin-binding molecule ( SBM-X ) to achieve expected levels of hASC homing ( in contrast to circulating monocytes which were found in the simulation to be insensitive to selectin expression ) ., This does not unequivocally confirm that there were not other mechanisms that would have given this same effect in silico , had we hypothesized them ( hence the danger inherent in this type of modeling ) ., Rather , the model suggested that hASCs possess the capability to roll on selectins using an un-identified adhesion molecule , in addition to any rolling behavior mediated by CD15s , CD34 , CD62p , CD65 , E-selectin , or P-selectin expressed on hASCs ., Essentially , the failure of the model prompted the formation of a novel hypothesis that we were able to test in silico and in vitro , which illustrates the power and flexibility of utilizing computational techniques ., The ABM presented here does not accurately reproduce all aspects of acute skeletal muscle ischemia , nor does the rule-set adequately account for the entirety of potential mediators of circulating cell trafficking ( e . g . , hypoxia , Interleukin-4 , proliferation , vasodilation , angiogenesis ) ., It was impractical to account for all aspects in silico , and this was not the goal of this first generation ABM ., The goal was to develop a computational tool capable of investigating certain aspects of circulating cell trafficking in response to ischemia ., This tool was used to generate a new hypothesis , which , when tested in the ABM , informed future experiments ., The ABM , therefore , was valuable in that it was able to direct future research by synthesizing available literature , thus instantiating our , and others , hypotheses ., An important part of computational research is the pairing of in silico data with independent in vitro and/or in vivo data 33 , 34 ., As such , we validated the predictions of the ABM with independent in vitro data documenting an ability of hASCs to slowly roll on P-selectin using an adhesion molecule other than PSGL-1 ., Approximately 1% of perfused cells slowly rolled at speeds as low as 2 µm/s , although it was unclear which adhesion molecule expressed by hASCs was mediating this interaction ., Flow cytometry data suggested that CD24 , a molecule capable of supporting the rolling of monocytes , neutrophils , and metastatic cancer cells 35 , 36 , was a likely candidate ., Whether CD24 expression by hASCs can support rolling on P-selectin should be verified in future inhibition studies using , for example , a parallel plate flow chamber assay ., Additionally , future generations of this ABM should account for CD24 expression levels to determine if this could replace the rule for SBM-X ., Regardless , the ABM was successful for two reasons: ( 1 ) simulations informed experimentation that may not have been preformed otherwise; and ( 2 ) in silico data led to a fundamentally new understanding of hASC biology ., For example , data on adhesion molecule expression indicated hASCs do not express PSGL-1 , the main ligand for the selectins , at the protein or gene level ., It was because of this that the ability of hASCs to undergo selectin-mediated rolling was not investigated in vitro until after the ABM suggested otherwise , even though it was known to be important for the trafficking of other circulating cell phenotypes ., Most importantly , however , the ABM aided our discovery of the ability of hASCs to dynamically interact with and slowly roll on P-selectin , an adhesion molecule preferentially expressed at sites of injury ., This could be significant to the future of injectable hASC therapies , as it suggests molecular targeting and/or sorting strategies to enhance hASC homing ., It may also offer evidence for why hASC homing during therapy is so inefficient ( i . e . , if only ∼1% of hASCs possess a required ability ) ., Collectively , this work illustrates how an ABM can be utilized to inform biological experiments and produce new biological understanding ., The ABM of therapeutic stem cell trafficking during acute skeletal muscle ischemia was created through modification of a previously developed ABM of monocyte trafficking in healthy skeletal muscle microvasculature 23 ., The most notable changes were a larger microvascular network ( approximately 5× larger to include more venules—the presumed site of extravasation ) , additional cell types ( hASCs and tissue macrophages ) , expanded rules for CAM expression and chemokine and cytokine activity , simulation of acute ischemic injury , and realistic handling of time within the simulation space ., The ABM was instituted in the Netlogo software program 27 ( version 3 . 1 ) on three workstations ., Over 150 rules obtained from independent , peer-reviewed literature were used to govern interactions between cells and their environment ( Table 5 and Text S1 ) ., The ABMs code is available for download at the Peirce-Cottler laboratory website ( http://www . bme . virginia . edu/peirce ) ., Skeletal muscle microvascular network architectures for simulations were obtained from mouse spinotrapezius tissues according to previously established protocol 23 , 37 ., Briefly , mouse spinotrapezius tissues were harvested , immunolabeled , and visualized using confocal microscopy ( Nikon , Model TE200-E2 with 20× objective ) ., Endothelial cells were identified by positive isolectin staining ( GS-IB4 conjugated to Alexa-568; Molecular Probes ) ., Montages of digital images were made , and networks were then discretized into elements ( vessels ) and nodes ( branch-points ) before being manually inputted into the simulation space ( Figure 10 ) ., In silico vessels preserved their in vivo characteristics , which were stored as agent variables , including vessel phenotype ( arteriole , capillary , or venule ) , vessel diameter , vessel-to-vessel connectivity , and vessel length ., Endothelial cell agents were simulated to be 46 µm in length ( 1 pixel equals 1 endothelial cell ) , and capillary bed properties were conserved ( ∼1 mm distance from feeding arteriole to draining venule ) ., The simulation space was discretized into square pixels , and the simulation space was 11 . 02 mm2 ( 4 . 69×2 . 34 mm ) containing 1654 endothelial cells ( Table 1 ) ., We approached the paradigm of circulating cell trafficking and EC adhesion as being critically dependent on four general components: receptor-ligand interactions , CAM expression , soluble and surface-bound chemokines and cytokines , and hemodynamics ., We believed these simplifications were adequate for investigations and development and institution of rules governing these properties are outlined below ., For additional clarification and detailed explanation of individual rules , please see Text S1 ., Types of interactions between circulating cells and the endothelium in silico included secondary capture and rolling on already adherent circulating cells ( Figure 11 ) ., Table 3 lists adhesion molecules and binding pairs present within the model ., Of note , there were expanded rules governing selectin binding , as this was the primary scope of interest within the ABM ., For example , rules governing CD15s 38 , CD34 39 , and CD65 40 expression were instituted in addition to PSGL-1 ( CD162 ) expression because these have been shown to facilitate selectin-mediated rolling ., PSGL-1 , in silico and in vivo , is constitutively expressed on monocytes and absent on hASCs ., This molecule , or more importantly , the ability to roll via selectins , has been identified as critical for circulating cell homing ., Because hASCs do not express PSGL-1 , we hypothesized that an additional adhesion molecule ( currently unknown ) must be capable of supporting slowly rolling on selectins , in addition to any levels supported by CD15s , CD34 , E-selectin , P-selectin , and CD65 ., This hypothesized adhesion molecule was termed “SBM-X” and we assigned identical rules to those that governed PSGL-1 behavior ., CAM expression rules ( Text S1 ) were formulated as the percent probability that an individual agent ( cell ) within the simulated population would be “positive” for , or expressing , a given adhesion molecule ., For example , a synthesis of available literature provided a basis for the rule that 27% of therapeutically delivered hASCs expressed CD54 ., This rule was applied stochastically , through a random number generator , on an individual basis and averaged to a CD54+ subpopulation representing ∼27% of the total population ., CAM expression was dynamic and was re-assessed at a minimum of once every time-step ( 1 time-step equals 1 second ) ., Often , multiple publications reported on hASC adhesion molecule expression levels , while also fitting the inclusion criteria ( Text S1 ) ., In these instances , expression data were averaged to obtain the stated rule ., In contrast , the rule for hASC expression of the simulated selectin-binding molecule ( SBM-X ) was theorized with no basis in the literature ., Rather , we hypothesized that additional mechanisms to facilitate selectin-mediated rolling may be required for hASC trafficking , and set SBM-X expression arbitrarily at 75% ., IL-1β , IL-8 , IL-10 , NO , SDF-1α , TNF-α , active TGF-β , and MCP-1 were present within the simulation space ., These chemokines and cytokines have been implicated in ischemic injuries and/or inflammation 4 , 5 , 30 ( Text S1 ) ., Chemokine and cytokine activity was simplified to consider only connections between exposure and subsequent behaviors ( change in CAM expression , chemokines or cytokine secretion , and/or integrin activation ) , in a binary manner for each chemokine or cytokine per cell per time-step ., This was because: ( 1 ) we were concerned with tissue-level changes across large spatial and temporal scales; and ( 2 ) data to account for detailed mechanisms ( time of secretion , time of exposure , potency , etc . ) was not available for all of the chemokines or cytokines ., From examined literature , chemokine and cytokine activity reported in vivo ( both secretion and induced changes in cellular behavior ) occurred as a function of location , concentration , time of secretion , time of exposure , diffusion limitations , cell history , cell phenotype , and others ., All of this information was not available in the literature for each chemokine or cytokine , making an in silico representation of chemokine or cytokine activity this detailed impossible ., However generic “promote” or “inhibit” behavior could be deduced from the literature ., Therefore , chemokine and cytokine activity in silico was simplified in this manner ( Text S1 ) and the emphasis was placed on cause-and-effect linkages between chemokine and cytokine activity and cell behavior ( Figure 12 ) ., There is precedence for the simplification of cellular behavior in this manner for use in computational modeling 41 ., If an agent ( cell ) were exposed to a chemokine or cytokine in silico , it would instantaneously either promote or inhibit a resultant cellular behavior , independent of most of the parameters outlined above , as long as there was evidence in the literature for the connection ., What was considered during rule formulation was cell phenotype , history , presence of other chemokines and cytokines , and diffusion limitations ., Consider the following example: The data from the example would be used to generate the following rule: There would also be a percent probability of action assigned to whether Cell Type A would see Chemokine A , and also a percent probability whether Chemokine B would be subsequently secreted ., Whether an agent can become exposed to a secreted chemokine depends , primarily , on its phenotype ., For example , circulating cells can become exposed if: ( 1 ) they come into contact with an endothelial cell that is acti

Introduction, Results, Discussion, Materials and Methods

Intravenous delivery of human adipose-derived stromal cells ( hASCs ) is a promising option for the treatment of ischemia ., After delivery , hASCs that reside and persist in the injured extravascular space have been shown to aid recovery of tissue perfusion and function , although low rates of incorporation currently limit the safety and efficacy of these therapies ., We submit that a better understanding of the trafficking of therapeutic hASCs through the microcirculation is needed to address this and that selective control over their homing ( organ- and injury-specific ) may be possible by targeting bottlenecks in the homing process ., This process , however , is incredibly complex , which merited the use of computational techniques to speed the rate of discovery ., We developed a multicell agent-based model ( ABM ) of hASC trafficking during acute skeletal muscle ischemia , based on over 150 literature-based rules instituted in Netlogo and MatLab software programs ., In silico , trafficking phenomena within cell populations emerged as a result of the dynamic interactions between adhesion molecule expression , chemokine secretion , integrin affinity states , hemodynamics and microvascular network architectures ., As verification , the model reasonably reproduced key aspects of ischemia and trafficking behavior including increases in wall shear stress , upregulation of key cellular adhesion molecules expressed on injured endothelium , increased secretion of inflammatory chemokines and cytokines , quantified levels of monocyte extravasation in selectin knockouts , and circulating monocyte rolling distances ., Successful ABM verification prompted us to conduct a series of systematic knockouts in silico aimed at identifying the most critical parameters mediating hASC trafficking ., Simulations predicted the necessity of an unknown selectin-binding molecule to achieve hASC extravasation , in addition to any rolling behavior mediated by hASC surface expression of CD15s , CD34 , CD62e , CD62p , or CD65 ., In vitro experiments confirmed this prediction; a subpopulation of hASCs slowly rolled on immobilized P-selectin at speeds as low as 2 µm/s ., Thus , our work led to a fundamentally new understanding of hASC biology , which may have important therapeutic implications .

Ischemic pathologies , such as acute myocardial infarction and peripheral vascular disease , continue to be associated with high morbidities and mortalities ., Recently , therapies wherein adult stem cells are injected into the circulation have been shown to increase blood flow and help to restore tissue function following injury ., Pre-clinical animal models and human trials have shown successes utilizing this approach , but variable trafficking efficiencies and low incorporation of cells into the injured tissue severely limit effectiveness and may preclude clinical adoption ., To address this , we sought to study the complex process of how injected stem cells traffic through the microcirculation and home to sites of injury , in an effort to identify bottlenecks in this process that could be manipulated for therapeutic gain ., We developed an agent-based computer model to speed the rate of discovery , and we identified a key cell–cell adhesion interaction that could be targeted to enhance stem cell homing efficiencies during injectable stem cell therapies .

cardiovascular disorders/vascular biology, cardiovascular disorders/peripheral vascular disease, biotechnology/bioengineering, cardiovascular disorders/hemodynamics, computational biology/systems biology

journal.pgen.1002869

Comparative Analysis of the Genomes of Two Field Isolates of the Rice Blast Fungus Magnaporthe oryzae

Rice blast caused by the heterothallic ascomycete Magnaporthe oryzae ( also known as Pyricularia oryzae ) is one of the most destructive diseases of rice , which is a staple for over half of the worlds population ., This pathogen also infects wheat and other small grains , and poses major threats to global food security 1 , 2 ., In the past two decades , rice blast has been developed as a model system to study fungal-plant interactions ., M . oryzae was the first plant pathogenic fungus to have its genome sequenced and made available to the public 3 ., In most parts of the world , rice blast is controlled mainly with resistant cultivars ., However , M . oryzae is notorious for its ability to overcome resistance based on race-specific R genes 4–6 ., New cultivars often lose their resistance within a few years of introduction ., Genetic variations in populations of the pathogen have been well-documented in many parts of the world 7 , 8 ., M . oryzae isolates are also known to lose virulence and female fertility during laboratory manipulations 1 and large chunks of genomic DNA can be lost spontaneously during cultivation on artificial media , such as the deletion of over a 40 kb region containing the BUF1 locus 9 ., The laboratory strain 70-15 of M . oryzae was generated by backcrossing a progeny from a cross between a rice isolate and a weeping love grass ( Eragrostis curvula ) isolate with the rice isolate Guy11 from French Guyana 10 , 11 ., It has been used in many laboratories and was selected for genome sequencing 3 ., Although most of the 70-15 genome should be from the rice pathogen after backcrossing with Guy11 several times , some weeping love grass pathogen sequences are likely retained ., In comparison with Guy11 , 70-15 is reduced in female fertility , conidiation , and virulence 12 ., To determine the extent of genetic variation among isolates of M . oryzae , we sequenced two field isolates Y34 and P131 ., Y34 was isolated from Japonica rice in 1982 in Yunnan province , China , where both Indica and Japonica rice cultivars are cultivated 13 , 14 ., Due to rich genetic diversity in rice cultivars and centuries of rice cultivation , highly diverse rice blast pathogen populations exist in Yunnan 15 , and hence Y34 was chosen as a representative from this region for sequencing ., The other field isolate , P131 , originated from Japan where Japonica rice cultivars are dominant 16 , 17 ., The isolates P131 , Y34 , and 70-15 differ in some cultural characteristics ( Figure S1 ) ., These three isolates also carry different avirulence genes and vary in aggressiveness toward different rice cultivars ( Table S1 ) ., In comparison with 70-15 , both Y34 and P131 have slightly larger genomes ., The two Asian field isolates share a higher degree of similarity and contain over 200 genes that are absent in 70-15 ., Many pathogenesis-related genes showed evidence of exposure to diversifying selection when comparing either field isolate ( P131 or Y34 ) to the laboratory strain ( 70-15 ) ., Functional characterization of randomly selected genes specific to the field isolates revealed that they play diverse roles , some of which affect virulence and others important for conidiation and vegetative growth ., Furthermore , thousands of loci with transposon-like elements were identified in each genome ., Many of them tend to be associated with the distribution of unique sequences and translocation of duplicated genes ., The genomes of P131 and Y34 were sequenced with the Sanger ( 2-fold ) and 454 sequencing technologies ( 18-fold ) ., The combined sequence reads for P131 and Y34 were 793 . 94 Mb and 843 . 92 Mb , representing about 20- and 21-fold genome sequence coverage , respectively ( Table 1 ) ., The 454 sequence reads were assembled into contigs and placed into scaffolds by the Newbler assembler with paired-end information from the Sanger reads ., The assembled P131 genome consisted of 1 , 823 scaffolds with a combined length of 37 . 95 Mb ., The N50 and maximum lengths of P131 scaffolds were 65 kb and 459 kb , respectively ( Table 1 ) ., The Y34 genome was assembled into 1 , 198 scaffolds with a combined length of 38 . 87 Mb ., The N50 and maximum length of Y34 scaffolds were 106 kb and 708 kb , respectively ( Table 1 ) ., Over 95% of the sequence reads were assembled into scaffolds >5 kb in both isolates ., Approximately 33% and 51% of P131 and Y34 sequences , respectively , were assembled into scaffolds longer than 100 kb ., In addition , the mitochondrial genomes of P131 and Y34 were also assembled ( Table 1 ) ., While P131 has an almost identical mitochondrial genome with 70-15 , Y34 lacks two short fragments with a combined length shorter than 350 bp ( Figure S2 ) ., Because repetitive sequences comprise approximately 10% of the genome of the laboratory strain 70-15 ( version 6 ) , repetitive sequences in the new assemblies were masked out with the RepeatMasker program for comparative analyses ., The resulting ATCG bases after masking were 37 . 6 Mb , 38 . 2 Mb , and 37 . 5 Mb , respectively , for P131 , Y34 , and 70-15 ( Table 1 ) , indicating that the core genomes of these three isolates were not significantly different in size ., However , because repetitive sequences and singletons smaller than 2 kb were not included in this analysis , it remains possible that the complete genomes of these three isolates vary in abundance of repetitive sequences and actually have greater size differences ., Scaffolds of P131 and Y34 were aligned with the assembled genome of 70-15 ( Figure 1 ) ., Overall , most of the 70-15 genome ( 96% ) is also conserved in two field isolates ., Only 0 . 45 Mb of sequence in 70-15 are absent from the two field isolates ., In contrast , P131 and Y34 have 1 . 69 Mb and 2 . 56 Mb isolate-specific sequences , respectively ., In general , isolate-specific sequences were dispersed throughout the genomes ., For individual chromosomes , there are regions enriched for isolate-specific sequences ( Figure 1 ) ., Blocks of such sequences can be found at both ends of chromosome IV and at single ends of chromosomes I , II , III , V , and VI ., In M . oryzae , genetic variation and avirulence genes are known to be enriched near the telomeres 18 , 19 ., Comparative analysis of the genomes of these three M . oryzae isolates revealed that genes responsible for variations in virulence and adaptation to the environment may be concentrated at the chromosomal ends ., To locate and verify isolate-specific sequences in the field isolates , we used clamped homogenous electric fields ( CHEF ) gel electrophoresis to separate the chromosomes ., Chromosome size polymorphisms were observed among these three isolates ( Figure 2A ) ., Whereas chromosome VII ( the smallest chromosome ) in 70-15 was estimated to be 4 . 3 Mb , the smallest chromosomes in Y34 and P131 were approximately 1 . 8 Mb and 2 . 5 Mb , respectively ., When one P131-specific sequence , P131_scaffold00006_11 , which was not mapped on the chromosome alignment was used as the probe , an aggregate band of chromosomes larger than 6 . 0 Mb was detected in P131 but not in Y34 nor in 70-15 ( Figure 2B ) ., When a similar blot was probed with an Y34-specific sequence , Y34_scaffold00824_1665 , only the smallest chromosome of Y34 was hybridized ( Figure 2B ) ., These findings confirm that the field isolates contain isolate-specific DNA ., Because the assembly of P131 or Y34 relied on the alignment with the 70-15 genome , it was not possible to accurately map P131 and Y34 sequences that were absent from the 70-15 genome assembly ., However , the P131 and Y34 sequences could be used to fill the sequence gaps ( ≥50 bp ) in the 70-15 assembly ., We identified the end sequences of the contigs or scaffolds flanking these gaps ., After filtering out simple repeats , these sequences were used to search against the assembled P131 and Y34 sequences ., If both upstream and downstream flanking sequences of one gap were mapped on the same contig in either P131 or Y34 , the in-between sequences were used to fill the gaps of 70-15 ., A total of 55 gaps were filled with sequences from P131 or Y34 ( Table 2 ) ., Among them , 35 gaps had the sequences present in both P131 and Y34 ( Table 2 ) ., The total gap sequence filled in the 70-15 genome was 25 . 3 kb ., We randomly selected 18 of these filled gaps of the 70-15 genome for verification ., All of them were confirmed in 70-15 by PCR ( Figure S3 ) ., The number of predicted genes in the masked genomes of P131 , Y34 , and 70-15 was 12 , 714 , 12 , 862 , and 12 , 440 ( Table 1 ) , respectively ., The average length of predicted proteins was over 400 amino acids ., Y34 apparently has the largest genome size and gene content , which may contribute to its adaptation to the environment or to rice cultivars grown in Yunnan province , China ., To identify the gene pool of these three strains , the predicted amino acid sequences of the total gene set from each isolate were used to search against the nucleotide sequences of other two isolates by TBLASTN ., The large majority of M . oryzae genes ( 12 , 375 from P131 , 12 , 431 from Y34 , and 12 , 214 genes from 70-15 ) share sequence homology in pair-wise comparisons ., Among these genes constituting the ‘core’ gene set of the M . oryzae genome ( Figure 3A ) , 11 . 3% had no orthologous sequences in other organisms ., Moreover , approximately 10 . 1% of these M . oryzae-specific genes were predicted to encode secreted proteins ., To improve gene annotation in 70-15 , we identified the genes that were common to all three isolates and had similar sizes ( difference less than 1% ) between Y34 and P131 but were 50 amino acids or 20% longer or shorter in 70-15 ., A total of 340 genes meeting these criteria were then manually annotated ., Among them , 135 genes in 70-15 had incorrect intron annotations ., The number of genes with inaccurate start or stop codon predictions was 259 or 15 , respectively ( Table S2 ) ., The number of genes shared only by the two field isolates ( 198 from P131 and 220 from Y34 ) was approximately twice that of those shared by either P131 or Y34 with 70-15 ( Figure 3A ) , implying that the two Asian field isolates share a higher degree of similarity and with about 200 genes that are absent in 70-15 ., For isolate-specific genes , we found that 51 , 136 , and 71 genes were unique to P131 , Y34 , and 70-15 , respectively ( Figure 3A ) ., All the genes randomly selected for verification were confirmed by PCR to be either shared by two isolates or unique to one specific isolate ( Figure S4 ) ., As found in 70-15 , isolates P131 and Y34 also had various copies of DNA helicase Q genes and LTR elements towards the chromosomal ends 3 ., For the genes common in Y34 and P131 but absent in the automated annotation of 70-15 , we used their amino acid sequences to search the 70-15 scaffolds ., The resulting homologous sequences of 70-15 were then used to search against M . oryzae ESTs deposited in GenBank ., A total of 81 candidate genes were identified in the 70-15 genome and ESTs ( Table S3 ) ., Seventy-six of them encoded hypothetical proteins with no known homologs in GenBank ., Some of these M . oryzae specific genes may be important for the virulence or fitness of the pathogen because all three isolates have these genes ., The other five genes had orthologous sequences of unknown functions in Sordariomycetes but were absent in lower fungi , such as Zygomycetes and Saccharomycetales ., To further analyze genetic relatedness of these three isolates , the 10 , 074 clusters containing one protein from one isolate were selected and the resulting individual protein sequences from each isolate were combined for distance analysis with PHYLIP ., As shown in Figure 3B , the two field isolates have a closer relationship to each other than with the laboratory strain 70-15 ., Based on analyses of gene content , 51 , 136 , and 71 genes , respectively , were unique to P131 , Y34 , and 70-15 ., Overall , 13% of these isolate-specific genes encoded secreted proteins and 46% of them had no significant homolog in GenBank ( Table S4 ) ., RT-PCR analyses were performed with 10 and 14 randomly selected P131- and Y34-specific genes , respectively ., All the selected genes were confirmed to be expressed in mycelia ( Figure S5 ) ., While most of the isolate-specific genes were dispersed through the genome , some were located within clusters ( Figure 1; Table S4 ) ., For example , scaffolds 00875 and 01112 of Y34 contained five and eight of the Y34-specific genes , respectively ., In P131 , there were three isolate-specific genes each on scaffolds P131_scaffold01777 and P131_scaffold01784 ., Moreover , many of the isolate-specific genes with known chromosomal positions in P131 and Y34 were located near the chromosomal ends ( within 500 kb ) , which is consistent with the distribution tendency of isolate-specific sequences ( Figure 1 ) ., To determine the biological function of these isolate-specific genes , nine Y34-specific genes and three P131-specific genes were selected for functional characterization ., For majority of them , the resulting gene deletion mutants had no obvious changes in colony growth , conidiation , or virulence ( Figure S6 ) ., Their functions in plant infection may be redundant or too minor to be detected under laboratory conditions ., However , deletion of one P131 unique gene , P131_scaffold00208-2 , resulted in a reduction in virulence in infection assays with seedlings of a susceptible rice cultivar ( Figure 4A ) ., Deletion of another P131 unique gene , P131_scaffold01777-7 , resulted in approximately 10% growth reduction on oatmeal tomato agar plates ( Figure 4B ) ., Proteins encoded by these two P131-unique genes were predicted to be localized in the nucleus ., Homologous sequences of these two genes were not found in other sequenced fungal species ., Moreover , deletion of one Y34 unique gene , Y34_scaffold00875-3 , resulted in approximately 36% reduction in conidiation ( Figure 4C ) ., Interestingly , deletion of one Y34-unique gene encoding a putative G protein-coupled receptor ( GPCR ) -like integral membrane protein with six transmembrane domains resulted in changes in pathogenicity on a rice cultivar carrying the Pi-7 R gene , suggesting that this Y34-unique gene might be the potential AVR Pi-7 gene ( data not shown ) ., Among the genes shared by both field isolates P131 and Y34 but absent in 70-15 , 19% had signal peptides for secretion and 12% had transmembrane domains ( Figure 3; Table S5 ) ., About 70% of these genes had no functional annotation ., Strain 70-15 may have lost these genes during the initial genetic cross or after generations of cultivation in the laboratory ., For example , a gene encoding a CFEM-containing GPCR-like protein 20 and the avirulence gene AVR Pi-a 21 were present in the field isolates P131 and Y34 but not found in 70-15 ., Duplication is one of the major mechanisms for evolutionary innovation ., The total duplicated genomic DNA fragments ( longer than 500 bp and greater than 90% identity ) were 289 kb , 385 kb , and 825 kb in P131 , Y34 , and 70-15 , respectively ., A total of 16 , 20 , and 155 predicted genes in P131 , Y34 , and 70-15 , respectively , were located in these duplicated sequences ( Table S6 ) ., Although duplicated DNA sequences were detected genome-wide in all three isolates , in general chromosomes II , IV , V , and VII had more duplicated DNA sequences than other chromosomes ( Figure 5A ) ., For individual chromosomes , the end regions tend to contain more duplicated DNA sequences than the central region ., Comparative analysis indicated that P131 , Y34 , and 70-15 all contained isolate-specific duplicated regions ( Figure 5A ) ., However , the laboratory strain 70-15 had significantly more duplicated genes , including the AVR gene PWL2 22 ( Table S6 ) ., Other duplicated genes with known functions include LPS glycosyltransferases , MFS transporters , sugar transporters , and carboxypeptidases ., Both intra- and inter-chromosomal duplications were observed , but more inter-chromosomal duplications were apparent , and only a small portion of duplication events were conserved in all three isolates ( Figure 5A ) ., To identify gene families , the entire set of the predicted proteins from all three isolates were clustered with the OrthoMCL program ., A total of 38 , 016 proteins were grouped into 14 , 189 clusters with each cluster representing a group of putative orthologs ., Among these clusters , 195 gene families were identified with more than one member in at least one isolate ( Figure 5B ) , suggesting that 1 . 37% of the M . oryzae genes may have been evolved by gene family expansion ., Among 45 clustered loci duplicated equally in each isolate , 38 , 6 , and 1 gene loci were duplicated between two , three or four times , respectively , per isolate ( Table S7 ) ., These gene families might have existed before the divergence of the three isolates ., The majority of these gene families were predicted to be involved in synthesis and transport of nutrition and secondary metabolites , suggesting that they may be related to plant infection ( Table S7 ) ., There were 87 clustered loci duplicated at different frequencies in three isolates ( Table S8 ) ., Most of these gene families ( 61 out of 87 ) contained duplicated genes in only one isolate , and 17 gene families contained gene loci duplicated at least three times in one or more isolates ( Table S8 ) , suggesting that they have been expanded or contracted in different strains , possibly during environmental adaptations ., For example , one putative calcium P-type ATPase gene was duplicated three times in P131 and Y34 , and twice in 70-15 ., Members of this gene family have been demonstrated to be required for disease development and induction of host resistance 23 , 24 ., For loci duplicated in two isolates but absent in the third one , there were eight in P131 and Y34 , five in P131 and 70-15 , and twelve in Y34 and 70-15 ( Figure 5B; Table S9 ) ., Most of these expanded gene families had unknown functions ., To confirm the duplication events that were unique to the two field isolates , three genes were selected by Southern blot analysis ., All of them were confirmed to be specifically duplicated in P131 and Y34 but not in 70-15 ( Figure S7 ) ., There were seven , thirteen , and eighteen gene families specifically expanded in P131 , Y34 , and 70-15 , respectively ( Figure 5B; Table S10 ) ., Most of these isolate-specific gene families contained two or three duplicated members that had unknown functions or no known homologs in GenBank ., To analyze asynonymous and synonymous nucleotide substitutions , we first identified and removed orthologous genes with large deletions or insertions in any of the isolates from the list of common genes ., In total , 9 , 184 highly conserved orthologs were used to identify nucleotide substitution events ., Among them , 7 , 569 genes had neither synonymous nor asynonymous nucleotide substitution in pair-wise comparisons , indicating that most of the genes were well-conserved among different isolates ., Only 428 genes had nucleotide substitutions between P131 and Y34 , and 1 , 651 genes had nucleotide substitutions between 70-15 and P131 or Y34 , further indicating that the field isolates had closer relationship with each other than with the laboratory strain ., Genes with substitutions in the 70-15 versus P131/Y34 comparison could be categorized into four groups: 414 genes only with synonymous nucleotide substitutions , 697 genes only with asynonymous nucleotide substitutions , 124 genes with Ka/Ks<1 , and 6 genes with Ka/Ks>1 ., Overall , similar numbers of genes identical between Y34 and P131 but with nucleotide variations in 70-15 were thought to have undergone diversifying versus purifying selections ., However , several functional categories of genes , such as those involved in cellular responses to stimuli and organophosphate metabolisms , had more members exhibiting diversifying selection in the two field isolates ( Table S11 ) ., Several of the genes underwent diversifying selection in the 70-15 versus P131/Y34 comparison ( Table S12 ) , including ATG4 , HEX1 , MCK1 , MoSNF1 , PTH2 , and RGS1 , which are known virulence factors in M . oryzae 25–30 ., Three of them encode putative CFEM-domain receptors that may be involved in recognizing different environmental and plant signals ( Table S12 ) ., Repetitive sequences were masked by Newbler for assembling 454 sequence data of P131 and Y34 ., To compare repetitive sequences of these two isolates , we assembled the Sanger reads of P131 and Y34 ( approximately 2-fold genome coverage ) and found that 10 . 8% , 10 . 3% , and 10 . 6% of the 70-15 , P131 , and Y34 genomes , respectively , were repetitive sequences , indicating that the abundance of repetitive sequences is similar among these three isolates ., Transposable elements ( TE ) and their insertion sites ( flanking sequences ) were identified by RepeatMasker ., Although the exact copy numbers vary , both field isolates contained all classes of transposable elements identified in 70-15 ( Table 3 ) ., In general , 70-15 has more members of the LINE , Maggy , and RETRO5 LTR retrotransposons ., The Pot2/Pot4 DNA transposons and the Pyret and Grasshopper LTR retrotransposons were more abundant in P131 and Y34 ., In addition , nine new clusters of repetitive sequences were identified by analysis with RepeatScout ( Table 3 ) ., However , none of them was unique to the field isolates ., While clusters 1 , 4 , 5 , 6 , and 7 were much more abundant in the field isolates , 70-15 had more copies of the cluster 2 repetitive elements ( Table 3 ) ., In comparison with 70-15 , the two field isolates were more similar in the distribution pattern of repetitive sequences ( Figure 1 and 6A ) ., While chromosomal ends tend to have more repetitive sequences , all three isolates had much reduced numbers of TEs in the gene-rich regions of chromosomes III , V , and VI ( Figure 6A ) ., For the TEs that could be assembled into the genome sequences , approximately 27% of them had the same locations in all three isolates by comparison of their flanking sequences ( Figure 6B ) ., Y34 had more TEs with unique chromosomal positions ( 1 , 061 ) than P131 ( 830 ) or 70-15 ( 976 ) ., In addition to the 603 locations of TEs conserved among the three strains , Y34 and P131 also shared 281 TEs with the same chromosomal locations , which was fewer than the 377 between 70-15 and Y34 or the 341 between 70-15 and P131 ( Figure 6B ) ., While over two-thirds of the members of some TEs , including Occan , had conserved genomic locations , TEs such as Retro5 and Maggy differed significantly in their chromosomal positions between Y34 and 70-15 ., Similar results were obtained with the P131 and 70-15 comparison ( Table 3 ) ., A total of 41 . 1% and 46 . 0% of TEs in 70-15 and P131 , respectively , had conserved genomic locations ., The Pot3 , Maggy , Retro5 , and Retro7 elements had the highest variation in chromosomal positions between 70-15 and P131 ., We also analyzed the impact of TEs on the genome evolution by comparing two-fold coverage Sanger data of P131 and Y34 with the 70-15 assembly ., A total of 35 , 38 , and 116 genes were disrupted by the insertion of TEs in P131 , Y34 , and 70-15 , respectively ( Table S13 , S14 , S15 ) ., Over 50% of the gene disruption events were caused by TEs belonging to MGL , Mg-SINE , or Pot2/Pot4 ., Strain 70-15 had a number of genes disrupted by cluster 7 , cluster 9 , Occan , and RETRO5 elements , which were not observed in P131 or Y34 ( Figure 6C ) ., Some of these genes may have been disrupted by transposition events occurring during generations of cultivation under laboratory conditions , and these genes may play roles in plant infection or survival in the field isolates but were not required for the laboratory isolate ., In comparison with 70-15 , the field isolates P131 and Y34 had more genes disrupted by SINE ( Figure 6C ) , which may indicate that these SINE elements were more active in these two field isolates ., Among all the genes disrupted by TEs in three isolates , only approximately one third of them have known functions based on their orthologs in GenBank , and most of them are involved in protein metabolism , transportation , transcription , or lipid metabolism ., The majority of the TE-disrupted genes encode hypothetical proteins with unknown functions ., Interestingly , 23 . 8% of them contained putative signal peptide sequences , which is significantly higher than the average percentage of predicted extracellular proteins in the genomes of these three strains ( Table S13 , S14 , S15 ) ., Some of them may function as effectors involved in fungal-plant interactions , such as AVR Pi-ta1 in 70-15 ( Table S13 ) ., In addition , 14 . 7% , 14 . 2% and 15 . 8% of the TE-disrupted genes in 70-15 , P131 , and Y34 , respectively , encoded proteins with putative nuclear localization sequences ., Intriguingly , the regions containing isolate-specific sequences or duplicated genes families were often near areas with high frequency of TEs ( Figure S8 ) ., In 70-15 , several TEs were found within 1 . 0 kb from 23 duplicated genes families , including the avirulence gene PWL2 ( Table S16 ) although many of these duplicated sequences were not closely linked or located on different chromosomes ., Taken together , it is likely that the transposition events of TEs might be related to translocation of duplicated DNA fragments and presence of isolate-unique sequences in these three strains ., In a number of eukaryotic organisms , comparative analysis of multiple genomes of the same species has been used to improve assembly and annotation and to identify genome variations 31–34 ., The rice blast fungus is well-known for its natural genetic variation 1 , 2 ., In this study , we sequenced two field isolates of M . oryzae from Asia ., Genome analysis indicated that these two field isolates are more closely related to each other than to 70-15 , which is a laboratory strain derived from three backcrosses of rice pathogen Guy11 with a progeny of a cross involving a weeping love grass pathogen , and maintained for many years under laboratory conditions ., The overall genome content and composition are similar among these three isolates , but the genomes of P131 and Y34 with only A/C/T/G and no Ns were slightly larger than that of 70-15 ., Although the 70-15 genome has been updated several times , it still has many gaps ( www . broadinstitute . org/annotation/genome/magnaporthe_grisea ) ., In this study , a total of 55 gaps of the 70-15 genome ( version 6 ) were filled in with sequences from P131 and Y34 , and the results were validated by PCR analyses of 70-15 ., This number of putative filled gaps with sequences from two isolates may seem low , but because of the short read length , the threshold set may have been too stringent ., For 35 gaps , they were filled with consensus sequences found in both field isolates ., For the gaps with sequences only available in either Y34 or P131 , the filling sequence for 70-15 was less certain , but of high probability because the overall nucleotide sequence identity between 70-15 with P131 or Y34 was over 98% ., Besides improving the genome assembly , the sequences of P131 and Y34 were used to improve the annotation of 70-15 ., We identified 81 genes that were not predicted in the automated annotation of the 70-15 genome sequence , and none of them were related to the sequence gaps ., In addition , we identified potential annotation errors in 340 predicted genes of 70-15 ., Most of them were related to the problems with the prediction of the boundaries of introns and start or stop codons ., Our study revealed that each M . oryzae isolate had some unique genomic DNA sequences ., Because genome sequences of P131 and Y34 were aligned with that of 70-15 , it was impossible to locate most of the sequences unique to Y34 and P131 onto specific chromosomes or chromosomal regions ., However , sequences unique to 70-15 were distributed over all seven chromosomes ., Because 70-15 was derived from three backcrosses of rice pathogen Guy11 with a progeny of a cross involving a weeping love grass pathogen , we expected that a small portion of its genome was from the weeping love grass pathogen ., The isolates Y34 , P131 , and 70-15 had 136 , 51 , and 71 unique genes , respectively ., Therefore , less than 1% of the predicted genes were unique to each isolate and these genes play diverse roles , some of which might possibly contribute to the specificity of individual isolates ., Some of the isolate-specific genes were clustered , suggesting that isolate-specific DNA fragments might be gained or lost during evolution ., The P131-specific gene P131_scaffold00208-2 encoded a hypothetical protein without known homologs in other fungi ., Deletion of this gene resulted in reduced virulence toward rice plants ., Because it might be involved in plant infection , P131_scaffold00208-2 may play an isolate-specific role in suppressing or overcoming plant defense responses ., These results suggest that some of the field isolate-specific genes may play important roles in plant infection ., In all three M . oryzae isolates , most of the duplicated genes are functionally unknown ., Duplicated sequences are distributed all over seven chromosomes and appear to be enriched in the telomeric regions ., For the duplicated genes with known functions , many of them are predicted to be involved in primary and secondary metabolism and interactions with the host ( such as cutinases and Avr proteins ) , which is consistent with earlier observations with 70-15 3 ., Interestingly , several gene families involved in synthesis and transport of nutrients and secondary metabolites were expanded with different frequencies in these three isolates ., Some of these duplicated genes may contribute to the adaption of M . oryzae to different environmental conditions ., Among the genes that had undergone diversifying selection in Y34 and P131 in comparison with 70-15 , a number of them are known to be important for virulence , suggesting that such genes may have been under strong selection pressure in their natural field environments ., There were six genes under positive selection in the two field isolates compared to 70-15 ., Two of them encoded two hypothetical proteins , a serine/threonine protein kinase , an acyltransferase , a putative catalytic domain of diacylglycerol kinase , and an aspartic-type endopeptidase ., Three of them are located on chromosome I ., In contrast , there were no genes showing positive selection in the comparison between the field isolates ., Because sexual reproduction has not been observed in the field , it is possible that translocations of the repetitive sequences may be one of the major sources for genome variation and rapid adaption to different host and environmental conditions ., Consistent with this hypothesis , over 10% of the genome sequences were found to be repetitive sequences ., In addition to TEs that have been identified in previous studies 3 , nine new clusters of repetitive sequences were identified in all three M . oryzae strains in this study ., Most of these TEs have different copy numbers in different isolates ., Strikingly , among thousands of TE loci , less than 30% of them were conserved among these isolates , suggesting active transposition of these TEs in M . oryzae ., Moreover , approximately 200 genes were totally disrupted by TEs in these three strains , and approximately 40% of them encoded extracellular or nuclear proteins , suggesting that transpositions of TEs may contribute to variations in host-microbe interactions and transcriptional regulation ., Interestingly , TEs tended to be found near isolate-specific sequences and duplicated DNA fragments ., It is possible that translocation of TEs is important for gain or loss of isolate-specific sequences and gene duplication events ., Overall , our results indicate that g

Introduction, Results, Discussion, Materials and Methods

Rice blast caused by Magnaporthe oryzae is one of the most destructive diseases of rice worldwide ., The fungal pathogen is notorious for its ability to overcome host resistance ., To better understand its genetic variation in nature , we sequenced the genomes of two field isolates , Y34 and P131 ., In comparison with the previously sequenced laboratory strain 70-15 , both field isolates had a similar genome size but slightly more genes ., Sequences from the field isolates were used to improve genome assembly and gene prediction of 70-15 ., Although the overall genome structure is similar , a number of gene families that are likely involved in plant-fungal interactions are expanded in the field isolates ., Genome-wide analysis on asynonymous to synonymous nucleotide substitution rates revealed that many infection-related genes underwent diversifying selection ., The field isolates also have hundreds of isolate-specific genes and a number of isolate-specific gene duplication events ., Functional characterization of randomly selected isolate-specific genes revealed that they play diverse roles , some of which affect virulence ., Furthermore , each genome contains thousands of loci of transposon-like elements , but less than 30% of them are conserved among different isolates , suggesting active transposition events in M . oryzae ., A total of approximately 200 genes were disrupted in these three strains by transposable elements ., Interestingly , transposon-like elements tend to be associated with isolate-specific or duplicated sequences ., Overall , our results indicate that gain or loss of unique genes , DNA duplication , gene family expansion , and frequent translocation of transposon-like elements are important factors in genome variation of the rice blast fungus .

Magnaporthe oryzae is the causal agent of rice blast that is mainly controlled with resistance cultivars ., However , genetic variations in the pathogen often lead to overcoming R gene-mediated resistance in rice cultivars ., In this study we sequenced two field isolates from China and Japan ., In comparison with the laboratory strain that was previously sequenced , the field isolates have a similar genome size and overall genome structure ., However , they have slightly more genes and contain a number of expanded gene families that are likely involved in plant-fungal interactions ., Each of the isolates has specific genes , some of which affect virulence and some others are important for asexual development ., The three strains differ noticeably in the distribution of transposon-like elements ., Many of the transposable elements tend to be associated with isolate-specific or duplicated sequences ., This study revealed genetic factors involved in genome variation of the rice blast fungus .

genetics, biology, genomics, evolutionary biology, genetics and genomics

journal.pcbi.1003338

Sharpness of Spike Initiation in Neurons Explained by Compartmentalization

Action potentials are generated in central neurons by the opening of sodium channels in the axon initial segment ( AIS ) 1 ., From patch-clamp studies , it is known that these channels open gradually with depolarization , with a Boltzmann slope factor of about 6 mV 2 , 3 ., Yet the onset of spikes recorded at the soma of cortical neurons appears very sharp , much sharper than would be expected in an isopotential membrane , according to standard biophysics 4 ., There is a distinct “kink” at spike onset , which appears in a voltage trace as a rapid voltage transition from the resting membrane potential ., This kink has been explained by the “lateral current hypothesis”: spikes are initiated in the axon and backpropagated to the soma , so that the kink reflects the sharpened current coming from the axon 5 , 6 , an observation already made in the early days of electrophysiology 7 ., In particular , the phenomenon can be replicated in multicompartmental models based on standard Hodgkin-Huxley formalism 8 , 9 , provided sodium channel density is high enough in the AIS 10 ., However , this explanation misses an important part of the story , because it focuses on the shape of action potentials , rather than on spike initiation per se ., Indeed several lines of evidence indicate that spike initiation is very sharp , and not only the initial shape of spikes seen at the soma ., First , cortical neurons can reliably transmit frequencies up to 200–300 Hz , and respond to input changes at the millisecond timescale 11 , 12 ., This is surprising because theoretical studies predict this effect for integrate-and-fire models 13 , which have sharp spike initiation , but not for isopotential Hodgkin-Huxley models 14 ., It was indeed shown that the cut-off frequency of signal transmission in the latter type of models is inversely related to the activation slope factor of Na channels ., On this basis , the cut-off frequency should be one order of magnitude lower than empirically observed ., Second , current-voltage relationships measured at the soma in vitro show an effective slope factor of about 1 mV , instead of the expected 6 mV 15 ., Third , spiking responses of cortical neurons to noisy currents injected at the soma are surprisingly well predicted by integrate-and-fire models 16 , and when models with parameterized initiation sharpness are optimized to predict these responses , the optimal slope factor is indistinguishable from 0 mV 17 ., These remarks imply that sharpness is a functionally relevant property of spike initiation rather than a measurement artifact ., In fact , there are two distinct sets of observations ., The first set focuses on the shape of spikes at onset , the “kink” seen at the soma in the temporal waveform of the action potential ., I will simply refer to this phenomenon as the “kink” at spike onset , that is , the abrupt voltage transition seen at the soma at spike onset ., Observations of the second set do not refer to the shape of spikes , but rather to the input-output properties of the spike initiation process ., Sharpness of spike initiation refers to the abrupt opening of Na channels at the initiation site when a threshold somatic voltage is exceeded ., Thus , it can be quantified as the somatic voltage interval over which available Na channels switch from mostly closed to mostly open: in a single-compartment Hodgkin-Huxley model , it would be on the order of 6 mV; in an integrate-and-fire model , it would be 0 mV ( no Na current flows until a spike is generated ) ; in a cooperative Na channel model , it would be in between ., Thus , to claim that spike initiation is sharp essentially means that spikes are initiated as in an integrate-and-fire model: a negligible amount of Na current flows until a threshold somatic voltage value is reached and a spike is suddenly produced ., Sharpness of spike initiation and the “kink” at spike onset are directly related in a single-compartment Hodkgin-Huxley model , but they are not necessarily equivalent in a spatially extended neuron ., Using a simple geometrical model consisting of a sphere and a thin cylinder , I give a parsimonious account of these observations by showing that spike initiation sharpness arises from the geometrical discontinuity between the soma and the AIS , rather than from backpropagation of spikes ., When Na channels are placed in the thin axon , they open abruptly rather than gradually as a function of somatic voltage , as an all-or-none phenomenon ., I further show that the phenomenon is governed by equations ( a bifurcation ) that are mathematically almost equivalent to the cooperativity model of Na channels 4 , 18 , even though the neuron model follows the standard Hodgkin-Huxley formalism ., I then show the relationship between spike initiation sharpness and the shape of spikes at the initiation site and at the soma ., In order to clearly demonstrate the phenomenon and avoid confounding factors , I consider a neuron model with only passive leak channels and Na channels ( low-threshold Kv1 channels are considered in the Text S1 , section 6 ) ., The activation curve of Na channels is a Boltzmann function , with half-activation voltage V1/2\u200a=\u200a−40 mV and slope factor ka\u200a=\u200a6 mV ( Fig . 1A ) , consistently with measured properties of Nav1 . 6 channels in the AIS 9 , 19 ., Neither Na channel inactivation nor potassium channels were included , so as to isolate the mechanisms responsible for spike initiation and avoid confounding factors ( these two factors contribute to repolarization and spike threshold adaptation ) ., In an isopotential neuron , the current-voltage relationship ( I–V curve ) , as measured by a voltage-clamp recording , is well described below V1/2 by the sum of a linear part , representing the leak current , and of an exponential part , representing the Na current 14 , 20 ( Fig . 1B ) ., The minimum of the curve is reached at a voltage value VT: this is the maximum voltage that can be reached with a constant current injection without triggering a spike ., This voltage is set by V1/2 and by the maximal conductance of the Na channel , relative to the leak conductance 21 ., The curvature of the exponential function is the slope factor , equal to ka , and sets the sharpness of spike initiation , as assessed for example by the cut-off frequency of signal transmission 11 , 14 ., However , neurons are not isopotential and spikes are initiated in the AIS , not in the soma ., In cortical neurons , the AIS is about 1 µm in diameter , and extends from the axon hillock over an unmyelinated length of 10–60 µm 22 ., Spikes are initiated about 20–40 µm from the soma 22 ., Na channels of the Nav1 . 6 subtype , which have a low half-activation voltage 19 , are concentrated in the AIS , more specifically in the distal part 9 ., Thus I now consider a simple geometrical model of the neuron consisting of a sphere ( diameter: 50 µm ) and a thin cylinder , with a diameter of 1 µm ., The cylinder extends over a long distance ( 300 µm ) to avoid artifactual boundary effects ., Many neurons also express Na channels in the soma , but since these are not involved in spike initiation , they were not included in the model ( except in section “The initial shape of spikes” below; they are responsible for the biphasic nature of phase diagrams 8 ) ., Na channels are placed at a single point in the axon , which we shall call the initiation site ., The I–V curve measured at the soma now reflects the sum of the leak current and of the lateral current coming from the axon ( Fig . 1C ) ., As is shown in Fig . 1C , an interesting phenomenon occurs as the initiation site is moved away from the soma , while keeping the same maximal Na conductance: the I–V curve becomes sharper , and the voltage at the minimum of the curve also decreases ( from −61 mV at the soma to −65 mV at 100 µm away ) ., At 40 µm away from the soma ( red ) , the I–V curve appears indeed much sharper than when Na channels are at the soma ( blue ) ., The increased sharpness due to the lateral current coming from the initiation site has been attributed to the active backpropagation of the distally initiated spike 5 , 8 ., However , the initiation site is only a small fraction of the axons space constant away from the soma ( which is 700 µm in this model ) ., As a matter of fact , in the present model , there is no active backpropagation since all channels are concentrated at a single location ., Fig . 1D shows that the sharpness of the I–V curve directly reflects the abrupt opening of Na channels at the initiation site as the somatic voltage is increased ., I now quantitatively define the sharpness of spike initiation as half the somatic voltage interval over which the proportion of open Na channels rises from 27% to 73% ( dashed lines ) ., When Na channels are at the soma , this quantity equals ka , the Boltzmann slope factor of the Na activation curve ( 6 mV in this model , in accordance with patch-clamp measurements 9 , 19 ) ., But this quantity drops to 2 mV when channels are 20 µm away from the soma , and to 0 . 1 mV at 40 µm ( 0 . 03 mV at 100 µm ) ., In other words , when channels are located at 40 µm away from the soma , they open essentially in an all-or-none fashion when the somatic voltage is increased above a threshold value ., This phenomenon is not due to active backpropagation , since sharpness is defined for Na channels at the initiation site; in addition there are no Na channels between the soma and the initiation site in the present model ., Fig . 1E shows what happens along the axon as the soma is depolarized by steps of 3 mV , from −64 mV to −55 mV ., At low voltages , the axon is effectively space clamped: the voltage along the axon is essentially equal to the somatic voltage ., Indeed cable theory shows that , when Na channels are closed and for an infinite cylindrical axon , voltage decays exponentially along the axon with a space constant of magnitude 1 mm ( 700 µm in this model ) , which is essentially constant at this spatial scale ., For a shorter cylindrical cable ( 300 µm in these simulations ) , voltage decay is even slower ., At some critical voltage , Na current flows through the membrane at the initiation site ., Because the soma is large compared to the axon , it acts as a current sink: most Na current flows to the soma ., This can be seen in the top curve in Fig . 1E , where lateral currents are proportional to the slope: there is a large slope towards the soma , and a horizontal slope towards the distal end of the axon ., As a result , there is a loss of space clamp: the voltage now peaks at −25 mV at the initiation site , while it is −55 mV at the soma ., Fig . 1F shows the voltage at the initiation site as a function of the somatic voltage ., At about −56 mV , a loss of voltage control occurs and the somatic and axonal compartments are effectively decoupled ., This phenomenon underlies the fact that voltage-clamp recordings at the soma capture large spikes of inward current when a threshold voltage is exceeded 23 ., Mathematically , this loss of voltage control corresponds to a bifurcation , that is , a sudden change in the equilibrium points of the system when a parameter ( here somatic voltage ) is changed by a small amount ., The Na current is a function f ( Va ) of the voltage Va at the initiation site ., If the soma acts as a current sink , then the lateral current must be equal to the Na current ., This corresponds to a simplified electrical model that approximates the spatially extended model , in which the initiation site and the soma are connected by a resistor , and the Na current is inserted at the initiation site ( Fig . 2A ) ., In this section , I analyze the properties of this simplified model , and then I derive theoretical predictions that match numerical simulations of the ball-and-stick model ., The lateral current is given by Ohms law: I\u200a= ( Va−Vs ) /Ra , where Vs is the somatic voltage and Ra is the axial resistance between the soma and the initiation site , which is proportional to the distance ., Thus at any time the axonal voltage Va is determined by the somatic voltage Vs through a non-linear equation , which expresses the equality of the lateral and Na currents: ( Va−Vs ) /Ra\u200a=\u200af ( Va ) ., This equation , which I shall call the current equation , is almost equivalent to the cooperativity model of Na channels 4 , 18 , and therefore has the same properties ., Fig . 2B shows the Na current ( red ) as a function of Va for an initiation site at 20 µm from the soma , corresponding to the green curves in Fig . 1C–D ., The black curves show the lateral current as a function of Va for a somatic voltage Vs of −60 , −55 and −50 mV ., The value of Va is determined by the intersection of the red and black curves: −59 , −52 and −40 mV ., Thus Va is amplified compared to the somatic voltage but still varies continuously with it ., When the initiation site is at 40 µm from the soma , corresponding to the red curves in Fig . 1C–D , a qualitatively different situation occurs , as shown in Fig . 2C ., Compared to the previous case , the only difference is that the axial resistance Ra is twice larger ., But now as the somatic voltage is increased , the intersection point suddenly jumps from about −55 mV to −25 mV ., This occurs because the number of solutions to the current equation changes from 3 to 1 when Vs is increased , that is , a bifurcation occurs with respect to variable Vs . It corresponds to the loss of voltage control seen in Fig . 1F ., Graphically , this bifurcation occurs when the line representing the lateral current ( black ) is tangent to the curve representing the Na current ( red ) at the intersection point ., This can only happen if the slope of the line ( 1/Ra ) is smaller than the maximum slope of the Na curve ., Thus there is a critical value of Ra above which spike initiation becomes sharp ., At this point , represented in Fig . 2D , the black line is tangent to the red curve at the inflexion point ., This critical value can be calculated as a function of parameters ( see Text S1 , section 1 ) ., With the Na channel properties used in this model , the condition for sharpness is approximately: Ra . gNa>0 . 27 , where Ra is the axial resistance to the initiation site and gNa is the total maximal conductance of Na channels ., The axial resistance is determined by the geometry of the AIS and by the intrinsic resistivity Ri ( 150 Ω . cm in this model ) ., The condition can then be written Ri . gNa . x/d2>0 . 21 , where d is the axon diameter and x is the distance of the initiation site away from the soma ., For the present model , the critical point occurs when the Na channels are placed at distance x\u200a=\u200a27 µm away from the soma ( see Fig . 2D ) ., The spike threshold can be defined as the voltage at the bifurcation point , which is when the line representing the lateral current ( black ) is tangent to the curve representing the Na current ( red ) at the intersection point ., Mathematically , this is obtained by differentiating the current equation with respect to Va: 1/Ra\u200a=\u200af′ ( Va ) ., A simple calculation shows that the threshold is higher at the initiation site than at the soma by an amount ka ( see Text S1 , section 2 . 1 ) , the Boltzmann slope factor of the Na activation curve , which is about 6 mV according to patch-clamp measurements ., This is in very close quantitative agreement with dual whole-cell recordings in the soma and AIS of cortical cells 19 ., The somatic spike threshold can also be calculated ., A full equation is given in the Text S1 ( section 2 . 2 ) , which can be approximated as follows:This is similar to the equation derived for an isopotential neuron 21 , but a striking difference is that the threshold does not depend on the leak conductance ., In effect , the equation is almost identical , with the leak resistance ( 1/gL ) replaced by the axial resistance Ra ., This equation implies that the spike threshold decreases logarithmically with the distance of the initiation site ., Fig . 2E shows the spike threshold as a function of the distance of the initiation site in logarithmic scale , as given by the full equation ( black ) and by the above approximate equation ( blue ) ., This is compared to the somatic voltage at which half of the sodium channels are activated in the numerical simulation of the ball-and-stick model ( red ) : as predicted , it decreases logarithmically with distance and is only about 2 mV above the predicted values ., At the critical distance ( 27 µm here ) , the spike threshold is a constant that depends only on Na channel properties , and is independent of geometry ( dashed line ) ., It equals −55 . 6 mV with the chosen parameters ( see calculation in the Text S1 ) ., Many empirical discussions have focused on the shape of action potentials at onset: a “kink” is indeed observed in cortical neurons , as if the action potential were suddenly rising from nowhere 4 , 6 ., This kink reflects the lateral current coming from the axon: indeed it can be recorded under somatic voltage clamp 23 ., In the present model , Na channels open almost instantaneously after the bifurcation ., This produces a discontinuous change in the lateral current , between the initiation site and soma , equal to ΔV/Ra , where ΔV\u200a=\u200aVa−Vs is the voltage difference between the somatic voltage and the voltage at the initiation site , i . e . , the discontinuous voltage change seen in Fig . 1F ., This quantity can be analytically calculated in the simplified model ( see Text S1 , section 3 ) , and is about ΔV≈33 mV for the case shown in Fig . 1F ( consistent with the numerical simulation ) ., The lateral current then jumps to the value ΔV/Ra ., For the present model , with an initiation site at 40 µm , this gives an initial “kink” in the voltage derivative at the soma of dV/dt\u200a=\u200aΔV/ ( CRa ) =\u200a7 . 5 mV/ms ., Fig . 3A shows the response of the ball-and-stick model to a somatically injected current pulse , both at the initiation site ( black ) and soma ( red ) ., At some point , all Na channels open abruptly ( dashed ) and as a result , there is indeed a sudden increase in the voltage derivative ., The phase plot , representing dV/dt as a function of V , shows that this increase is about 5 . 2 mV/ms , near the predicted value ( Fig . 3B ) ., Two remarks are in order ., First , from the formula , it can be seen that this kink becomes more pronounced when Na conductance increases , which is expected , but it becomes less pronounced when the initiation site is moved away from the soma ( Ra increases ) ., This latter fact is more surprising , because it means that the sharpness of the “kink” at the soma is inversely correlated with the sharpness of spike initiation ., Second , even though this kink is significant , it remains an order of magnitude smaller than what is typically observed in cortical neurons ., Thus it appears in this case that spike initiation can be sharp ( abrupt all-or-none opening of Na channels when somatic voltage exceeds a threshold value ) , without producing a very strong “kink” at the soma ., This is because the initial shape of spikes at the soma is not only determined by the sharpness of spike initiation , but also by properties of the piece of axon between initiation site and soma ., Increasing the Na conductance would lower the threshold ( by about 4 mV for every doubling ) , and in any case it cannot push the lateral current above , which is the current obtained with a fully developed spike at the initiation site ., Thus , to obtain a larger “kink” , there must be a fully developed spike closer to the soma ., As we have seen ( Fig . 1E ) , the voltage across the axon increases linearly between the soma and the initiation site when all Na channels are clustered at that point ., Therefore no spike can develop closer to the soma unless additional Na channels are also present closer to the soma ., Therefore , to explore the conditions for a significant “kink” at the soma , we now consider Na channels between the soma and initiation site , again clustered at a single location ( distributed channels are considered later ) ., Immunostaining in the AIS of cortical neurons shows that low-threshold Nav1 . 6 channels accumulate at the distal end of the AIS , while high-threshold Nav1 . 2 channels accumulate at the proximal end 9 ., The half-activation voltage of Nav1 . 2 can be higher than that of Nav1 . 6 by up to about 15 mV ., This implies that the maximum conductance for Nav1 . 2 can be set an order of magnitude larger than for Nav1 . 6 without affecting spike initiation or resting potential ., When a spike is initiated at the distal end of the AIS , the voltage suddenly rises at the location of Nav1 . 2 channels ., If this shift is sufficient , these channels suddenly open and produce an additional current to the soma ., Thus , for this phenomenon to occur , Nav1 . 2 channels must be placed at an intermediate position between the soma and the initiation site: if they are too close to the soma , the voltage does not increase sufficiently at spike initiation to open the channels; if they are too far from the soma , the lateral current is small ., In Fig . 3C , Nav1 . 2 channels are placed at 15 µm and Nav1 . 6 channels at 40 µm ., Spike initiation is still sharp ( Na channels open abruptly ) and the spike threshold is similar , but the kink at the soma is much more pronounced ., Fig . 3D shows that Nav1 . 6 channels at the initiation site open abruptly when the soma is depolarized ( black ) , shortly followed by Nav1 . 2 channels ( green ) ., This panel highlights two points:, 1 ) both types of channels open much more abruptly than the activation curves alone would suggest ( dashed ) ,, 2 ) Nav1 . 2 channels open at about the same somatic voltage as Nav1 . 6 channels , even though there is a 15 mV shift in the activation curves ., This latter observation derives from the fact that when Nav1 . 6 channels open , the voltage at the site where Nav1 . 2 channels are placed suddenly increases above their bifurcation point ., As a result , the voltage derivative at the soma now reaches 42 mV/ms ( Fig . 3E ) , about 8 times higher than without the new channels ., Note that there are no Na channels at the soma in this model , which would produce the biphasic trajectory typical of cortical cells ( dashed red curve ) ., Additional Na channels at the soma have no impact on initial spike shape at the soma ( first “bump” in the plot ) , because the “kink” reflects the current coming from the axon ., To quantify the initial sharpness of spikes , previous studies have used a measure named “onset rapidness” , defined as the slope of the trajectory in the phase plot when a fixed value α of dV/dt is reached , typically of the order of α\u200a=\u200a10 mV/ms 4 , 8 ., Perhaps surprisingly , despite the fact that Na channels open abruptly at the AIS , this sharpness does not appear in the phase plot , where onset rapidness is low , about 2 ms−1 ( Fig . 3F , black ) ., This observation may be confusing: on one hand , spike initiation is sharp , but on the other hand the initial shape of spikes is not sharp at the initiation site ., Some explanation is necessary ., That spike initiation is sharp means that Na channels in the initiation site open abruptly as a function of the somatic membrane potential Vs , and as a function of time ., But as a function of the axonal membrane potential Va at the initiation site , the opening of Na channels follows the Na activation function , which does not depend on spatial properties ., Therefore the derivative dVa/dt essentially reflects the Na activation function , and “onset rapidness” at the initiation site is essentially a measure of this function ., This point can be demonstrated analytically: at the initiation site , as in an isopotential neuron , onset rapidness equals α/ka , independently of all other properties ( Text S1 , section 4 . 1 ) ., In the present model , this theoretical prediction is 10 mV . ms−1/6 mV≈1 . 7 . ms−1 , close to the numerical value ., In contrast , onset rapidness is about four times larger at the soma ( 7 . 7 ms−1 ) ., Indeed the voltage trajectory at the soma is determined by the lateral current , and in particular should correlate with the total Na conductance at the initiation site ( Text S1 , section 4 . 2 ) ., This difference between soma and AIS is consistent with patch recordings in the soma and axon of the same cells 5 , 8 ., As this is a rather subtle point , I will try to rephrase this result , in the context of previous results ., At the initiation site , the voltage derivative dVa/dt reflects the Na current , and therefore is a smooth function of Va , as determined by the Na activation curve ( Fig . 3E–F ) ., However , as a function of membrane potential Vs at the soma , Na channels open abruptly ( Fig . 3D ) ., This is because axonal voltage Va is a discontinuous function of somatic voltage Vs , due to loss of voltage control ( Fig . 1F ) ., The “kink” at the soma is a direct consequence of this discontinuity: since it reflects the lateral current , it is proportional to the spatial derivative of voltage along the axon ., In summary , sharpness of spike initiation is due to compartmentalization ( loss of voltage control ) ; the “kink” at the soma is due to compartmentalization together with transmission by proximal Na channels ., Spike initiation can be sharp with only a small kink seen at the soma , if there are no Na channels close to the soma to transmit the spike ( Fig . 3B ) ., All the previous results were obtained with channels clustered at a single location , but Na channels are rather distributed along the AIS 9 ., Analytical formulae are more difficult to obtain in this case , but the same phenomenon occurs ., First , when Na channels are continuously distributed on a portion of the AIS , maximum depolarization always occurs at the distal end ( Fig . 4A ) ., This is because almost all current flows towards the soma , and therefore the potential must be an increasing function of the distance to the soma ., In addition , the spatial voltage profile along the axon is concave , because at any given point , the current flowing towards the soma is always greater than the current coming from the distal end since it also includes the Na current flowing through the membrane at that point ., In cable theory , these remarks correspond to the fact that the diffusion current equals the opposite of the Na current at any point ( Text S1 , section 5 ) ., Importantly , these facts hold independently of the particular profile of the Na channel density ., For example , if Na channels have linearly decreasing density between 20 µm and 40 µm , spikes are still initiated at the distal end , 40 µm away from the soma ( Fig . 4B ) , which is consistent with recent findings 24 ., However , for spike initiation , the situation is not equivalent to the case when all channels are clustered 40 µm away from the soma ., Indeed , because the voltage profile is concave , it can be seen that it is in fact close to the one obtained with channels clustered at an intermediate location between the two ends ., To show this effect quantitatively , spike threshold and sharpness were calculated with Na channels evenly distributed on a portion of the AIS , with various start and end points , and otherwise the same parameter values as previously ( Fig . 4C , D ) ., The total Na maximal conductance was left unchanged , the start point x1 varied between 1 µm and 35 µm and the end point x2 varied between 40 and 60 µm ., Empirically , it appeared that both spike threshold ( Fig . 4C ) and sharpness ( Fig . 4D ) corresponded to the values obtained when Na channels are clustered at a single “effective” location x\u200a=\u200a0 . 6 x1+0 . 4 x2 ., This formula was empirically determined and may depend on other parameters ., In the ball-and-stick model , the axon is geometrically modelled as a cylinder ., However , at the hillock near the soma , the diameter is larger than in the initial segment 25 ., Fig . 4E–F show the effect of inserting a 10 µm tapering piece of axon at the beginning , with diameter linearly decreasing from 4 µm to 1 µm ., Since in this model there are no Na channels on this part of the axon , the main effect is to increase the axial resistance Ra , by an amount that can be analytically calculated ( see Text S1 , section 1 ) ., In this case , it is equivalent to extending the cylindrical axon by 2 . 5 µm , so the phenomenon is essentially unchanged ., However , the voltage increases much more slowly along the axon in this tapering part , which could have consequences if voltage-gated channels are placed in this region ., It has been proposed that a potential benefit of spike initiation in the distal AIS is to make it more energetically efficient , because the AIS has a smaller capacitance than the soma and therefore requires less transfer of charge to produce a spike , consistently with the fact the current threshold is lower in the axon than in the soma 19 ., Indeed energy consumption is essentially proportional to the number of Na ions entering the cell 26 ., However , the argument does not apply if a full spike is also seen at the soma , as in cortical cells ., In this case , the total transfer of charge carried by Na ions should be about C ., ΔV , where C is the total capacitance of the cell where a full spike develops , and ΔV≈100 mV is the spike height ( assuming no overlap with outward currents ) ., It is not obvious why the location of the initiation site should make a difference ., In fact , spike initiation in the distal AIS is indeed more energetically efficient , not because of the smaller axonal capacitance , but because it reduces the flow of Na current below threshold , which is proportional to the rate of ATP consumption 26 ., First , the maximal Na conductance required to initiate spikes at a given threshold is inversely proportional to the distance of the initiation site from the soma ., Therefore , the Na current at rest is also inversely proportional to that distance ., Second , because Na channels open abruptly when initiation is distal , most channels are closed before initiation ., To demonstrate these points , the ball-and-stick model is simulated in current-clamp with fluctuating current in two configurations: Na channels at the soma , and at 40 µm away from the soma ( Fig . 5 ) ., Since only Na channels are included in the model , the membrane potential across the neuron is reset to the resting value when half of the Na channels are open ( Fig . 5A ) ., The input current is the same in all simulations , so that subthreshold activity is comparable ., Fig . 5B shows the neurons firing rate as a function of maximal Na conductance: the relationship is approximately linear , but the firing rate increases about twice faster with conductance when Na channels are in the distal AIS , compared to the soma ., For the same firing rate , the average Na current is much higher when Na channels are in the soma ( Fig . 5C ) ., This difference is indeed not only due to the difference in maximal Na conductance ( i . e . , number of channels ) , because this average current is still about twice higher with channels at the soma after it is normalized by the maximal conductance ( Fig . 5D ) : this reflects the fact that when Na channels are at the soma , a substantial proportion of them can open without triggering a spike ., Thus the sharpness of spike initiation , and not just the smaller capacitance of the AIS , makes spiking more energetically efficient ., The claim that spike initiation is much sharper in cortical neurons than expected from isopotential Hodgkin-Huxley models is supported by different lines of evidence:, 1 ) the initial shape of spikes recorded at the soma is very sharp , with a distinct “kink” 4 ,, 2 ) cortical neurons can transmit high frequency signals and follow input changes at the millisecond timescale 11 , 12 ,, 3 ) current-voltage relationships measured at the soma in vitro

Introduction, Results, Discussion, Methods

In cortical neurons , spikes are initiated in the axon initial segment ., Seen at the soma , they appear surprisingly sharp ., A standard explanation is that the current coming from the axon becomes sharp as the spike is actively backpropagated to the soma ., However , sharp initiation of spikes is also seen in the input–output properties of neurons , and not only in the somatic shape of spikes; for example , cortical neurons can transmit high frequency signals ., An alternative hypothesis is that Na channels cooperate , but it is not currently supported by direct experimental evidence ., I propose a simple explanation based on the compartmentalization of spike initiation ., When Na channels are placed in the axon , the soma acts as a current sink for the Na current ., I show that there is a critical distance to the soma above which an instability occurs , so that Na channels open abruptly rather than gradually as a function of somatic voltage .

Spike initiation determines how the combined inputs to a neuron are converted to an output ., Since the pioneering work of Hodgkin and Huxley , it is known that spikes are generated by the opening of sodium channels with depolarization ., According to this standard theory , these channels should open gradually when the membrane potential increases , but spikes measured at the soma appear to suddenly rise from rest ., This apparent contradiction has triggered a controversy about the origin of spike “sharpness . ”, This study shows with biophysical modelling that if sodium channels are placed in the axon rather than in the soma , they open all at once when the somatic membrane potential exceeds a critical value ., This work explains the sharpness of spike initiation and provides another demonstration that morphology plays a critical role in neural function .

journal.pgen.1006257

miR-71 and miR-263 Jointly Regulate Target Genes Chitin synthase and Chitinase to Control Locust Molting

Molting is a crucial process in insect growth and development 1 , 2 ., Chitin , as a vital component of the cuticle of the epidermis , plays key roles in maintaining morphology and the molting process 3 ., Because chitin is absent in plants and vertebrates , and insect growth and development are strictly dependent on chitin biosynthesis and degradation , chitin metabolism represents an attractive target for developing safe and effective insecticides 4 ., The migratory locust Locusta migratoria , a worldwide insect pest species , undergoes five molting stages in its life cycle 5 , 6 ., The chitin-mediated molting process is considered to depend on two crucial genes , chitin synthase ( CHS ) and chitinase ( CHT ) , which are regulated by molting hormone 20-hydroxyecdysone ( 20E ) and juvenile hormone 7 , 8 , 9 , 10 , 11 ., Chitin synthases are the key regulatory enzymes for chitin synthesis in insects and represent a specific target of insecticides 12 ., The LmCHS1 gene cloned from the migratory locust is expressed specifically in the epidermis during the molting stage ., Knockdown of the LmCHS1 gene increases the number of non-molting and abnormal molting nymphs 6 ., However , another paralog LmCHS2 contributes to the biosynthesis of chitin associated with the peritrophic matrix 13 ., Moreover , chitinases are hydrolytic enzymes that are required for the degradation of glycosidic bonds of chitin 14 ., TcCHT10 prevents larval molting and plays a vital role during the molting process at all developmental stages; the other paralogs , CHT5 and CHT7 , prevent molting and wings from folding properly only in adults 15 , 16 ., An interesting feature of CHS1 and CHT10 in locusts is that the abrupt increase and decrease in transcript levels at the end of every nymph stage ( before molting ) suggest that the two key enzymes are likely precisely modulated in the molting process ., However , the underlying regulatory molecular mechanisms of enzyme-dependent chitin metabolism and the molting process have remained elusive ., MicroRNAs ( miRNAs ) , small non-coding regulatory RNAs , have emerged as key posttranscriptional regulators of gene expression in multiple biological processes 17 because they can directly trigger translational repression or mRNA degradation by low complementary base-pairing with the 3’UTRs of the target genes 18 , 19 ., However , recent studies have shown that miRNAs can extensively target the protein-coding region of mRNAs in animals or insects 20 , 21 , 22 ., Many studies have shown that miRNAs critically affect the molting of insects , thus resulting in molting defect phenotypes ., For example , miR-8-5p and miR-2a-3p negatively regulate membrane-bound trehalase and phosphoacetylglucosamine mutase of the chitin biosynthesis pathway , leading to a significant reduction in survival rate along with a molting defect phenotype in the hemipteran insect Nilaparvata lugens 23 ., Several distinct miRNAs have been approved in the regulation of insect metamorphosis ., The loss of miR-2 up-regulates Kr-h1 mRNA , thereby leading to impaired metamorphosis 24 , 25 ., Additionally , let-7 and miR-125 mutants induce temporal mis-regulation of specific metamorphic processes in Drosophila 26 ., In the migratory locust , we reported that depletion of Dicer-1 , the enzyme that catalyzes the final step of miRNA biosynthesis , induced a molting defect 27 ., Results indicated that miRNAs play a crucial role in regulating the molting process of locusts ., However , the mechanism regarding how miRNAs affect posttranscriptional modifications in the molting process has not yet been fully elucidated ., Considering that CHS1 and CHT10 are crucial molt-dependent enzymes that balance chitin metabolism in many insect species 15 , 16 , 28 , 29 , we chose CHS1 and CHT10 as candidate genes ., We hypothesized that miRNAs might play essential roles in the regulation of CHS1- and CHT10-mediated molting processes ., In this study , we performed small RNA transcriptome sequencing to identify expressed miRNAs in the integument of locusts ., We found that the integument-expressed miR-71 and miR-263 directly target the two key genes CHS1 and CHT10 and regulate chitin production during the molting process , resulting in the successful molting of the migratory locust ., Our results reveal a molecular mechanism by which miRNAs play a role in balancing the modulation of CHS1- and CHT10-dependent chitin metabolism during molting ., To identify the miRNAs associated with molting , we sequenced a transcriptome of small RNAs of the locust integument , which is an important tissue during the molting process in insects ., A total of 15 , 459 , 187 sequencing reads were obtained , of which 4 , 590 , 268 ( 29 . 7% ) corresponding to mature and star strands were mapped to the known miRNA precursors of locusts 30 ., Forty-five conserved miRNAs showed transcriptional activities ( reads per million threshold 1 ) in the integument of locusts ., Their expression levels varied over several orders of magnitude ., The top ten most highly expressed miRNAs were miR-9b , miR-184 , miR-14 , miR-100 , bantam , miR-71 , miR-275 , miR-305 , miR-263 and miR-279b ( Fig 1A ) ., All of the expressed miRNAs were used for further miRNA candidate screening ., CHS1 and CHT10 involved in chitin metabolism have been confirmed to regulate the insect molting process 15 , 16 , 28 , 29 ., Using the miRanda software , we predicted the expressed miRNAs that could potentially bind to LmCHS1 and LmCHT10 ., Thirteen miRNAs exhibited potential target sites in the 3’UTR and CDS regions of LmCHS1 , and 6 miRNAs possessed target sites located in the 3’UTR of LmCHT10 in locusts ( Fig 1B , S1 Table , and S2 Table ) ., An additional prediction software , RNAhybrid , was used to further improve the target prediction efficiency ., The RNAhybrid program also identified LmCHS1 and LmCHT10 as potential targets for miR-71 and miR-263 , respectively ( S1 Fig ) ., Furthermore , we confirmed the absence of miR-71 and miR-263 binding sites in the Tweedle , Cryptocephal , Obstructor , Knickkopf , and ecd1 genes to exclude the other possible miR-71/miR-263 targets 31 , 32 , 33 , 34 , 35 , which can lead to molting defects similar to those caused by LmCHS1 and LmCHT10 ., To confirm the correlation of the expression pattern between LmCHS1 , LmCHT10 and their target miRNAs , we performed stem-loop quantitative reverse transcriptase-polymerase chain reaction ( qRT-PCR ) to quantify the expression levels of these predicted miRNAs and target genes in the integument of second-instar nymphs ( S2 Fig ) ., The overall expression of miRNAs ( miR-71 or miR-263 ) and that of the target genes ( LmCHS1 or LmCHT10 ) exhibited opposite patterns during the second nymph stage ( Fig 1C and 1D ) ., The miR-71 expression levels showed the opposite wave-like pattern of miR-263 expression levels , with the highest level occurring at the mid-stage for miR-71 , whereas miR-263 expression decreased to the lowest level ( Fig 1D ) ., In contrast , the mRNA expression of LmCHS1 was down-regulated at the mid-stage and up-regulated at the early and late stages ., However , the mRNA expression of LmCHT10 was suppressed at the early and late stages and was promoted at the mid-stage ( Fig 1C ) ., These data indicate that miR-71/miR263 expression is negatively correlated with LmCHS1 and LmCHT10 expression during new integument formation in the nymph stages ., The results imply that there is a possible regulatory relationship between the miRNAs and the genes ., To confirm the interactions of miR-71 , miR-263 and their targeting genes in vitro , we performed reporter assays using luciferase constructs fused to the coding region of LmCHS1 and the 3’UTR of LmCHT10 ., Compared with the agomir control ( agomir-NC ) , the constructs with either the LmCHS1 or LmCHT10 binding sites produced lower luciferase activity when co-transfected with miR-71 or miR-263 agomir , respectively , in S2 cells ( Fig 2A and 2B ) ., When the regions homologous to the “seed” sequence of miR-71 and miR-263 were mutated in the LmCHS1 and LmCHT10 reporter constructs , the luciferase activity returned to levels similar to those produced by mock transfection with the empty reporter plasmid ( Fig 2A and 2B ) ., However , the luciferase activity of sites transfected with miR-252 , whose expression is negatively correlated with LmCHS1 , showed no change compared with the control ( S3 Fig ) ., To further validate the effect of endogenous miR-71 and miR-263 in S2 cells on the luciferase activity , we investigated miRNA-71 and miR-263 levels in S2 cells ., The mir-71 homolog was not detected in the fly genome 36 ., The small RNA transcriptome data indicated that only a few reads for miR-263 ( 7 counts in GEO accession GSM272651 and 1 count in GEO accession GSM272652 ) were detected in S2 cells , implying a limited expression of miR-263 in S2 cells ., We examined luciferase activity in S2 cells with antagomir-263 ., The luciferase signals of the CHT10 construct incubated with antagomir-263 did not vary significantly compared with those of the control ( S4 Fig ) ., The data suggested that the endogenously expressed miR-263 did not affect the luciferase assay results for the locust miR-263 ., Thus , the predicted miRNA binding sites in LmCHS1 and LmCHT10 are functional and might be targeted by miR-71 and miR-263 , respectively , in S2 cells ., Ago1 , as a RNA binding protein , is a core component of RISC involved in miRNA-mediated gene silencing ., Anti-Ago1 RIP is a biochemical approach to identify the composition and organization of endogenous mRNAs , miRNAs associated with Ago1 proteins ., This approach is widely used in interaction validation between miRNA and its target in vivo ., We then performed an RNA immunoprecipitation assay in the integument to examine the interactions of miR-71 and miR-263 with their targeting genes in vivo ( Fig 2C and 2D ) ., LmCHS1 or LmCHT10 were significantly enriched in the Ago1-immunoprecipitated RNAs from the integuments treated with agomir-71 or agomir-263 compared with those treated with agomir-NC ., These results indicated that miR-71 and miR-263 directly regulate LmCHS1 and LmCHT10 in the locust integument , respectively ., To determine whether miR-71/miR-263 were co-localized in the locust integument , we performed in situ analyses of miRNA-71/miR-263 and their targets by miRNA/mRNA fluorescence in situ hybridization ( FISH ) ., Indeed , we found that miR-71 and LmCHS1 as well as miR-263 and LmCHT10 were both widely detected in the epidermal cells of the locust integument ( Fig 2E ) ., Specifically , miR-71 is co-localized with LmCHS1 and miR-263 is co-localized with LmCHT10 in cells of the integument ., The results suggest that in the locust integument , LmCHS1 and LmCHT10 interact directly with miR-71 and miR-263 , respectively , in a spatial manner ., To determine the effects of miR-71 and miR263 on their target genes in vivo , we detected the expression levels of LmCHS1 and LmCHT10 after miRNA agomir ( overexpression ) or antagomir ( knockdown ) administration in the locust integument ., We first assessed the miRNA expression changes by injecting locusts with miRNA agomir or antagomir in vivo to confirm the delivery efficiency of miRNA administration ., The qPCR results showed that miR-71 and miR-263 levels were significantly induced and depleted by their agomir and antagomir treatments , respectively ., As expected , the treatment had no effect on the expression of the negative control , let-7 ( S5 Fig ) ., Moreover , the mRNA levels of LmCHS1 decreased by approximately 60% compared to those in the control locusts after agomir-71 injection ( Fig 3A ) ., Additionally , antagomir-71 injection resulted in a significant up-regulation of the mRNA expression level of LmCHS1 ( Fig 3B ) ., In contrast , inhibition of approximately 55% of LmCHT10 expression was observed after agomir-263 injection ., Additionally , the mRNA levels of LmCHT10 were up-regulated by miR-263 knockdown ( Fig 3A and 3B ) ., No significant effects on the expression of paralog genes LmCHS2 and LmCHT5 upon miR-71/miR-263 administration were observed , suggesting that the agomir/antagomir injection specifically acted on the target genes ( S6 Fig ) ., Additionally , we sought to determine whether the administration of miR-71 had any effects on the expression of miR-263 , or vice versa ., The results indicated that there was no interaction between miR-71 and miR-263 , which were involved in the regulation of two distinct processes ( chitin synthesis and degradation ) ( S7 Fig ) ., Since 20E is believed to primarily regulate insect growth and development processes , including molting 2 , 37 , we further used 20E treatments to investigate whether 20E might play a relevant role in the regulation of the expression of miR-71 and miR-263 ., The results indicated that 20E treatment depressed the expression levels of miR-263 , but did not have significant effects on miR-71 expression level ( S8 Fig ) ., Because LmCHS1 and LmCHT10 are essential genes in chitin synthesis and degradation , we determined the effects of miR-71 and miR-263 on chitin production in the integuments after miRNA administration in vivo ., The administration of the miR-71 agomir reduced chitin production by approximately 48% ( Fig 3C ) ., In contrast , miR-71 knockdown significantly increased the chitin content ( by approximately 18% , p < 0 . 05 ) of the locusts ., Accordingly , a significant increase in chitin content was observed after agomir-263 manipulation , and antagomir-263 injection decreased chitin content ( Fig 3D ) ., Thus , administration of miR-71 and miR-263 may have profound regulatory effects on chitin production and content in vivo in locusts ., To determine the function of miR-71 and miR-263 during the molting process , we monitored the molting of locusts after agomir-71 or agomir-263 injection , respectively ., The locusts injected with agomir-71 or agomir-263 displayed a distinct molting defect phenotype ., In total , of 25 nymphs injected with agomir-71 , 12 ( 48% ) died during the molting process from second instar to third instar , whereas only 8 . 7% ( 2 out of 23 ) of the control nymphs died during this process ( Fig 4A ) ., Similarly , after injection of agomir-263 , the mortality reached 40 . 7% , which was significantly higher than that of the control ( only 7 . 4% ) during the molting process ( Fig 4C ) ., In parallel , miR-71 and miR263 knockdown caused by injection of antagomir-71 or antagomir-263 resulted in incomplete ecdysis , with 24 . 0% and 20 . 7% mortality , respectively ( Fig 4B and 4D ) ., After nymphs were injected with the miR-71 and miR-263 agomir or antagomir , the nymphs exhibited abnormal and unsuccessful molting ( Fig 4E–4H ) , in which a certain amount of the old cuticle was separated from the body but not detached from the body to any extent ., Moreover , some nymphs showed a molting defect , in which legs failed to slough from the old cuticle , and other nymphs died without obvious molting defects due to molting arrest ( Fig 4E–4H ) ., Treatment of locusts with agomir-71 and agomir-263 resulted in the down-regulation of LmCHS1 and LmCHT10 expression and a corresponding change in chitin content , thereby generating a significant molting defect ( Fig 4I ) ., To further determine whether the abnormal layer of the cuticle was responsible for the molting defect induced by miR-71 and miR-263 , we performed hematoxylin and eosin staining and chitin staining in the integument by injecting agomir-71 or agomir-263 into the locusts ( Fig 5A and 5C ) ., A significant decrease in chitin content occurred in response to miR-71 overexpression in the newly formed cuticle , which exhibited a severe deficiency due to diminished chitin synthesis ( Fig 5A ) ., Similarly , RNAi for LmCHS1 prevented the synthesis of cuticle chitin , as expected in the newly formed cuticle ( Fig 5B and 5D ) ., Conversely , miR-263 overexpression inhibited the degradation of the old cuticle , the chitin of which was not diminished compared with that of the agomir controls ( ig 5A and 5C ) ., Consistent with the miR-263-mediated phenotype , knockdown of LmCHT10 transcripts hindered chitin degradation of the old cuticle , leading to a dramatically thickened layer of the old cuticle and impeding the shedding of the old cuticle during molting ( Fig 5B and 5D ) ., Therefore , the change in chitin content of new/old cuticle regulated by miR-71 and miR-263 is a key mediator of defective molting ., Our previous studies confirmed that depletion of Dicer-1 prevented the molting process 38 and chitin-metabolic genes , including LmCHS and LmCHT , are related to molting in the migratory locust 7 ., The results presented herein explored the link between the Dicer-mediated phenotype and LmCHS/LmCHT-associated molting ., In this study , we found that miR-71 and miR-263 control chitin synthesis and degradation by targeting LmCHS1 and LmCHT10 , resulting in post-transcriptional regulation of the molting process in locusts ( Fig 4I ) ., This miRNA-mediated mechanism of chitin metabolism provides insight into the molecular basis of the molting process in locusts ., We found that miR-71 targets LmCHS1 and miR-263 targets LmCHT10 in the chitin metabolic pathway of locusts ., Chitin synthases and chitinases are responsible for the synthesis or degradation of chitins , which represent two inverse processes 39 ., Thus , we suspected that the two processes of chitin synthesis and degradation affect one another ., Therefore , we tested whether these two miRNAs interacted with each other using agomir-71/263 treatment ., However , this was not the case ( S7 Fig ) ., The expression levels of the two miRNAs were very similar to those of the respective controls ., In addition , 20-hydroxyecdysone ( 20E ) , as a key steroid hormone , coordinates multiple developmental events involving insect molting and metamorphosis 40 ., Although 20E treatment is considered to be correlated with CHS and CHT expression , its roles in the regulation of CHS and CHT remain a matter of controversy ., DmeCHS-1 and DmeCHS-2 transcription is activated by 20E during Drosophila metamorphosis 41 ., MsCHS-1 gene is negatively controlled by 20E , reflecting a dual effect of 20E 42 ., LmCHT5 and TmCHT5 gene expression can be induced by 20E during the molting process 43 , 44 ., However , the pathway linking 20E to CHS or CHT is still largely unknown ., miR-8-5p and miR-2a-3p act as molecular regulators that tune the chitin biosynthesis pathway in response to 20E 45 ., Thereby , we wondered whether 20E might play a role in the regulation of the expression of miR-71 and miR-263 , leading to the precise expression of LmCHS1 and LmCHT10 ., The 20E treatment inhibited miR-263 expression and induced LmCHT10 expression but did not affect the expression levels of miR-71 or LmCHS1 ( S1 Fig ) ., Thus , the 20E-miR-263-CHT10 axis may switch the degradation of chitin on and off , whereas miR-71-CHS1 axis-mediated chitin synthesis is regulated by mechanisms other than 20E ., The regulatory function of miR-263 is conserved across a broad range of insect species ., We performed miRNA target prediction in other insect species for which CHT10/CHS1 UTR sequences were available in the NCBI GenBank ., The prediction results revealed that miRNA-263 binding sites of CHT10 are present in several holometabolous insects including flies , beetles and mosquitoes , whereas no binding sites for miR-71 were identified in other insect species ( S3 Table ) ., This finding suggests that these regulatory interactions have been evolutionarily conserved , indicating that there is selective pressure to maintain the regulatory interactions of miR-263 and CHT10 across species ., Previous studies have shown that the expression patterns of CHT10 during developmental instar stages are quite similar among insects 15 , 46 , suggesting a common regulatory mechanism of the miRNA-263-dependent molting process ., Our study provided experimental evidence that this regulatory mechanism is also present in hemimetabolous locusts ., Members of Orthoptera occupy a more basal position in the insect lineage relative to holometabolous insects 47 , 48 ., Thus , we presume that the regulatory roles of miR-263 in the molting process represent an ancestral function in insects that perhaps originated with the emergence of the common ancestor of hemimetabolous and holometabolous insects ., miRNAs are precise regulators that are able to sharpen developmental transcription by increasing and reducing target expression to meet developmental demands 49 ., Due to the reduced expression of CHS1 and CHT10 , the agomir treatments of miRNA-71 and miR-263 can cause severe deficiency of new cuticle synthesis and failed degradation of the old cuticle , respectively ., Unexpectedly , the antagomir treatments of miR-71/miR-263 and the resulting up-regulation of their target genes resulted in a similar aberrant phenotype , indicating that the elevated ectopic expression of CHS1/CHT10 is also detrimental to the molting process ., These results indicate that extremely high or low expression of CHS1 and CHT10 during a critical period of the molting process can result in the development of an aberrant molting phenotype ., miRNAs could tune the transcriptional activities of target genes to physiologically relevant levels 50 ., miR-71/miR-263 could directly interact with CHS1/CHT10 and play a role in ensuring an accurate level of their expression ., The precise interactions of miR-71/miR-263 and CHS1/CHT10 regulate the molting process in a spatio-temporal manner ., Taken together , our results show that the transcriptional activities of CHS1 and CHT10 are tuned to a precise level at which they can execute proper function , emphasizing the important roles of miRNA-mediated precise regulation in the molting process ., The conclusion that emerges is that two miRNAs control the molting process by precisely regulating chitin metabolism ., miR-71 and miR-263 suppress CHS1 and CHT10 transcript levels , thus preventing the progression of the molt to the next stage ., This miRNA-mediated post-transcriptional regulation of chitin metabolism is particularly significant for understanding the molting process of locusts and potentially provides new targets for controlling locust plagues worldwide ., Locusts were obtained from the same locust colonies , which were maintained at the Institute of Zoology , Chinese Academy of Sciences , China ., Nymphs were reared under a 14:10 light/dark photo regime at 30 ± 2°C and were fed fresh wheat seedlings and bran ., Total RNA was extracted using TRIzol ( Invitrogen ) and treated with DNase I following the manufacturer’s instructions ., The RNA concentration and purity were assessed in an Agilent 2100 Bioanalyzer ( Agilent ) to verify RNA integrity ., Small RNA libraries were constructed using a TruSeq small RNA sample preparation kit ( Illumina ) ., Briefly , the 3’ and 5’ RNA adapters were ligated to the corresponding ends of small RNAs ., Following adapter ligation , the ligated RNA fragments were reverse transcribed using M-MLV reverse transcriptase ( Invitrogen ) ., The resulting cDNA products were PCR amplified with two primers that were complementary to the ends of the adapter sequences ., The PCR amplicons were separated by size in 6% Novex polyacrylamide gel for miRNA enrichment and sequenced on an Illumina Genome Analyzer IIx sequencing system ., Using the Cutadapt software , we trimmed the low-quality reads and the reads showing sequence similarity to adaptor sequences at the start or end terminals ., The quantifier module in the miRDeep ( version 2 . 0 . 0 . 5 ) software package was used to measure expression levels based on read counts ., The ~300-bp sequences of the CDS and the 3′ UTR surrounding the predicted miR-71 and miR-263 target sites in CHS1 and CHT10 , respectively , were separately cloned into the psiCHECK-2 vector ( Promega ) using the XhoI and NotI sites ., To generate the mutation version , the 8 nt of binding sites were mutated ( GTTTTTCA for CHS1; GTGCTATT for CHT10 ) , which include the region complementary to the miR-71 and miR-263 seed ., S2 cells were co-transfected with 800 ng of the luciferase reporter vector or the empty vector and agomir-71 ( -263 ) at a 1:4 ratio using the Lipofectamine™ 2000 reagent ( Invitrogen ) according to the manufacturer’s instructions ., The activities of the firefly and Renilla luciferases were measured 48 h after transfection with the Dual-Glo Luciferase Assay System ( Promega ) using a luminometer ( Promega ) ., Results are expressed as the ratio Renilla/firefly luciferase activity ( mean ± SEM ) based on six independent replicates ., The miRNA agomir or antagomir , each of which is a stable miRNA mimic or inhibitor , was used to validate the function of the miRNA in vivo ., Briefly , 210 pmol of agomir-71 ( -263 ) or antagomir-71 ( -263 ) ( 500 μM; RiboBio ) was injected into the thoracic hemocoels of second-stadium nymphs two times at 48 h intervals ., The agomir or antagomir negative controls ( 500 μM ) were also injected into the locust thoracic hemocoels ( RiboBio ) ., All injections were administered using a nanoliter injector ( World Precision Instruments ) with a glass micropipette tip ., Treated nymphs were subjected to phenotypic observation of molting process ., Their integuments were harvested , snap-frozen , and stored at -80°C ., Total RNA enriched for small RNAs was isolated from integuments using the mirVana miRNA isolation kit ( Ambion ) ., Moloney murine leukemia virus ( M-MLV ) reverse transcriptase ( Promega ) and a miRNA first-strand cDNA synthesis kit ( Ambion ) were used to prepare the Oligo ( dT ) -primed cDNA and stem-loop cDNA , respectively ., The miRNAs and mRNAs were subjected to qPCR using the SYBR Green miRNA expression and gene expression assays , respectively , according to the manufacturer’s instructions ( Tiangen ) ; qPCR was performed on a LightCycler® 480 instrument ( Roche ) ., The PCR data were analyzed using the 2−ΔΔCt method of relative quantification ., As endogenous controls , U6 snRNA and ribosomal protein RP49 were used to quantify the miRNA and mRNA expression levels , respectively ., Dissociation curves were determined for each miRNA and mRNA to confirm unique amplification ., The qPCR primers are listed in S4 Table ., All the qRT-PCR reactions were performed in six biological replicates ., Four integuments were involved in one biological replicate ., A combined two-color fluorescence in situ analysis of miRNA-71 ( -263 ) and its targets was performed on the integuments of second-instar nymphs by co-labeling of the miRNA and its target according to the method described by Nuovo et al . 51 ., An antisense locked nucleic acid ( LNA ) detection probe for miR-71 , miR-263 or a scrambled control ( Exiqon ) was labeled with double digoxigenin ., Biotin-labeled antisense and sense probes of CHS1 and CHT10 were generated from linearized recombinant pGEM-T Easy plasmids using the T7/SP6 RNA transcription system ( Roche , Basel , Switzerland ) following the recommended protocols ., Based on the timepoint at which higher expression activities were observed for miR-71 and miR-263 , we selected the nymphs on the third day of the 2nd instar stage for miR-71 and CHS1 co-localization detection and the nymphs on the fifth day of the 2nd instar stage for miR-263 and CHT10 co-localisation detection , respectively ., The integuments were fixed in 4% paraformaldehyde overnight ., The paraffin-embedded integument tissue slides ( 5 μm thick ) were deparaffinized in xylene , rehydrated with an ethanol gradient , digested with 20 μg/mL proteinase K ( Roche ) at 37°C for 15 min , and incubated with the LNA miRNA probes and its target RNA probe at 60°C for 5 min ., The slides were then hybridized for 7–15 h at 37°C and washed in 0 . 2× SSC and 2% BSA at 4°C for 5 min ., The slides were incubated in anti-digoxigenin–alkaline phosphatase conjugate ( 1:150 dilution ) for 30 min at 37°C , followed by incubation with the HNPP substrate ., For biotin-labeled probes , a TSA kit ( Perkin Elmer , MA , USA ) including a streptavidin horse radish peroxidase-conjugate and fluorescein tyramide substrate was used ., The signals of the miRNA and its target were detected using an LSM 710 confocal fluorescence microscope ( Zeiss ) ., The primes for probe synthesis of CHS1 and CHT10 are listed in S4 Table ., The RIP assay was performed using a Magna RIP Quad kit ( Millipore ) according to the manufacturer’s instructions , with slight modifications ., The 2-day-old second instar nymphs were microinjected with agomir-71 or agomir-263 ., A scrambled miRNA agomir was used as a negative control ., Treated nymphs were subjected to RIP analysis 48 h later ., Eight integuments of abdomen were collected and homogenized in ice-cold RIP lysis buffer ., The homogenates were stored at -80°C overnight ., A total of 5 μg of Ago-1 antibody ( Abmart ) or normal mouse IgG ( Millipore ) , which was used as a negative control , was pre-incubated with magnetic beads ., The frozen homogenates in the RIP lysates were thawed and centrifuged , and the supernatants were incubated with the magnetic bead–antibody complex at 4°C overnight ., The immunoprecipitated RNAs were reverse-transcribed into cDNA using random hexamers ., qPCR was performed to quantify LmCHS1 and LmCHT10 ., The supernatants of the RIP lysates ( input ) and the IgG controls were assayed to normalize the relative expression levels of the target genes ., The chitin content of the locust integuments was quantified after miR-71 or miR-263 administration ., The integument tissues of the locust nymphs were immediately dissected and stored in liquid nitrogen ., Three integuments of locust abdomens were homogenized in liquid nitrogen and transferred to 3% SDS ., The homogenates were incubated at 100°C for 15 min; then , 120% KOH was added ., The pellets were re-suspended and incubated at 130°C for 1 h ., After cooling , 0 . 8 ml ice-cold 75% ethanol was added , and the samples were shaken until the KOH and ethanol formed a single phase ., The homogenates were then centrifuged at 4°C , and the supernatants were discarded ., The pellets were washed with 40% cold ethanol containing insoluble chitosan ., Approximately 50 μl of 10% NaNO2 and 50 μl of 10% KHSO4 were added to each sample , and the samples were centrifuged at 4°C ., The supernatants were combined with 20 μl of 12 . 5% NH4SO3NH2 and 20 μl freshly prepared 0 . 5% ( wt/vol ) 3-methyl-2-benzothiazolone hydrazone hydrochloride hydrate solution , and the reaction was heated to 99 . 9°C for 3 min ., After cooling , 20 μl of 0 . 83% FeCl3 . 6H2O solution was added to the reaction ., Measurements of the reaction mixture were performed using a microplate reader at 650 nm using glucosamine as a standard ., To knock down CHS1 and CHT10 , double-stranded RNA ( dsRNA ) was synthesized using T7 RiboMAXTM Express RNAi System ( Promega , USA ) following the manufacturer’s instructions ., Each insect was injected with 3 μg of dsRNAs at day 3 of the second instar nymphs ., Control nymphs were injected with equivalent volumes of dsGFP alone ., Total 25 nymphs were injected with dsRNA for each gene ., Nymphs were observed carefully after injection ., The nymphs that typically showed abnormal ecdysis were used for subsequent extraction ., Six abnormal nymphs and six control nymphs were used for hematoxylin and eosin staining and chitin staining ., The SPSS 17 . 0 software ( SPSS Inc . ) was used for statistical analysis ., The differences between treatments were compared using either Student’s t-test or one-way analysis of variance ( ANOVA ) followed by Tukey’s test for multiple comparisons ., The Mann–Whitney U test was used to analyze the behavioral data due to its non-normal distribution characteristics ., p < 0 . 05 was considered statistically significant ., All results are expressed as means ± SEM .

Introduction, Results, Discussion, Materials and Methods

Chitin synthase and chitinase play crucial roles in chitin biosynthesis and degradation during insect molting ., Silencing of Dicer-1 results in reduced levels of mature miRNAs and severely blocks molting in the migratory locust ., However , the regulatory mechanism of miRNAs in the molting process of locusts has remained elusive ., In this study , we found that in chitin metabolism , two crucial enzymes , chitin synthase ( CHS ) and chitinase ( CHT ) were regulated by miR-71 and miR-263 during nymph molting ., The coding sequence of CHS1 and the 3’-untranslated region of CHT10 contain functional binding sites for miR-71 and miR-263 , respectively ., miR-71/miR-263 displayed cellular co-localization with their target genes in epidermal cells and directly interacted with CHS1 and CHT10 in the locust integument , respectively ., Injections of miR-71 and miR-263 agomirs suppressed the expression of CHS1 and CHT10 , which consequently altered chitin production of new and old cuticles and resulted in a molting-defective phenotype in locusts ., Unexpectedly , reduced expression of miR-71 and miR-263 increased CHS1 and CHT10 mRNA expression and led to molting defects similar to those induced by miRNA delivery ., This study reveals a novel function and balancing modulation pattern of two miRNAs in chitin biosynthesis and degradation , and it provides insight into the underlying molecular mechanisms of the molting process in locusts .

Molting is a crucial process in the growth and development in insects ., Disturbing the molting process represents an attractive strategy for developing safe and effective insecticides ., The migratory locust is a hemimetabolous pest that undergoes five molting stages in its life cycle ., Similar molting defects can be observed in expression silencing of the key genes both in miRNA processing and in chitin metabolism ., However , any link between a specific miRNA to chitin metabolism has not yet been established ., In this study , we elucidated a mechanism by which two miRNAs regulate chitin metabolism related to the molting process ., We found that miR-71 and miR-263 directly repress two genes , chitin synthase1 ( CHS1 ) and chitinase10 ( CHT10 ) , which are required for chitin biosynthesis and degradation in chitin metabolism ., Manipulation of miR-71 and miR-263 expression blocked molting and resulted in abnormal molting by negatively regulating the expression of LmCHS1 and LmCHT10 ., Furthermore , both up-regulation and down-regulation of LmCHS1 and LmCHT10 by miRNA manipulation altered the chitin content of the new cuticle and old cuticles , leading to a similar aberrant molting phenotype ., Our results demonstrate a balancing modulation pattern of two miRNAs in chitin biosynthesis and degradation that controlled the precise molting process in locusts .

chitin, invertebrates, medicine and health sciences, luciferase, locusts, gene regulation, enzymes, enzymology, animals, insect pests, physiological processes, developmental biology, micrornas, nymphs, materials science, pests, macromolecules, materials by structure, polymers, polymer chemistry, molting, proteins, gene expression, oxidoreductases, chemistry, insects, agriculture, arthropoda, biochemistry, rna, nucleic acids, physiology, genetics, biology and life sciences, metamorphosis, physical sciences, non-coding rna, organisms

journal.pntd.0002725

Epidemiology of Visceral Leishmaniasis in Georgia

In recent years reports have emerged of increased leishmaniasis transmission in Europe 1 , drug resistant leishmaniasis has spread further 2 , and the spread of HIV/leishmaniasis co-infection is a trend of particular concern 3 , 4 ., Visceral Leishmaniasis ( VL ) is mainly caused by two species of parasites , the anthroponotic L . donovani and the zoonotic L . infantum , for which a variety of canids serve as the animal reservoir ., Most infections are asymptomatic , although longitudinal follow-up has shown that some infected individuals eventually predispose to clinical disease ., Malnutrition and immune suppression , notably HIV infection , predispose to clinical disease , and children are especially affected 5 ., Zoonotic VL occurs in many former Soviet Union countries and presents one of the most serious public health concerns in Georgia 5 , 6 ., Several natural VL foci have been identified in the country where various species of Phlebotomus and canine reservoirs facilitate the transmission ( see Figure, 1 ) 7 ., The first four cases of VL in Georgia were described in 1913 , which to the best of our knowledge was the first report about this disease in the entire Caucasus region ., The first well-described outbreaks ( 540 cases ) of VL were reported in eastern Georgia in 1954 6 , 8 ., All cases were registered in 6 cities and 164 villages , mainly in the east of the country , in the Shida Kartly and Kakheti regions ( see Figure 1 ) ., Malaria control efforts in Eastern Georgia in the sixties included massive spraying campaigns with the insecticide dichlorodiphenyltrichloroethane ( DDT ) 8 , which is believed to have also caused a significant reduction in the sand fly population , as during the next 40 years , until the 1990s , only sporadic VL cases were registered , and only in the extreme eastern part of the country ., Since 2005 , 19 VL fatal cases have been registered in Georgia , usually the result of late diagnosis and/or misdiagnosis ., VL traditionally affects children in the 1–5 years age group ., However in the last 5 years a relatively high number of adults have been among the reported VL cases , indicating that the disease may be re-emerging as an epidemic instead of the previously low endemic situation official disease records , National Centre for Disease Control ( NCDC ) , Tbilisi , Georgia ., The first VL case in Tbilisi was registered in 1990 7 ., Two cases of Leishmania/HIV co-infection were diagnosed in 2008; for both cases the outcome was fatal official disease records , NCDC 2008 ., Cutaneous leishmaniasis ( CL ) is less frequent: 125 CL cases were registered in the period 1928–1964 , of which 110 ( 88 . 0% ) occurred in Tbilisi and villages situated in the western part of the Mtkvari river valley –Shida Kartli region , 56 km west from Tbilisi ( see Figure 1 ) ., After a long interval without registered cases , new cases of CL started to appear , and mandatory registration of CL started in 2001 ., Between 2001 and 2007 , 1–5 cases of CL were reported in most years , which increased to 12 cases in 2008–2009 , 8 of them in Tbilisi official disease records , NCDC ., Both CL and VL are underreported , due to their relatively recent re-emergence , a consequent lack of awareness in the population , and the lack of a leishmaniasis training program for medical doctors ., In Kutaisi the first VL cases were only registered as recently as 2007 ( 3 cases ) official disease records , NCDC 2007 in which year also the first sand flies of the Phlebotomus genus were identified in this area G . Babuadze unpublished study ., During the last two decades , the annual number of clinical VL cases has remained persistently high , and varies between 122 and 189 ., In the period 1995–2010 , 1919 VL cases were registered , including 1052 from Tbilisi official disease records , NCDC ., Several natural VL foci have been identified , where several species of Leishmania vectors exist and canine reservoirs facilitate the transmission of this disease 7 ., The situation is most urgent in the East Georgian city of Tbilisi , which has approximately 1 . 5 million inhabitants ., Most of the natural VL foci are located in the very centre of the city , which are the oldest and most popular areas ., Urban transmission is aided by the elongated shape of the city along the banks of the river Mtkvari ( Kura ) , especially in areas located close to the hills and forests from where wild animals , including jackals and foxes , often appear ., This situation facilitates the synanthropy between wild canids and stray and domestic dogs as these animals are known as reservoirs for Leishmania 8 ., Since 1997 all VL cases have been reported from the left ( Eastern ) bank of the Mtkvari ( see Figure 2 ) ., In order to understand the spread of VL in Tbilisi and Kutaisi , three surveys were performed: ( 1 ) an infection screening of populations in selected districts of the two cities by Leishmanin Skin Test ( LST ) ; ( 2 ) a seroprevalence study of Leishmania infection in wild canines , stray and pets dogs; and ( 3 ) an entomologic identification of potential Leishmania vectors ., We have identified alarmingly high prevalence rates in humans , vectors and dogs , especially in Tbilisi , although the emergence of VL in Western Georgia , where it has never been reported before , is almost equally serious ., Tbilisi is the capital and the largest city of Georgia ( 726 km2 , 1 , 480 , 000 inhabitants ) , situated in the South Caucasus at 41°43′ North Latitude and 44°47′ East Longitude , lying on the banks of the Mtkvari ( Kura ) River ., Highest elevation is 770 m and the lowest is 380 m above the sea level ., Kutaisi is Georgias second largest city , in the western region of Imereti and has approximately 186 , 000 inhabitants ., It is located along both banks of the Rioni River , 221 km west of Tbilisi ., The city lies at an elevation of 125–300 meters ., For this study , Tbilisi was divided in three districts ( see Figure 2 ) ., The climate of the Tbilisi area can be classified as humid subtropical , with relatively cold winters and hot summers , which constitutes a good seasonal habitat for sand flies ., Ethical clearance for conducting this study was secured by the Institutional Board of Review ( IRB ) of the National Center for Disease Control and Public Health ( IRB00002150 ) in compliance with Georgian legislation and international bioethical frameworks ., All volunteers were interviewed and written informed consent was obtained for participation in the study ., Leishmanin was obtained by WHO from the Pasteur Institute , Teheran , Iran , made with phenolized L . major promastigotes 9 ., The LST detects CL 10 as well as asymptomatic infection and cured VL although VL patients with active disease are generally LST negative as a result of a strong humoral response 11 ., As such the LST is generally employed to assess prevalence of infection in a population rather than disease levels 12 ., The sensitivity of the LST for cutaneous leishmaniasis was estimated as 88% in a recent study 13 ., For the LST survey ( during a 3 months period , June–August 2012 ) a cross-sectional study was carried out using a multi-stage cluster selection and probability proportional to size sampling 14 , 15 ., District #1 consists of the central areas located in the central part of city situated on the right ( Western ) side of the Mtkvari River , and is bordered by green parks , hills and forest ., In this district VL cases have been registered every year since 1997; District #2 includes the areas located in the eastern part of city ( left bank of the Mtkvari River ) , where VL cases have consistently been registered since 2000 ( incidence lower than in District #1 ) ; District #3 consists of the remaining parts of Tbilisi where VL cases also have been permanently registered since 2000 ( comparable less than in the first two districts ) official disease records , National Center for Disease Control ( NCDC ) , Tbilisi , Georgia ., A total of 981 LST were performed within the thus-defined districts in Tbilisi ( 816 ) , and in Kutaisi ( 163 ) ., After cleaning the skin over the flexor surface of the forearm with 70% alcohol , 0 . 1 ml of the antigen , containing standard amounts of Leishmania promastigotes , was administered intradermally employing single use insulin syringes ., Skin test results ( indurations ) were read at 48 hours , although a small number was tracked for a 72 hours reading , according to the WHO recommendations 16 ., Indurations were considered to be positive if they were at least 6 mm in diameter ., In June–August 2011/2012 blood-serum specimens from 1571 asymptomatic dogs were sampled in Tbilisi ( 623 pet and 670 stray dogs ) and Kutaisi ( 151 pet and 50 stray dogs ) ., The pet dog sampling was performed in the same areas where the LST screening was performed ., Samples were taken from almost all pet dogs , with a few exceptions due to lack of consent from the owners ., The blood from the stray dogs was collected thanks to the Municipal Service of Emergency and Urgent Situations of both Tbilisi and Kutaisi ., Wild animals were captured alive and released after blood collection during a 2-month period ( September–October 2012 ) , with written permission from the Ministry of Environment of Georgia ., Blood samples from 77 wild canines ( 38 foxes and 39 jackals ) were taken ., Collected canine blood samples were placed into Vacutainer vials for serum and stored at 4°C ., For the detection of VL antibodies in canine serum Kalazar Detect rK39 Rapid tests ( InBios International Inc , Seattle , USA ) were performed according to the manufacturers instructions 17 , 18 ., This test detects the circulating antibodies to recombinant K39 antigen of L . donovani-infantum complex and is highly specific ( 100% ) and sensitive ( 97% ) in diagnosing symptomatic and asymptomatic infections 19 , 20 ., The test procedure involved adding 20 µl of serum to the absorbent pad on the bottom of the test strip ., Test strips were placed into a well of a sterile 96 well plate , to which 2–3 drops of buffer solution ( provided with the test kit ) were added; results were read within 10 minutes ., There is a variety of housing styles within the study area , including apartment blocks of 9 floors or more; however , some 90% are modest private homes constructed of brick or stone ., Most of these are walled compounds , with courtyards and gardens containing a variety of trees including fruit trees ., Within these areas , pens for animals including dogs , chickens and rabbits are frequent which offers a diversity of blood meal sources , resting and breeding sites for sand fly species ., Windows and doors are unscreened , providing easy access for sand flies to residents ., Vector surveys were implemented in Tbilisi ( June–October , 2011 ) and Kutaisi ( June–October , 2012 ) ., The total number of collected sand flies was 873 ( 656 in Tbilisi and 217 in Kutaisi ) ; of these , 516 were female ., Sandflies being phototrophic 21 , 22 Seven CDC miniature light traps ( John W . Hock Company , Gainesville , FL 32606 , USA ) were used to collect sand flies during three consecutive nights per month in each area 23 ., Traps were placed outside houses , one per family compound , within fenced or protected habitats , especially near animal pens or in courtyards close to houses , between 7 pm and 7 am ., Collected female sand flies were morphologically identified , dissected for detection of Leishmania parasites and scored according to various parameters: blood feed/unfed; gravid/non gravid 24 ., Live female sand flies were removed from traps and transferred to 10–20% soapy water solution to clean and immobilize them; afterwards they were rinsed in clean distilled water and soaked for about 10 minutes in 1% sodium hypochlorite solution to disinfect them ., Sand flies were dissected in sterile conditions ( sterile dissecting needles on a sterile microscope slide in a drop of sterile phosphate-buffered saline ( PBS ) ) according to the method of Lawyer et al . 25 ., Two terminal segments of the sand fly abdomen containing the spermathecae and the guts were separated from the whole body ., Midguts were transferred to a fresh drop of sterile PBS on another clean slide for identification ., Each gut was covered with sterile , glass cover slip ., In positive sand flies we observed a dense infection with many parasites attached to the microvillar lining of the midgut wall and to the cuticular intima of the stomodeal valve ., Tightly packed Nectomonads and haptomonads were observed in the thoracic midgut , behind the stomodeal valve , which was forming a “plug” with a high proportion of easily motile metacyclic parasites that escaped into the sterile dissection buffer ., They were distinguished by a small cell body with a long flagellum and fast movement ., These motile cell forms were clearly visible and distinguishable from other elements under the 40× magnification ., Data statistical analysis was performed using SPSS Statistics v20 software ( IBM ) ., We analyzed variants to compare differences between the groups and determined statistical significance , with P values less than 0 . 05 ., The results with the Leishmanin Skin Test ( LST ) , summarised in Table 1 , revealed a high prevalence of LST positives in age groups 5–9 , 15–24 and 25–59 years in Tbilisi District #1 ( 22 . 2% , 37 . 5% and 19 . 5% , respectively ) ., Prevalence for the same age groups was significantly lower in Kutaisi , at 0% ( P\u200a=\u200a0 . 0062 ) , 3 . 2% ( P\u200a=\u200a0 . 0018 ) and 5 . 2% ( P\u200a=\u200a0 . 0017; all Χ2 test ) , respectively , and the overall difference in prevalence , 19 . 3% versus 7 . 3% , was also highly significant ( P\u200a=\u200a0 . 00016 ) ., Other notable results include high prevalence in Tbilisi District #2 for ages 10–14 ( 28 . 6%; n\u200a=\u200a21 ) ; Tbilisi District #3 ages 1–4 ( 21 . 7%; n\u200a=\u200a23 ) ; ages 60 and above in Kutaisi ( 17 . 24% ) ( Table 1 ) ., While overall prevalence was not significantly different between Tbilisi districts ( P>0 . 05 ) , a clear difference was observed in total prevalence in Tbilisi compared with Kutaisi ( P\u200a=\u200a0 . 0019 , Χ2 test; Table 1 ) ., We further compared LST-positivity rates between male and female subjects in the same age groups ( Supporting Information Table S1 ) ., Whereas prevalence in male subjects ( 15 . 2% overall ) and female subjects ( 11 . 7% ) was not significantly different , a significant difference was observed specifically in the 25–59 age group ( 16 . 2% males versus 7 . 8% females LST positive; P\u200a=\u200a0 . 011 ) ; this may reflect a different behaviour pattern in working age subjects , with a larger percentage of men working outdoors in various occupations , in addition to males spending more social time outdoors; this is consistent with Georgian society , especially in an urban environment ., Based on the results of the rK39 test , we found that the highest proportion of seropositive pet dogs is present in District #2 ( 82/292 pet dogs , 28 . 1% ) and District #1 ( 24/89; 27% ) in Tbilisi; in District #3 the percentage was 21 . 5% ( 52/242 ) ., Surprisingly , the percentage was also quite high in Kutaisi , as 17 . 3% of pet dogs were positive ( 26/150 ) , even though the first few cases of human VL in this city were reported only a few years ago ., Table 2 shows a breakdown of rK39-positivity rates in pets , stray dogs and wild canids showing a highly significant ( P\u200a=\u200a5 . 3×10−7; 3-way X2 test ) difference between those groups , with almost a quarter of all pet dogs testing positive as opposed to only 2 . 6% in wild canids ., A further stratification was applied for the pet dogs , by age and breed ( Table 2 ) , and a X2 test confirmed that age and breed had a significant ( P<0 . 001 ) influence on test outcome ( prevalence ) ., The table further shows that in particular the age of the dog is a main determinant , with increased age increasing infection rate , rather than the size of the breed ., We identified five Phlebotomus species of three subgenera in Tbilisi and 3 of these species were also found in Kutaisi ., Sand fly infectivity rates , species composition and the primary vectors have previously been reported to be different on either side of the Mtkvari River 6 ., Amongst the 5 Phlebotomus species in Tbilisi the most abundant were P . sergenti ( 43% of total ) and P . kandelakii ( 45% ) ; whereas no infected specimens of P . sergenti were found , P . kandelakii displayed an infection rate of 5 . 5% , with all the positives originating in District # 1 - ( Table 3 ) ., In District #1 , situated on the right side of the Mtkvari River , the most abundant species was P . kandelakii , with 69 . 8% ., In District #2 , only two sand flies species were identified and P . sergenti was by far the most abundant ( 91 . 8% ) ., In the rest of Tbilisi ( Districts 3 ) P . sergenti was also the most abundant ( 84% ) Phlebotomus species but no infected sand flies were found in this area ., The most abundant sand fly species collected in Kutaisi city were P . balcanicus ( 53 . 5% ) and P . halepensis ( 45 . 8% with infectivity rate of 1 . 3% ) - the two least prevalent Phlebotomus species in Tbilisi ., These results show a very different sand fly population in the two cities ., We report here the first detailed survey of leishmaniasis prevalence in humans and canids , and of phlebotomine sand fly populations , in the two main cities of Georgia ., Historical records as well as more recent clinical records show that leishmaniasis is more prevalent in the Eastern parts of the country ., Consistent with this trend , we find highly significant differences in infection rates between the Eastern city of Tbilisi and the Western city of Kutaisi ., Overall prevalence of infection , as measured by the standard LST test , was double the rate in Tbilisi ( P\u200a=\u200a0 . 0019 ) ; this was significant for most age groups and where it did not reach significance , this was mostly due to smaller sample sizes in Kutaisi ., Whereas LST does not detect 100% of all infections , especially during clinically active disease , potentially producing false negatives; however despite its long use , this test has never been associated with significant numbers of false positives ., We thus contend that the numbers reported here are more likely to be an underestimation than an overestimation of true leishmaniasis prevalence in Georgia ., The sand fly infection rate was quite high in Tbilisi and Kutaisi , 2 . 8% and 1 . 3% , respectively ., The species of Phlebotomus were also different according to regions , with P . sergenti and P . kandelakii in Tbilisi and P . halepensis and P . balcanicus in Kutaisi ., From this we surmise that the introduction of the disease to the Kutaisi region involved the adaptation of L . infantum to different vectors after the introduction of infected hosts to this region , with its distinct , endogenous sand fly population ., There is no historical record of sandfly infestation in the Kutaisi area , but the last sandfly survey of Western Georgia we are aware of dates from 1956 26 and this study reported only a very low number of P . chinensis , and only in the Zestafoni district ( approx 30 km South-east of Kutaisi ) ., Apart from this , we have found no mention of leishmaniasis , or its vectors , in Kutaisi or the surrounding region in Western Georgia in the literature or in clinical records 8 , 26 ., Here , we report specimens of both P . halepensis ( 43% of total sand fly population in Kutaisi ) and P . balcanicus ( 56% of total ) infected with Leishmania parasites in Western Georgia ( first time described in the country ) ., Using the models of Pampiglione et al . for Mediterranean leishmaniasis 27 , 28 , the low seropositivity rate in the 15–24 and 25–59 age groups in Kutaisi , relative to age groups 1–4 and 10–14 ( Table 1 ) , suggests that this VL focus was developed relatively recently , as in old foci infection rates increase with age ., Although we find the highest incidence in the 60-plus age group , this represents only 5 positives and we would hesitate to present this as evidence that Kutaisi is in fact an old focus ., As the overall prevalence in Kutaisi is lower than in Tbilisi , the Kutaisi focus is also clearly less active but we need to emphasize that the results presented here constitute the first research on VL in Kutaisi since the first case of this disease was reported there , in 2007 official statistical data of NCDC , and constitute a relatively small dataset ., The presence of infected P . halepensis in Western Georgia is potentially significant for a further reason: while this species is a suspected vector for L . infantum 29 it is also a suspected vector for L . major , and experimental infections with this species , at least , have been reported 30 While there is no proof of transmission of L . major in Georgia this possibility has never been investigated , and the presence of a potential vector in both eastern and western Georgia is of concern ., Pratlong et al recently described the epidemiological features of Old World cutaneous leishmaniasis foci , based on the isoenzyme analysis of 1048 strains , and list a confirmed L . major strain 31 ( as well as an L . donovani strain 32 ) from Georgia ., While it is not known whether this strain originated from local transmission or from a traveller infected elsewhere , 5 to 10 cases of cutaneous leishmaniasis are annually reported in Georgia official disease records , National Center for Disease Control ( NCDC ) , Tbilisi , Georgia ., However it is not currently known whether this is caused by L . infantum , which can also be responsible for cutaneous leishmaniasis 33 , 34 , or by L . major as suggested in the publication by Pratlong 31 ., Within Tbilisi , the highest prevalence of LST positive subjects was found in central districts of the city situated on the right side of Mtkvari River ( District 1 ) , which has the highest population density in Tbilisi ., Although this failed to reach statistical significance from the other two city areas sampled it correlates with the high number of seropositive pet dogs in that area and the highest diversity of Phlebotomine sand fly species in this district ( Table 3 ) ., The high LST prevalence among adult males compared to adult females in Tbilisi can be explained by their more frequent contacts with the vectors ., The social pattern is that in summer time adult males spent a significant part of the evening with their neighbours and/or friends in either their own gardens or in nearby open spaces ., At the same time most of adult females are doing housekeeping work and their social life is also far more indoors ., A positive LST result is thought to indicate durable cell-mediated immunity after asymptomatic infection or clinical cure of VL and it persists in immunocompetent patients 35 ., According to our observations during this study , for those dogs that were clinically suspected to have leishmaniasis , the rK39 test was weakly positive 19 ., The seropositivity among pet dogs was found to be proportionate to their age as previously reported in other countries 18 ., Seroprevalence was also high among stray dogs , however much less than in pet dogs ( P\u200a=\u200a0 . 00017 ) , which means that the cycle is typically domestic ., Indeed , most of the cases happen in houses with back gardens and chicken shelters , an appropriate environment for sand fly breeding , where dogs also cohabitate ., A possible reason for the comparatively low prevalence in stray dogs may be the frequent movement of these animals within and outside the city , taking in areas of low population and/or vector density ., Therefore , urgent control measures should focus on infected domestic dogs and vector control in the potential breeding sites around the house ., We think that the roaming behaviour of stray dogs limits the contact with VL vectors but potentially contributes to the spread of the disease to new areas , developing new VL foci ., The present study also reports for the first time that apparent VL being positive in the rK39 test was observed in wild foxes and jackals in Georgia ., This appears to prove the hypothesis of Bardjadze , who suggested that these animals ( especially the fox ) are the reservoirs in non-permanent VL foci in this country 8 ., However , it must equally be noted that we found only 2/77 wild canines ( 1 fox and 1 jackal ) to be positive for rK39 , a prevalence much below that of pet and stray dogs ., This result seems consistent with the observations of Courtenay et al ( 2002 ) 36 , who also observed low infection rates in wild foxes and concluded these are not important for the spread of L . infantum infection ., The transmission in active urban foci is thus from domestic dogs to human and , in the densely populated urban environment , appears to be much higher than in sylvatic environments ., Thus , while we confirm that foxes and jackals do appear to carry leishmania parasites in non permanent foci of the Eastern part of the country they are not considered the main source of this disease in permanent foci such as Tbilisi and Kutaisi , in part due to their seasonal migration uplands in the summer , which is the disease transmission period ., We determined 5 Phlebotomus species in the selected districts of Tbilisi ., The sand fly season starts in the last weeks of June or early July and ends in the middle of September 8 ., The sand fly population peaks in the middle of July and starts to come down after the middle of August ., The number of these vectors is strongly dependent on climate and environmental conditions 6 , 7 ., The data summarised in Table 3 show that vector composition was different on either side of the Mtkvari River , even though the overall infection rate was very similar ( P>0 . 05 ) ., In District 1 , P . kandelakii was by far the most prevalent species ( 71 . 7% ) , whereas this species was not found in District 2 , where P . sergenti was dominant ( 94 . 1% ) ; P . sergenti was also the dominant sand fly species found in the rest of Tbilisi ( 77 . 3% ) ., P . kandelakii was found to be infected with Leishmania parasites ., No infected sand flies were found in District 3 ( ‘other Tbilisi’ ) , but this could reflect the comparatively low number of flies sampled in this area ., This study shows that Tbilisi is an active focus for VL with very high infection prevalence in pets , in stray dogs and in humans as determined by the LST and rK39 tests ., The infection rate in sand flies is also high , consistent with a recent report on the sand fly population in Tbilisi 37 ., The microclimate of this city and the social behaviour of the population create conditions that are very favourable for the sand flies and for the spread of the infection ., We demonstrate that almost the entire population of Tbilisi is at risk from VL , including all age groups , and in all districts , in large part because of the high percentage , and number , of seropositive dogs in the city ., The outcome of the LST survey shows that a very significant percentage of the population has already been in contact with the parasite , although this does not imply that all will develop the clinical manifestations ., The comprehensive survey described in this manuscript has for the first time documented the very significant risk to the Georgian population from visceral leishmaniasis , and that its transmission is spreading to the west of the country ., The results obtained will allow the Georgian health authorities to initiate control measures to reduce the high urban transmission rates responsible for the current outbreak , which displays the largest dimensions in many years , and to formulate a national strategy for leishmaniasis prevention and for improving treatment efforts to protect its population and economy from this very severe disease , of which the capital is especially at risk ., In order to arrive at a comprehensive national strategy , however , it will be necessary to expand this survey to other districts of Georgia and to map the environmental risk factors not only in known areas of transmission but also at the national level to anticipate the progression of the disease to other vulnerable areas .

Introduction, Materials and Methods, Results, Discussion

This study investigated the transmission and prevalence of Leishmania parasite infection of humans in two foci of Visceral Leishmaniasis ( VL ) in Georgia , the well known focus in Tbilisi in the East , and in Kutaisi , a new focus in the West of the country ., The seroprevalence of canine leishmaniasis was investigated in order to understand the zoonotic transmission ., Blood samples of 1575 dogs ( stray and pet ) and 77 wild canids were tested for VL by Kalazar Detect rK39 rapid diagnostic tests ., Three districts were investigated in Tbilisi and one in Kutaisi ., The highest proportions of seropositive pet dogs were present in District #2 ( 28 . 1% , 82/292 ) and District #1 ( 26 . 9% , 24/89 ) in Tbilisi , compared to 17 . 3% ( 26/150 ) of pet dogs in Kutaisi ., The percentage of seropositive stray dogs was also twice as high in Tbilisi ( 16 . 1% , n\u200a=\u200a670 ) than in Kutaisi ( 8% , n\u200a=\u200a50 ) ; only 2/58 wild animals screened were seropositive ( 2 . 6% ) ., A total of 873 Phlebotomine sand flies were collected , with 5 different species identified in Tbilisi and 3 species in Kutaisi; 2 . 3% of the females were positive for Leishmania parasites ., The Leishmanin Skin Test ( LST ) was performed on 981 human subjects in VL foci in urban areas in Tbilisi and Kutaisi ., A particularly high prevalence of LST positives was observed in Tbilisi District #1 ( 22 . 2% , 37 . 5% and 19 . 5% for ages 5–9 , 15–24 and 25–59 , respectively ) ; lower prevalence was observed in Kutaisi ( 0% , 3 . 2% and 5 . 2% , respectively; P<0 . 05 ) ., This study shows that Tbilisi is an active focus for leishmaniasis and that the infection prevalence is very high in dogs and in humans ., Although exposure is as yet not as high in Kutaisi , this is a new VL focus ., The overall situation in the country is alarming and new control measures are urgently needed .

Leishmaniasis is a disease complex of various clinical manifestations caused by infection with protozoan parasites ( Leishmania spp ) ., It is transmitted through the bite of infected sand flies ( Phlebotomus or Lutzomyia spp ) and dogs are the main reservoir host for the Leishmania infantum species described previously in Georgia ., It is prevalent in many tropical and subtropical regions of the world ., In Georgia , visceral leishmaniasis has been known to occur in the East and in the capital , Tbilisi , but to date no investigation of vectors , and of prevalence in humans and in canines , has been conducted ., Here , we report 5 different species of sand fly in Tbilisi and 3 in the West-Georgian city of Kutaisi , with infected vectors found in both places ., In some districts of Tbilisi more than a quarter of pet dogs were seropositive for Leishmania parasites; prevalence in stray dogs was somewhat lower ., Even in Kutaisi , where no leishmaniasis has previously been reported , 17 . 3% of pet dogs tested positive ., This was reflected in high prevalence of infection in humans in the capital ( 14 . 5% overall ) , compared to 7 . 3% in Kutaisi ., We conclude that the infection rate with visceral leishmaniasis in Georgia is alarmingly high and that its transmission has significantly spread west-wards in recent times .

medicine, infectious disease epidemiology, epidemiology

journal.pgen.1003667

ATM Release at Resected Double-Strand Breaks Provides Heterochromatin Reconstitution to Facilitate Homologous Recombination

DNA double-strand breaks ( DSBs ) are among the most deleterious cellular lesions since they threaten genomic integrity and cell viability ., To counteract cell degeneration and to preserve genomic integrity , a complex network of DSB repair and signaling processes has evolved 1–4 ., Two main DSB repair pathways exist , canonical non-homologous end-joining ( c-NHEJ ) and homologous recombination ( HR ) 5 , 6 ., In mammalian cells , c-NHEJ represents the major repair pathway for ionizing radiation ( IR ) -induced DSBs 7 ., C-NHEJ repairs unresected break ends without the need for sequence homologies and can function throughout the cell cycle 8 ., The key factors in c-NHEJ involve the KU70/80 heterodimer , which binds to the DSB end , and the DNA-dependent protein kinase catalytic subunit ( DNA-PKcs ) , which , together with KU70/80 , constitutes the DNA-PK holoenzyme ., The repair process is completed by a complex of DNA ligase IV , XRCC4 , and XLF/Cernunnos 5 ., In contrast to c-NHEJ , HR is restricted to the S and G2 phases of the cell cycle where break ends undergo extensive resection and homologous DNA sequences on the sister chromatid serve as a template for repair ., In addition to the repair of DSBs , HR functions during the S phase to restart stalled or collapsed replication forks 9 ., HR is initiated by CtIP-dependent resection to create 3′-overhangs at the DSB ends 10 , 11 ., Following extended resection by EXO1 or BLM/DNA2 , loading of RAD51 onto single-stranded DNA ( ssDNA ) is facilitated by BRCA2 , XRCC2 , and XRCC3 ., RAD54-mediated homology search then promotes strand exchange and Holliday junction formation 6 ., HR is completed after repair synthesis by Holliday junction resolution and DNA end ligation ., In the absence of c-NHEJ factors , DSB repair can also occur by an alternative end-joining mechanism , termed alt-NHEJ 12 , 13 ., In contrast to c-NHEJ but similar to HR , alt-NHEJ involves CtIP-dependent resection ., The resected break ends are subsequently rejoined by a process involving micro-homologies and various repair factors such as poly ( ADP-ribose ) polymerase ( PARP ) , DNA ligase I or III , and XRCC1 14–17 ., Although alt-NHEJ can efficiently operate in cells devoid of c-NHEJ factors , little is known about its ability to compensate for HR defects ., It has become clear over the last years that higher order chromatin structure impacts on the response to DSBs 18 ., Thus , IR-induced DSBs in densely compacted heterochromatin ( HC ) are more difficult to repair than euchromatic ( EC ) DSBs and they require additional structural changes in the surrounding chromatin 19 , 20 ., One example are ATM-mediated chromatin changes due to KAP-1 phosphorylation 21 ., In undamaged cells , KAP-1 forms HC by recruiting HP1 , CHD3 and other remodeling factors 22 , 23 ., DSB-induced KAP-1 phosphorylation leads to release of CHD3 which locally relaxes HC and facilitates repair 23 ., Other studies involving HP-1 mobilization have observed either a release from 24 or a recruitment to damaged chromatin 25–27 ., These apparently conflicting findings have led to the suggestion that a transient release might be followed by an accumulation of HP1 at sites of DNA damage 19 , 28 ., However , it is often unclear how the various processes of chromatin modification impact on DSB repair and if different repair pathways are differentially affected ., Repair kinetics for IR-induced DSBs are biphasic , exhibiting a fast and a slow component 29 ., The slow component accounts for the repair of a subset ( 15–20% ) of IR-induced DSBs that are localized to HC DNA regions , whereas DSBs induced in EC regions are typically repaired with fast kinetics ., In G1 phase , the fast and the slow component of DSB repair comprise a c-NHEJ mechanism 29 ., ATM-dependent phosphorylation of KAP-1 on serine 824 ( S824 ) is specifically required for the slow component 30 , 31 ., In G2 phase , in contrast , c-NHEJ accounts only for the fast DSB repair process , while the slow ATM-dependent HC component represents HR 32 ., Thus , in G2 , defined DSB populations , EC vs . HC breaks , are repaired by either c-NHEJ or HR , respectively ., Despite the existence of two repair pathways in G2 , a mutation in one of them leads to elevated unrepaired DSBs ., Thus , c-NHEJ and HR cannot compensate for each other which might be attributed to the fact that c-NHEJ is unable to repair DSBs which have undergone extensive resection ., Consistent with this notion , c-NHEJ can compensate for HR if resection is prevented by CtIP depletion 33 ., What remains unclear is why alt-NHEJ , which in principal is able to rejoin resected break ends , cannot compensate for a loss of down-stream HR factors such as BRCA2 or RAD51 ., In the present study , we analyzed the process of HR at HC DSBs in G2 phase ., We show that the intensity of phosphorylated ATM at DSBs decreases during the process of resection , suggesting that ATM initially binds to but is then released from DSBs which undergo repair by HR ., Consistent with this notion , chemical inhibition of ATM prior to but not after resection causes a repair defect ., Thus , ATM has an early role during HR but is dispensable for later stages ., This contradicts the situation in G1 where continuous ATM activity is required for HC DSB repair by c-NHEJ 34 ., In G1 , ATM functions to phosphorylate KAP-1 , leading to its inactivation and local relaxation of the HC structure 30 ., Moreover , depletion of KAP-1 by siRNA overcomes the requirement for ATM in G1 but leads to reduced HR usage in G2 ., Finally , following KAP-1 siRNA or expression of a phospho-mimic form of KAP-1 , both of which cause HC relaxation , resected DSBs can be repaired by a PARP-dependent alt-NHEJ process ., Together , these data show that the HC structure represents a barrier for repair by c-NHEJ and alt-NHEJ but facilitates usage of HR ., ATM , which initially binds to DSBs , is released from break ends during the process of resection ., This prevents usage of c-NHEJ and alt-NHEJ and commits resected DSBs to repair by HR ., We have previously demonstrated that BRCA2-deficient cells exhibit elevated γH2AX foci levels at 8 h post irradiation in G2 1 , 32 ., These unrepaired DSBs have undergone efficient end-resection as evidenced by RPA loading ( Figure 1A ) which might explain why they cannot be repaired by NHEJ ., We sought to further characterize these breaks and observed that the pATM focal intensity in G2- but not in G1-phase cells is greatly diminished at 8 h compared with 30 min time points ( Figure 1A and Figure S1A ) ., In contrast , the γH2AX focal signal is equally intensive at 30 min and 8 h in G1 and G2 ( Figure S1B ) ., We also measured the pATM focal intensity at 2 h post IR , a time point when resected and unresected DSBs are present in G2-phase cells ., Of note , the pATM focal intensity of RAD51-foci-positive resected breaks is reduced compared with RAD51-foci-negative unresected breaks ., In contrast , the γH2AX focal intensity is similar or even slightly increased at resected versus unresected DSBs ( Figure 1B ) ., These findings suggest that the pATM focal intensity decreases during resection in G2 ., pATM contributes , together with DNA-PKcs and ATR , to the phosphorylation of H2AX 35 , 36 ., To test if the loss of pATM intensity at the break site leads to reduced ATM activity , we measured the γH2AX focal intensity in cells with strongly diminished levels of ATR , a kinase which is activated by ssDNA regions 37 ., Significantly , although ATR-deficient cells show γH2AX focal intensities at unresected DSBs similar to wildtype ( wt ) cells , they exhibit greatly diminished intensities at resected breaks ( Figure 1C ) ., Consistent with the notion that ATM is active at unresected but not at resected DSBs , chemical inhibition of ATM only affects γH2AX foci intensities at unresected but not at resected DSBs ( Figure 1D and Figure S1C ) ., We next sought to confirm the immunofluorescence ( IF ) measurements by Western blotting ., We used A549 tumor cells which can be efficiently synchronized in G1 by serum starvation and moderately enriched in G2 by double thymidine blocking ( Figure S2A ) ., The level of chromatin-bound pATM decreases with time after IR due to ongoing repair in G1 and in G2 but , importantly , at later times the pATM level per γH2AX level is smaller in G2-enriched than in G1-synchronized cells ( Figure 2A ) ., We also measured pKAP-1 ( S824 ) levels as a specific read-out for ATM activity 21 and obtained similar results ( Figure 2A ) ., We next wished to measure pATM bound to DSBs and employed immunoprecipitation ( IP ) experiments ., For this , we used HeLa tumor cells which can be efficiently synchronized in G2 ( Figure S2B ) ., Strikingly , pATM bound to γH2AX is readily detected at 30 min but nearly absent at 8 h post IR in G2 ( Figure 2B ) ., To directly show that the diminished pATM activity in G2 is a result of resection , we inhibited resection by depleting CtIP or BLM 38 and measured pKAP-1 levels ., G2-synchronized HeLa tumor cells show a strongly reduced pKAP-1 level at 4 h post IR compared with unsynchronized cells which is fully or partly restored after CtIP or BLM depletion ( Figure 2C and Figure S2C ) ., To provide evidence for the restoration of chromatin condensation at resected DSBs , we performed IP experiments as in Figure 2B ., We observed that the level of KAP-1 bound to γH2AX continuously increases with repair time ( Figure 2D ) , possibly due to an enrichment of HC DSBs at longer times and the recruitment of KAP-1 to damaged sites as previously reported 25 ., Importantly , γH2AX-bound KAP-1 is substantially phosphorylated at early times post IR but largely unphosphorylated at later times ( Figure 2D ) ., Together , these biochemical approaches confirm the IF data above and provide strong evidence that ATM accumulation and activity is strongly reduced at DSBs which undergo resection ., This leads to KAP-1 dephosphorylation and possibly the restoration of HC ., The observed diminished ATM activity at resected DSBs is consistent with studies using a human cell extract-based assay in which ATM is activated by blunt DSB ends and ends with short ss overhangs but not by extended ssDNA regions which arise during the process of resection 39 ., ATM has been implicated in early steps of HR 33 , 40 , 41 ., A prediction of our findings above is that ATM is no longer required for HR after resection has occurred ., To test this , we inactivated ATM either before or at 2 h post IR , a time point when resection has occurred ( Figure S1C ) , and investigated the efficiency of DSB repair ., γH2AX foci numbers at 8 h post IR were substantially elevated both in G1- and G2-phase cells treated with ATM inhibitor ( ATMi ) before IR but only in G1-phase and not in G2-phase cells if ATMi was added 2 h post IR ( Figure 3A ) ., We also analyzed mitotic chromatid breakage in G2-irradiated cells and observed substantially elevated break levels if ATMi is administered before irradiation but not if it is added 2 h post IR ( Figure 3B ) ., HR in G2 leads to sister chromatid exchanges ( SCEs ) 42 which are diminished if ATM is inhibited before but not at 2 h after IR ( Figure 3C ) ., Together , these data show that ATM is dispensable for HR stages that occur after resection has taken place ., It was previously shown that ATM operates in G1 by continuously phosphorylating KAP-1 at heterochromatic DSBs and that KAP-1 depletion overcomes the requirement for this ATM function 34 ., Since ATM accumulation and activity is reduced at resected DSBs , we next asked if KAP-1 depletion might affect DSB repair in G2 ., KAP-1 siRNA did not alter γH2AX foci numbers in wt cells but strikingly rescued the repair defect in BRCA2 mutants and cells treated with BRCA2 siRNA ( Figure 4A and Figure S3A ) ., The same effect was observed in CHO cells deficient for the HR factor XRCC3 as well as in RAD51-depleted CHO cells ( Figures S3B and S3C ) ., Moreover , KAP-1 siRNA reduced the elevated level of chromatid breaks in BRCA2-deficient cells to that of wt cells ( Figure 4A ) ., We also measured the formation of SCEs and did not observe any IR-induced SCE formation in BRCA2/KAP-1-depleted cells ( Figure S3D ) ., Finally , we investigated cells containing an integrated HR reporter with two differentially mutated GFP genes 43 ., Expression of the endonuclease I-SceI generates a DSB in one of the two genes which can be repaired by HR ( gene conversion ) with the second gene copy as a template , resulting in a cell with functional GFP ., HR frequencies assessed by the fraction of GFP-positive cells are significantly decreased after BRCA2 depletion and dual depletion of BRCA2 and KAP-1 , confirming that the repair events occurring in BRCA2/KAP-1-depleted cells do not represent HR ( Figure S3E ) ., A pathway switch from HR to c-NHEJ has recently been demonstrated for heterochromatic DSBs after the inhibition of resection by CtIP siRNA , consistent with the idea that resection determines DSB repair pathway choice 33 ., Therefore , we asked if RPA foci formation , as a read-out for resection , is affected by KAP-1 depletion ., Significantly , wt and BRCA2-depleted cells show the same initial level of RPA foci at 2 h post IR which is unaffected by KAP-1 siRNA ., These RPA foci persist in BRCA2-depleted cells up to 8 h post IR consistent with their elevated γH2AX foci level ., In contrast , RPA foci numbers decrease with time due to ongoing repair in wt and BRCA2-depleted cells treated with KAP-1 siRNA ( Figure 4B and Figure S3F ) ., We also investigated RAD51 loading at resected DSBs and observed normal RAD51 foci numbers after KAP-1 siRNA in wt but not in BRCA2-depleted cells ( Figure 4B ) ., The finding that a BRCA2-independent process repairs resected DSBs after combined BRCA2 and KAP-1 siRNA suggests that the commitment for HR results from the loss of pATM at resected DSBs which is overcome by KAP-1 depletion ., To consolidate this finding , we investigated DSB repair in cells treated with KAP-1 siRNA and complemented with siRNA-resistant KAP-1 constructs which were mutated at the ATM-dependent phosphorylation site on S824 30 ., The BRCA2 repair defect , which is rescued after KAP-1 siRNA , is restored after complementation with wt KAP-1 or with KAP-1 rendered unphosphorylatable by mutating serine at position 824 to alanine ( S824A ) ., Significantly , however , KAP-1 mutated to a phospho-mimic aspartate at position 824 ( S824D ) fails to restore the BRCA2 repair defect ( Figure 4C ) ., Thus , KAP-1 phosphorylation at the established ATM site 824 overcomes the commitment for HR and DSB repair in the absence of BRCA2 can proceed by an HR-independent process ., Next , we wanted to investigate the process which is employed in BRCA2-deficient cells for the repair of resected DSBs ., For this , we depleted BRCA2 and/or KAP-1 in cells deficient in the c-NHEJ factor XLF ., XLF-defective cells show greatly elevated γH2AX foci and chromatid breaks consistent with the notion that c-NHEJ represents the predominant repair pathway in G2 32 ., Interestingly , depletion of BRCA2 leads to a similar increase in γH2AX foci/chromatid break numbers in wt cells and XLF mutants , demonstrating additivity of the two major repair pathways in G2 , c-NHEJ and HR ( Figure 5A ) ., But most importantly in the present context , dual depletion of BRCA2 and KAP-1 did not affect γH2AX foci/chromatid break numbers in XLF mutants , demonstrating that the HR defect is rescued by KAP-1 depletion even in the absence of the c-NHEJ factor XLF ( Figure 5A ) ., The same effect was observed in CHO cells deficient in the c-NHEJ factor KU80 ( Figure S4A ) ., We then tested if an alt-NHEJ pathway repairs DSBs in BRCA2/KAP-1-depleted cells and employed chemical inhibition of PARP ( PARPi ) , a factor which has been implicated in alt-NHEJ 14 , 17 ., γH2AX foci and chromatid breaks were not significantly affected in wt cells treated with PARPi , demonstrating that alt-NHEJ processes do not contribute substantially to IR-induced DSB repair in normal cells ., However , the elevated level of γH2AX foci/chromatid breaks in BRCA2-deficient cells , which is rescued after KAP-1 siRNA , is restored by PARPi ( Figures 5B and 5C ) ., Thus , PARPi precluded the repair events which arose in BRCA2-deficient cells after KAP-1 siRNA , demonstrating that a PARP-dependent process can function as a back-up pathway for HR ., We also investigated other factors which have been described to function in alt-NHEJ ., In CHO mutants deficient in XRCC1 as well as in cells deficient for DNA ligase I and III , KAP-1 failed to rescue the elevated γH2AX foci level which is conferred by a deficiency in BRCA2 or RAD51 ( Figure 5D and Figure S4B ) ., Consistent with the notion that alt-NHEJ can function as a back-up pathway for HR , we observed greatly increased levels of chromatid fusions in BRCA2/KAP-1-depleted cells ( Figure 5E ) ., To characterize the nature of these chromatid fusion events , we employed fluorescence-in-situ-hybridization ( FISH ) analysis with chromosome-specific probes ., In one set of experiments , we used probes for chromosomes 1 , 2 and 4 and observed that all fusion events ( ∼40 fusions from the analysis of ∼800 cells ) occurred between heterologous chromosomes , that is , between a stained and an unstained chromosome or between two differently stained chromosomes ( Figure 5F ) ., Further , we employed probes for chromosome 19 which is exceptionally rich in KAP-1 binding sites and for the similar-sized chromosome 18 which is largely devoid of these sites 44 ., Following BRCA2 depletion , we observed significantly higher breakage levels in chromosome 19 compared with chromosome 18 , confirming that HR in G2 occurs mainly in KAP-1-dependent HC ( Figure 5G ) ., Importantly , following dual depletion of BRCA2 and KAP-1 , chromosome fusions occur more often in chromosome 19 than in chromosome 18 confirming the notion that they arise from the misrejoining of chromatid breaks in KAP-1-dependent HC ( Figure 5G ) ., The data above show that KAP-1 depletion allows heterochromatic DSBs to be repaired by an alt-NHEJ pathway in the absence of BRCA2 , XRCC3 or RAD51 ., It is , however , unclear how the efficiency of HR in wt cells is affected by KAP-1-mediated chromatin changes ., As shown above , γH2AX foci and chromatid breaks are repaired with similar kinetics with and without KAP-1 siRNA ( see Figure 4A ) but it is not known if repair after KAP-1 siRNA involves HR or , as in the case of HR mutants , an alt-NHEJ pathway ., To address this question , we investigated the formation of SCEs in mitotic cells and observed greatly diminished SCE levels after KAP-1 siRNA in wt cells ( Figure 6A ) ., We also employed the HR reporter assay described above ( Figure S3E ) and observed strongly reduced HR levels following KAP-1 depletion ( Figure 6B ) ., Thus , KAP-1-depleted cells do not employ HR although repair occurs efficiently ., We also analyzed chromatid fusion events as a read-out for incorrect end-joining ., Strikingly , KAP-1-depleted cells show elevated chromosomal fusions , suggesting that the DSBs are repaired by an error-prone alt-NHEJ pathway ( Figure 6C ) ., This notion is consolidated by the observation that PARPi increases γH2AX foci and chromatid break numbers in cells depleted for KAP-1 or complemented with phospho-mimic KAP-1 ( S824D ) ( Figures 6D and 6E ) ., Further , cells deficient in DNA ligase I and III or in XRCC1 show elevated γH2AX foci levels following KAP-1 depletion ( Figures 6F and 6G ) ., Taken together , this data shows that HR is efficiently used in cells with unphosphorylatable KAP-1 and cannot occur if KAP-1 is depleted ., ATM binding and activation at DSB ends occurs within minutes after damage induction and is important for the initiation of various signaling processes 45 ., Concomitant with the induction of signaling pathways , a variety of chromatin remodeling processes are initiated ., This involves modifications which either relax or condense the chromatin structure in the surrounding of DSBs ., However , it is currently unclear how these changes are chronologically orchestrated and how they differentially affect different DSB repair pathways in different chromatin compartments ., Therefore , we focused our investigation on chromatin modifications which occur in HC regions due to the process of resection in order to specifically investigate how such chromatin changes impact on later stages of HR ., We did not examine chromatin remodeling processes at early times which affect the decision to initiate resection ., We have previously shown that ATM is dispensable for the majority of DSB repair in G1 but that HC breaks strictly require ATM 30 ., ATMs function during HC DSB repair in G1 involves continuous KAP-1 phosphorylation which leads to local HC relaxation 23 ., Our finding that ATM is released from resected DSBs in G2 was therefore unexpected ., However , there is precedence in the literature that ATM changes binding properties upon resection of DSBs ., First , ATMs binding affinity to break ends has been reported to be attenuated with the progressive presence of ssDNA at resected DSBs 39 ., This ATM attenuation is accompanied by increasing ATR activity 39 , consistent with our result that H2AX phosphorylation at RAD51-foci-positive DSBs requires ATR ., Second , 53BP1 , a damage response factor which localizes to and facilitates pATM accumulation at DSB sites 34 , has been reported to show reduced occupancy at resected DSBs in G2 46 ., Although the reported reduction of ATM accumulation and activity at resected breaks is consistent with published data , the functional consequence of this finding was hitherto unclear ., In G2 phase , DSB repair can be performed by NHEJ and HR ., It is therefore remarkable that cells with mutations in BRCA2 , XRCC3 or RAD51 exhibit unrejoined DSBs , which obviously are refractory to repair by NHEJ ., Thus , it has been suggested that the process of resection commits DSB repair to HR and prevents usage of NHEJ 33 ., Here , we provide mechanistic insight into the processes determining pathway usage upon resection ., Since ATM is released from resected DSBs we reasoned that the concomitant reduction in KAP-1 phosphorylation prevents repair of resected breaks by NHEJ ., Indeed , if loss of ATM-dependent KAP-1 phosphorylation is overcome by KAP-1 depletion or expression of phospho-mimic KAP-1 , BRCA2- , XRCC3- or RAD51-deficient cells exhibit normal repair kinetics ., Thus , it is not the resection per se but the loss of ATM activity at resected breaks which commits repair to HR ., HC DSBs which remain unrepaired in BRCA2- , XRCC3- or RAD51-deficient cells can be repaired if HC relaxation is provided by KAP-1 depletion or expression of phospho-mimic KAP-1 ., Interestingly , these DSBs undergo resection as evidenced by normal RPA foci formation ., Thus , HC repair occurring in the absence of BRCA2 , XRCC3 or RAD51 must involve a pathway which is capable of dealing with resected breaks ., Consistent with the notion that alt-NHEJ can repair resected DSBs , we showed that the HC repair events occurring in the absence of BRCA2 , XRCC3 or RAD51 require PARP , XRCC1 and DNA ligase I/III ., We also observed that HC repair in the absence of BRCA2 has a significant propensity to lead to chromatid exchanges in G2-irradiated cells ., Because alt-NHEJ has been implicated in the formation of genomic exchanges 47–50 , this finding supports our contention that HC repair in the absence of BRCA2 , XRCC3 or RAD51 involves alt-NHEJ ., Perhaps surprisingly , we observed that the process of HR is nearly abolished in cells with depleted KAP-1 , even in the presence of functional HR factors ., This suggests that DSB repair pathway usage is significantly affected by chromatin modifications , favoring HR in condensed genomic regions ., This notion is further supported by the observation that PARP inhibition or the loss of XRCC1 or DNA ligase I and III leads to elevated unrepaired breaks in KAP-1-depleted cells , which not only demonstrates that cells use alt-NHEJ but also , that they cannot employ HR in the absence of KAP-1-dependent HC ., In summary , these findings establish that KAP-1-dependent HC is not only a barrier to repair by c-NHEJ or alt-NHEJ but , unexpectedly , also facilitates the process of HR ., Consistent with our results , depletion of HP1α or KAP-1 strongly reduces gene conversion frequencies in a I-SceI-based HR assay 25 ., Furthermore , HP1α and KAP-1 is recruited to chromatin damaged by laser- or X-irradiation 26 , 27 and depletion of HP1α diminishes SCE formation after treatment with camptothecin 51 ., One explanation of how HC might promote HR is that a reduced spatial distance between sister chromatids in HC regions facilitates homology search 52 ., In support of this idea , we have recently obtained preliminary evidence that the average distance between sister chromatids , measured by FISH analysis with locus-specific probes , is substantially larger in EC versus HC regions ( Geuting et al . , unpublished data ) ., A similar mechanism has been suggested for cohesin proteins which might promote HR by providing the required proximity of sister chromatids in G2 phase 53 ., Another explanation of how HC might facilitate HR is by suppressing alt-NHEJ processes ., Although it is well established that the presence of KU70/80 at DSB ends prevents repair by alt-NHEJ , KU70/80 is likely released from resected DSB ends ., Chromatin condensation occurring due to ATM release at resected DSBs might represent an alternative mechanism to keep error-prone alt-NHEJ processes in check ., In conclusion , our study provides mechanistic insight into sequential events determining DSB repair pathway usage ., First , we demonstrate that ATM activity is diminished at DSBs which undergo resection during the process of HR ., Second , the concomitant loss of pKAP-1 at resected DSBs leads to local reconstitution of the HC superstructure and prevents repair of resected DSBs by alt-NHEJ ., Thus , our study links two seemingly unrelated findings by showing how modifications at DSBs undergoing resection affect chromatin remodeling processes and DSB repair pathway usage ., Immortalized and transformed cell lines were 82-6 hTert ( wt ) , HSC62 hTert ( BRCA2-deficient , kindly provided by Dr . M . Digweed ) , 2BN hTert ( XLF-deficient , kindly provided by Dr . P . Jeggo ) and F02-98 hTert ( ATR-deficient , kindly provided by Dr . P . Jeggo ) human fibroblasts , HeLa-S3 , HeLa pGC ( kindly provided by Dr . J . Dahm-Daphi ) and A549 human tumor cells , and CHO-AA8 ( wt ) , IRS1SF ( XRCC3-deficient; kindly provided by Dr . L . Thompson ) , CHO-K1 ( wt ) , XRS6 ( KU80-deficient , kindly provided by Dr . P . Jeggo ) , CHO-9 ( wt ) and EMC11 ( XRCC1-deficient , kindly provided by Dr . B . Kaina ) hamster cells ., HeLa-S3 and A549 tumor cells were cultured in DMEM with 10% FCS and 1% NEAA; HeLa pGC cells additionally in 0 . 3 µg/ml puromycin ., Human fibroblasts and CHO cells were cultured in MEM with 20% FCS , 1% NEAA ., All cells were maintained at 37°C in a 5% CO2 incubator ., SiRNA transfection was carried out with HiPerFect Transfection Reagent ( Qiagen ) following the manufacturers instructions ., siRNAs used in the experiments were: BLM ( 50 nM ) , Control ( 10 nM ) , CtIP ( 20 nM ) , KAP-1 ( 25 nM ) , RAD51 ( 20 nM ) , Lig I ( 20 nM ) , Lig III ( 20 nM ) ( Qiagen ) , and BRCA2 ( 25 nM ) ( SmartPool , Dharmacon ) ., SiRNA sequences were: BLM ( AAG CUA GGA GUC UGC GUG CGA ) , BRCA2 ( GAA ACG GAC UUG CUA UUU A; GUA AAG AAA UGC AGA AUU C; GGU AUC AGA UGC UUC AUU A; GAA GAA UGC AGG UUU AAU A ) , Control ( AAU UCU CCG AAC GUG UCA CGU ) , CtIP ( UCC ACA ACA UAA UCC UAA UUU ) , KAP-1_A ( CAG UGC UGC ACU AGC UGU GAG ) , KAP-1_B ( CAU GAA CCC CUU GUG CUG UUU ) , RAD51 ( AAG GGA AUU AGU GAA GCC AAA ) , Lig I ( AAG GCA UGA UCC UGA AGC AGA ) , Lig III ( AAC CAC AAA AAA AAU CGA GGA ) ., Experiments were performed 48 h following siRNA transfection ., For GFP-tagged siRNA-resistant KAP-1 plasmid transfection , HeLa tumor cells were incubated with KAP-1_B or KAP-1_B and BRCA2 siRNA and , 8 h later , transfected with 1 µg plasmid DNA using Lipofectamine LTX Transfection Reagent ( Life Technologies ) ., Cells were irradiated with 2 Gy , fixed and stained for γH2AX , EdU and GFP ., Only GFP-positive cells were analyzed ., A549 tumor cells were used for G1 synchronization and G2 enrichment ., HeLa tumor cells were only used for G2 enrichment ., G1 synchronization was carried out by 48 h serum starvation in DMEM without FCS and NEAA ., 0 . 5 h before irradiation , medium was replaced by DMEM with FCS and NEAA ., For G2 enrichment , a double thymidine blocking was used ., Cells were blocked 16 h with 2 mM thymidine ( Sigma ) , released in fresh medium for 9 h , blocked again with 2 mM thymidine for 16 h and released in fresh medium for 7–8, h . Synchronization was controlled by FACs analysis as described previously 54 ., X-irradiation was performed at 90 kV and 19 mA with an aluminum filter ( dose rate: 2 Gy/min ) ., Chemical inhibitors were added 0 . 5 h prior to IR and maintained during repair incubation ., The ATM inhibitor ( Tocris KU 60019 ) , the DNA-PK inhibitor Nu7441 ( Tocris NU7026 ) and the PARP inhibitor PJ34 ( Calbiochem PARP inhibitor VIII PJ34 ) were used at concentrations of 5 µM , 10 µM and 20 µM , respectively ., Repair incubation was limited to time periods which provided that the majority of G2-irradiated cells remained in G2 ( controlled by FACs analysis ) ., Cells were grown on glass coverslips ., EdU ( 10 µM ) was added 0 . 5 h prior to IR to discriminate between S- and G2-phase cells ., In experiments analyzing G1-phase cells , nocodazol ( 100 ng/ml ) was added 0 . 5 h prior to IR to prevent G2-phase cells progressing into G1 during repair incubation 55 ., Cells were fixed and stained as described 56 and additionally stained with Click-it EdU ( Life technologies ) ., Antibodies used were: mouse-α-γH2AX at 1∶2000 ( Millipore ) ; rabbit-α-γH2AX at 1∶2000 ( Abcam ) , mouse-α-pATM at 1∶1000 ( Biomol ) , rabbit-α-RAD51 at 1∶15000 ( Abcam ) , mouse-α-RPA at 1∶2000 ( Neomarkers ) and mouse-α-GFP at 1∶200 ( Roche ) ., Cells were analyzed with a Zeiss microscope and Metafer software ( Metasystems ) ., Samples were evaluated in a blinded manner ., Foci intensities were analyzed using ImageJ software ( see Figure S1A ) ., HeLa pGC cells were incubated with siRNA and , 24 h later , transfected with 3 µg pBL464-pCBASce plasmid DNA using MaTra transfection ( IBA ) ., After 24 h , cells were again siRNA treated and , 48 h later , fixed and stained ., 10000 cells per sample were analyzed with a Zeiss microscope and Metafer software ( Metasystems ) ., Whole cell extracts were prepared as described 56 ., For chromatin fractionation , cells were resuspended two times in NP-40 buffer ( 10 mM Tris/HCl pH 7 . 5 , 10 mM NaCl , 3 mM MgCl2 , 30 mM sucrose , 0 . 5% NP-40 , 0 . 2 mM sodiumvanadate , 0 . 5 mM PMSF ) and centrifuged for 10 min at 1500×, g . Cell pellet was resuspended in Glycerol buffer ( 20 mM Tris/HCl pH 7 . 9 , 100 mM KCl , 0 . 2 mM EDTA , 20% glycerol , 0 . 2 mM sodiumvanadate , 0 . 5 mM PMSF ) and incubated 10 min on ice ., After centrifugation ( 10 min , 1500× g ) chromatin fraction was lysed and sonicated in RIPA buffer ( 50 mM Tris/HCl pH 8 , 150 mM NaCl , 0 . 5 Na-deoxycholate , 1% Triton , 0 . 1% SDS ) ., For immunoprecipitation , cells were fixed with 3% paraformaldehyd containing 2% sucrose for 5 min at 4°C , immediately washed with PBS , scraped in medium and centrifuged for 10 min at 400×, g . Cells were resuspended two times in NP-40 buffer containing 15 mM caffeine and centrifuged for 10 min at 1500×, g . Cell pellet was resuspended in equal volume Nuclease buffer ( 10 mM HEPES pH 7 . 5 , 10 mM KCl , 1 mM CaCl2 , 1 . 5 mM MgCl2 , 0 . 34 M sucrose , 10% glycerol , 0 . 1% Triton-X-100 , 0 . 2 mM sodiumvanadate , 0 . 5 mM PMSF , 15 mM caffe

Introduction, Results, Discussion, Material and Methods

Non-homologous end-joining ( NHEJ ) and homologous recombination ( HR ) represent the two main pathways for repairing DNA double-strand breaks ( DSBs ) ., During the G2 phase of the mammalian cell cycle , both processes can operate and chromatin structure is one important factor which determines DSB repair pathway choice ., ATM facilitates the repair of heterochromatic DSBs by phosphorylating and inactivating the heterochromatin building factor KAP-1 , leading to local chromatin relaxation ., Here , we show that ATM accumulation and activity is strongly diminished at DSBs undergoing end-resection during HR ., Such DSBs remain unrepaired in cells devoid of the HR factors BRCA2 , XRCC3 or RAD51 ., Strikingly , depletion of KAP-1 or expression of phospho-mimic KAP-1 allows repair of resected DSBs in the absence of BRCA2 , XRCC3 or RAD51 by an erroneous PARP-dependent alt-NHEJ process ., We suggest that DSBs in heterochromatin elicit initial local heterochromatin relaxation which is reversed during HR due to the release of ATM from resection break ends ., The restored heterochromatic structure facilitates HR and prevents usage of error-prone alternative processes .

Double-strand breaks ( DSBs ) are critical DNA lesions because they can lead to cell death or , which is even more devastating , the formation of genomic rearrangements ., Cells are equipped with two main pathways to repair such lesions , homologous recombination ( HR ) and non-homologous end-joining ( NHEJ ) ., HR is an error-free process and completely restores the genetic information , whereas NHEJ has the potential to form genomic rearrangements ., We have previously shown that the structure of the chromatin is one important factor which determines the choice between these two pathways , such that DSBs localizing to highly condensed heterochromatic regions are mainly repaired by HR and breaks in more open euchromatic DNA undergo repair by NHEJ ., Here , we investigate this aspect of DSB repair pathway choice ., We show that DSB end-resection , which channels DSB repair into the process of HR , counteracts the profound local relaxation which initially takes place at the break site and reconstitutes the heterochromatic structure ., Cells which are genetically modified , such that they cannot reconstitute the heterochromatic structure at resected DSBs , fail to employ HR and instead repair heterochromatic DSBs by alternative NHEJ mechanisms ., Thus , chromatin modifications which occur during the process of end-resection prevent error-prone repair pathways from generating genomic rearrangements .

molecular cell biology, biology, radiobiology

journal.pcbi.1005356

Single-molecule protein identification by sub-nanopore sensors

When Church et al . 1 proposed to use nanopores for sequencing biopolymers , they had envisioned both DNA and proteins sequencing ., However , the progress in protein sequencing turned out to be much slower since it is more difficult to force proteins through a pore systematically and measure the resulting signal 2 ., These difficulties underlay the experimental and computational challenges of Single Molecule Protein Identification ( SMPI ) ., Nanopores promise single molecule sensitivity in the analysis of proteins , but an approach for the identification of a single protein from its nanospectrum has remained elusive ., The most common approach to nanopore sequencing relies on the detection of the ionic –current blockade signal ( nanospectrum ) that develops when a molecule is driven through the pore by an electric field ., Preliminary work 3 , 4 was limited to analyzing protein conformations in pure solutions rather than identifying proteins in a mixture ., Subsequent steps demonstrated that nanopores can detect protein phosphorylations 5 as well as conformations and protein-ligand interactions 6 ., Recent studies on combining nanopores with aptamers have shown limited success for protein analysis 7 ., Proposals for electrolytic cell with tandem nanopores and for single molecule protein sequencing have been made , but not yet implemented 8–11 ., Recently , the sequence of amino acids in a denatured protein were read with limited resolution using a sub-nanometer-diameter pore , sputtered through a thin silicon nitride membrane 12 ., Protein translocations through the pore modulated the measured ionic current , which was correlated with the volumes of amino adids in the proteins ., However , the correlation was imperfect , making it difficult to solve the problem of reconstructing a protein from its nanospectrum with high fidelity ., Developing computational and experimental methods for analyzing nanospectra derived from a electrical signals that produced when a protein translocates through a sub-nanopore could enable a real-time sensitive approach to SMPI that may have advantages over top-down mass spectrometry for protein identification ., Despite difficulty and expense ( requiring especially powerful magnets ) to implement it , top-down mass spectrometry has been used in a few labs around the world to identify intact proteins and their proteoforms ., However , it is about 100-fold less sensitive than bottom-up mass spectrometry , which can be used to detect attomoles of material 13 ., In stark contrast , a sub-nanopore has been used to discriminate residue substitutions in a single molecule with low fidelity 12 ., Similar to mass-spectrometry , where de novo protein sequencing ( based on top-down spectra ) remains error-prone 14 , 15 , the challenge of de novo deconvoluting nanospectra into amino acids sequences of proteins is currently unsolved ., However , protein identification based on top-down spectra ( i . e . , matching a spectrum against all proteins in a protein database ) is a well-studied topic ., For example , top-down protein identification tools ProsightPC 16 and MS-Align+ 17 reliably identify proteins , report p-values of resulting Protein-Spectrum Matches ( PrSMs ) , and even contribute to improving gene annotations by discovering previously unknown proteins 18 ., In this paper , we describe the first algorithm for protein identification based on nanospectra derived from current blockades associated with denaturated , charge linearized translocation of protein through pores with sub-nanometer diameters ., Our Nano-Align algorithm matches nanospectra against a protein database , identifies Protein-Nanospectrum Matches ( PrNMs ) , and reports their p-values ., Our analysis revealed that the typical p-values of identified PrNMs vary from 10−4 to 10−6 , which is already sufficient for a limited analysis of nanospectra against small bacterial proteomes ., The software is publicly available at http://github . com/fenderglass/Nano-Align ., The details regarding the experiments and methods used to acquire electrical current blockade signals from the translocation of single protein molecules through sub-nanopores have been described elsewhere 12 ., To summarize , first , a pore with sub-nanometer cross-section was sputtered through thin silicon nitride membrane supported on a silicon chip using a tightly focused , high-energy electron beam in a scanning transmission electron microscope ( Fig 1 ) ., The thickness of the membranes ranged from 8 to 12nm ., Then the silicon chip supporting the membrane was embedded in a multiport microfluidic device that allowed for independent electrical access to the cis and trans-sides of the sub-nanopore by two Ag/AgCl electrodes ., To perform electrical measurements , the sub-nanopore was immersed in 0 . 2 − 0 . 3 M NaCl and a transmembrane voltage in the range between 300 − 700 mV was applied ., The resulting pore current was measured using an Axopatch 200B amplifier controlled with Clampex 10 . 2 software ., Finally , recombinant denatured protein , along with 2 ⋅ 10 − 3% sodium dodecyl sulfate that imparted a nearly uniform negative charge to the protein , were added to the microfluidic reservoir ( c . a . 20 fmoles of protein ) and subsequently blockades in the open pore current associated with single molecules translocating through the pore were observed ., It was determined that a lower transmembrane bias voltage improved the signal-to-noise ratio ( SNR ) and lengthened the median duration of the blockades , but it also increased the propensity for the pore to clog ., Multi-level events associated with residual native protein structure or multiple molecules competing for the pore were occasionally observed , but were manually culled from the data pre- analysis 12 ., Five proteins were analyzed by measuring the blockade currents through sub-nanopores: a recombinant chemokine CCL5 of length 68 AAs; two variants of the H3 histone designated as H3 . 2 and H3 . 3 , which consist of the chain of 136 AAs , differing only by residue substitutions at positions 32 , 88 , 90 and 91; a tail peptide of the H3 histone ( residues 1-20 ) and a fourth histone , H4 of length 103 AAs ., More details about the datasets are given at the ‘Datasets’ section below ., When a single molecule of protein translocates through the sub-nanopore , its amino acids block the flow of ions , causing a change in the open pore current Iopen ., The fraction of occupied pore volume Vmol/Vpore ( where Vpore and Vmol are volumes of the pore and molecule inside this pore , respectively ) was assumed to be proportional to the fractional blockade current , which is calculated as |I − Iopen|/Iopen , where I is the raw current during the translocation ., The raw signal measurements from the pore were pre-processed as follows: first , the discretized pore signal , sampled at 250 kHz , was split into the separate blockades , each one representing a translocation of a single protein ( Fig 2 ) Only events with sufficient duration to detect single-AA duration features were selected ., Typical blockade duration analyzed here ranged from 1 to 20 milliseconds , as shorter times did not permit accurate discrimination of intra-event features due to the measurement bandwidth ., The mean fractional blockade current varied from 0 . 05 to 0 . 5 for different nanospectra ., Recorded signals exhibited fluctuations that were associated with different structural features of a protein translocating through the pore ., Since the electrolytic current through the pore is associated with the occupied pore volume , one of the major factors that influences the signal is the volume of amino acids that occupy the sub-nanopore near the waist 19 ., The estimates of amino acid volumes were obtained from crystallography data 20 ., Since the pore can simultaneously accommodate multiple amino acids , it was assumed that the fluctuations in a blockade were proportional to a linear combination of amino acids volumes in the pore waist ., In particular , we found that the mean volume of amino acids yielded a good approximation of the empirical signal values ., Thus , given a protein P of length |P| , we split it into overlapping windows of size k ( or k-mers ) and generate a theoretical nanospectrum MV ( P ) as a vector of dimension |P| + k − 1 by taking the average volume of |P| − k + 1 k-mers and extra 2 * ( k − 1 ) shorter prefix and suffix substrings from the beginning and end of a protein ., These extra prefix and suffix substrings correspond to the start and the end of a translocation , when the pore is occupied by less than k amino acids ., For example , for k = 3 , the “protein” KLMNP results in a vector of length seven corresponding to the following substrings: K , KL , KLM , LMN , MNP , NP , and P . Experimental analysis of peptides with post-translational modifications and mutations 12 revealed changes in the specific regions of the recorded signal traces , that corresponded to approximately four amino acids in length ., In addition , simulations of the electric field in a 0 . 5x0 . 5 nm2 diameter , 8 nm thick pore in an SiN membrane indicated that the vast majority of the field was confined within 1 . 5 nm of the pore near the waist at the center of the membrane , which gives roughly the same estimate of the number of amino acids ., Thus , the Mean Volume ( MV ) model assumes that each fluctuation in the blockade current corresponds to a read of a quadromer ( short prefixes and suffixes of a protein correspond to shorter mers ) , which results in the best fit ( among all reasonable values of k ) with experimental nanospectra ., Generally , the MV model results in theoretical nanospectra correlated with the empirical data ., The mean Pearson product-moment correlation coefficient between a consensus of experimental nanospectra ( an average of multiple protein translocations , as described below ) and the corresponding MV model was ranging from 0 . 25 to 0 . 45 for various datasets ., However some regions show large deviations between theoretical and experimental nanospectra , which may be associated with additional attributes such as hydrophilicity or charge ., In particular , our analysis revealed that such discordant regions were enriched with small amino acids , which have volumes below the median value ( see Fig, 3 ) for illustration and ‘Characterizing errors of the models’ section below for the detailed discussion ) ., Since we acquired multiple nanospectra originating from multiple known proteins , an alternative approach for generating theoretical nanospectra was to use a supervised learning paradigm ., We used a Support Vector Regression ( an SVM-based regressor ) to establish the correspondence between a k-mer inside the pore and a signal it generates 21 ., Given an empirical nanospectrum E recorded from a protein P , we tiled P into overlapping quadromers qi and discretized E into |P| + 3 points ., Thus , each qi had an associated experimental signal value ei ., Next , the feature space of the model has to be defined ., Following the ideas of the MV model , it is natural to assume that blockade current is affected by the composition of amino acids in a quadromer , rather than their order ( however , the dependence might be non-linear ) ., As many of the 20 proteinogenic amino acids have similar volumes , we partitioned them into four volume groups ( Fig, 4 ) and defined a feature vector fi of a quadromer qi as the composition of amino acids from each group ( as a tuple of length four ) ., For example , an amino acid quardromer GQLD has zero amino acids from Large group ( > 0 . 2nm3 ) , two from Intermediate group ( between 0 . 15 and 0 . 2 nm3 ) , one from Small group ( between 0 . 11 and 0 . 15nm3 ) and one from Minuscule group ( < 0 . 11nm3 ) , and is converted to a feature vector ( 0 , 2 , 1 , 1 ) ., This choice of the feature space reduced the overfitting effect and increased coverage of the training dataset ( there are only 35 distinct quadromer compositions in the defined feature space versus 204 = 160 000 amino acid quadromers ) ., Using a set of pairs ( fi , ei ) we trained an SVR regressor with the Radial Basis Function kernel ( implemented in an open-source library libsvm 22 ) ., The Support Vector Regression ( SVR ) model takes a peptide P as input and outputs an SVR-based theoretical nanospectrum SVR ( P ) ( Fig 3 ) ., The mean Pearson correlation coefficient between the theoretical and empirical nanospectra ( consensus ) for the SVRmodel was varying from 0 . 38 to 0 . 68 for different datasets , confirming the improvement over the MV model ., The parameters of the SVR model were chosen through cross validation experiments and are equal to C = 1000 , γ = 0 . 001 , and ϵ = 0 . 01 ., The analysis of error patterns of the SVR model revealed a bias in the signal estimation that was correlated with the hydrophilicity of the amino acids ( see ‘Characterizing errors of the models’ section ) ., Also , Bhattacharya et al . 23 recently reported that water molecules affect the signal of DNA translocating through the nanopore since hydrophilic amino acids are more likely to acquire a water molecule and change the effective volume 24 ., Thus , it is desirable to include amino acid hydrophilicity into the model ., Motivated by these finding , we explored an alternative approach for supervised learning by using the Random Forest ( RF ) regression 25 , 26 for theoretical nanospectra generation ., In comparison to the SVR model , the resulting Random Forest ( RF ) model is more robust to outliers and exhibit less overfitting 27 , which allowed us to use the volumes of all 20 amino acids as features ., According to this RF model , each quadromer qi from the training set is converted to a feature vector fi , where each element of the vector is a pair of volume and hydrophilicity of the corresponding amino acid ., We used an open source implementation of the Random Forest regressor from Scikit-learn package 28 to build the described model ., The model performed well on the training sets , but the accuracy was poor on the test proteins with different amino acid sequence and composition ., This was mainly caused by the fact that only a few among all possible amino acid quadromers were observed in the training sets ., However , under assumption that nanopore current does not depend on the order of amino acids , it is possible to significantly expand the training sets by randomly permuting amino acids within quadromers ., Specifically , prior to model we randomly permuted each fi vector , leaving the same corresponding qi value ., This dataset expansion significantly improved the performance of the RF model on to training testing datasets ., See Fig 3 for examples of theoretical nanospectra in the MV , SVR , and RF models ., Given an experimental nanospectrum S and a protein P , we transformed S into a vector S → by splitting S into |P| + 3 regions and taking the average value inside each of them ., The vector S → was then normalized by subtracting the mean and dividing by the standard deviation ., Under the hypothesis that P has generated S → , we estimated the proportion of explained variance by computing R2 coefficient of determination between S → and the model output ., Given a database of proteins DB , a protein P ( S , DB ) is defined as a protein with the maximum R2 against S among all proteins from DB ., A pair formed by the protein P ( S , DB ) and the nanospectrum S defines a putative Protein-Nanospectrum Match ( PrNM ) ., Single protein correlation analysis indicated that proteins were correlated more with themselves on average ( Fig 5 ) ., In contrast , we did not observe such correlation in the open pore current , indicating that there is an inherent signal in blockades ., However , electrolytic current through the pore is affected by many factors , such as uncorrelated time-dependent fluctuations in the ionic current and electrical instrument noise , which results in noisy nanospectra ., Averaging multiple nanospectra from the same protein resulted in significant noise reduction and increased accuracy of PrNM identification ., This effect is similar to improvements in peptide identifications that are achieved by clustering of mass spectra in traditional proteomics 29 , 30 ., Typically , clustering of 5 − 10 nanospectra results in a consensus nanospectrum that significantly improves the signal-to-noise ratio over a single nanospectrum ( the mean Pearson correlation coefficients between theoretical and empirical nanospectra increased 1 . 5—2-fold for various datasets ) ., Since each of the existing datasets of nanospectra originated from a single pure protein , we randomly partitioned the dataset of nanospectra into clusters and performed identification of consensus nanospectra instead of a single nanospectrum ., In traditional proteomics , the precursor mass assists top-down protein identification since it greatly reduces the computational space that has to be searched in the protein database ., Likewise , information about the protein length would be very useful for SMPI , but estimating the protein length based on a nanospectrum originating from a sub-nanopore is a non-trivial problem since the existing experimental protocol does not control the translocation speed that may vary widely as evident from the blockade duration ., Our analysis revealed that protein translocations modulate the blockade current , which was captured by the measurements ., Each blockade , associated with the translocation of a protein showed a characteristic number of fluctuations during the duration of the blockade ., It turned out that the fluctuation frequency ( described below ) was correlated with the protein length and the other features , such as amino acid composition ., We explored a possibility of the separation of a sample of nanospectra into clusters corresponding to different proteins ., From a sample of different proteins , we estimated the fluctuation frequency of each nanospectrum as the number of peaks ( local maximums ) divided by the duration of the blockade ., The distribution of fluctuation frequencies ( Fig 5b ) revealed that each protein in our datasets has a characteristic peak in the distribution ., To separate the nanospectra into clusters based on the fluctuation frequency one can apply the Gaussian Mixture model to estimate the protein lengths from nanospectra and to improve the efficiency of SMPI ., Analyzing a mixture of multiple proteins is conceptually harder than analyzing the existing experimental datasets of nanospectra that all originated from pure protein solutions ., Since it is unknown what protein gives rise to what nanospectrum in a mixture , it is difficult to cluster nanospectra for a reliable identification ., Further , orientation of each molecule must be deduced prior to clustering since each protein can translocate through the pore in two different directions ., However , it is possible to cluster nanospectra based on their estimated fluctuation frequency to differentiate proteins with different lengths ., As multiple proteins may have a similar length , it is important to further split some length-based clusters into finer protein-based clusters ., We believe , that this could be done by applying clustering algorithms which automatically estimate the number of clusters ( e . g . Affinity Propagation 31 ) ., Evaluating the results of clustering in the case of complex mixtures was problematic since all available experimental datasets of nanospectra were generated from the pure protein solutions ., We benchmarked Nano-Align using nanospectra from five short human proteins: H3 . 2 , H3 . 3 , H4 , CCL5 and H3 tail peptide ( Table 1 ) ., The nanospectra from H3 . 2 , H3 . 3 and H4 were acquired using the two similar pores whereas the nanospectra for CCL5 and H3 tail were acquired using two different pores with different sizes ., The proteins were split into three pairs: ( CCL5 , H3 tail ) , ( H4 , H3 . 2 ) and ( H3 . 3 , H3 . 2 ) ., For each pair of proteins , the SVR and RF models were trained using the protein with higher number of nanospectra and the accuracy of identifications was estimated using the other protein from the pair ., The first two pairs represented proteins that were very different in both length and amino acid composition , thus minimizing the overfitting effect ., The third pair represented highly similar proteins , that only differ in four amino acids ., To evaluate the accuracy of SMPI , we constructed decoy protein database for each dataset from the correct protein and randomly generated proteins of the same length and amino acid composition as the correct protein ., The size of decoy database varied from 105 to 5 ⋅ 106 for different datasets , depending on the identification accuracy and the number of nanospectra in the dataset ., The p-value of a PrNM was approximated as the percentage of proteins from the database scoring higher than the correct protein against the given nanospectrum ., Below we show results for the SVR and RF models only since they turned out to be significantly more accurate than the MV model for all datasets ., Fig 6 shows median p-values for SVR and RF models as a function of the number of nanospectra in a cluster ., As expected , both models showed the improvement in the accuracy with the increase in the cluster size ., The p-values for the pair ( CCL5 , H3 tail ) were high for both models ( 0 . 03–0 . 05 for a consensus of size 10 ) ., However , the dataset ( H4 , H3 . 2 ) showed a significant improvement for the RF model ( p-values of the order of 10−4 for a consensus of 10 nanospectra ) , while the accuracy of the SVR model was comparable to the previous dataset ., Finally , the RF model showed high accuracy on ( H3 . 3 , H3 . 2 ) dataset , with p-values below 10−5 for the nanospectra clusters of size five ., The RF model consistently outperformed the SVR model on the datasets that were generated using pores of similar sizes , which suggests that the decision trees are better suited for SMPI due to their robustness against outliers ., Also , amino acid hydrophilicity proved to be a valuable predictor of the pore signal ., The RF model performed slightly worse than the SVR model on the dataset generated using two different pores , suggesting that it is more sensitive to the experimental conditions ., The fact that the RF model performed better on the proteins that were more similar to the training proteins is not surprising , but rather highlights the importance of choice of the training set , which should have substantial coverage of the data ., Additionally , we benchmarked the RF model performance using a database containing real human proteins ., We extracted all proteins of length between 100 and 160 from the human proteome ( about 20% of the human proteome ) and performed the identification of H3 . 3 spectra against this reduced database ., On average , the true protein was ranked five against all other proteins ( for a cluster of size five ) ., An example of database hits is given in the Table 2 ., Interestingly , all high-scoring proteins belong to H3 histone family and differ by only few amino acids ., While the search space was artificially reduced , this experiment already provides a justification for analysis of unknown nanospectra against small bacterial proteomes , after further improvements in the protein length estimation discussed above ., For each of the three models ( MV , SVR and RF ) we measured the bias with respect to different features of amino acids ., Using H3 . 2 dataset ( that provides the best amino acid coverage ) we calculated the signed error defined as the mean difference between the empirical and theoretical nanospectra ., For each amino acid , the signed error was measured among the associated quadromers ., Fig 7 shows the volume-related bias of the MV model ., This bias could be explained by the fact that larger amino acids have more influence on the pore signal than smaller amino acids ., The SVR model and RF model show no bias with respect to amino acid volumes ., A similar analysis revealed a bias with respect to amino acid hydrophilicity in the SVR model ., The MV model did not show a clear dependence , possibly due to the dominant effect of the volume bias ., The RF model showed no statistically significant bias related to hydrophilicity ., We presented the first algorithm for Single Molecule Protein Identification using a signal generated by a protein translocation through a sub-nanopore ., We also proposed three models for generating theoretical nanospectra and concluded that the Random Forest model results in the most accurate identifications ., The typical estimated p-values of identification accuracy were ranging from 10−4 to 10−6 , which is already sufficient for a limited analysis of nanospectra against small bacterial proteomes containing a few thousands proteins ., The comparison of algorithm performance on different datasets suggests that the model sensitivity will further improve when more nanospectra originated from different proteins become available ., Cysteine ( Cys ) was the highest source of error in all three models for H3 . 2 ., Likewise , Cys was an above average source of error in CCL5 12 but , it was a below average source of error in the similar sequence of CXCL1 ., Thus , it seemed unlikely that only the size affects the error ., On the other hand , both Cys and Met , which exhibit higher number of prediction errors are at the high end of the hydropathy index and have only few waters ( 4 and 10 , respectively ) binding them 32 , which may indicate that water affects the blockade current ., In addition , it has been speculated that charge could also affect the duration and magnitude of a blockade 12 , 33 ., Whereas it seems likely that both charge and water play a role in the blockade current , measurements and the MV model testing these ideas have been inconclusive so far ., While SMPI is currently not in a position to compete with top-down proteomics , this technology is still in its infancy ., Furthermore , due to the inherent single molecule sensitivity , there are several avenues of research that can be addressed uniquely by SMPI that offer protein-discrimination from very small samples ( attomoles ) ., Thus , SMPI has a potential to emerge as a new technology for accurate protein identification .

Introduction, Methods, Results, Discussion

Recent advances in top-down mass spectrometry enabled identification of intact proteins , but this technology still faces challenges ., For example , top-down mass spectrometry suffers from a lack of sensitivity since the ion counts for a single fragmentation event are often low ., In contrast , nanopore technology is exquisitely sensitive to single intact molecules , but it has only been successfully applied to DNA sequencing , so far ., Here , we explore the potential of sub-nanopores for single-molecule protein identification ( SMPI ) and describe an algorithm for identification of the electrical current blockade signal ( nanospectrum ) resulting from the translocation of a denaturated , linearly charged protein through a sub-nanopore ., The analysis of identification p-values suggests that the current technology is already sufficient for matching nanospectra against small protein databases , e . g . , protein identification in bacterial proteomes .

Protein identification is the key step in many proteomics studies ., Currently , the most popular technique for intact protein analysis is top-down mass spectrometry which recently enabled high-throughput identification of many proteins and their proteoforms ., However , this approach requires large amounts of materials and is currently limited to short proteins , typically less than 30 kDa ., On the other hand , nanopore sensors promise single molecule sensitivity in protein analysis , but an approach for the identification of a single protein from its blockade current ( nanospectrum ) has remained elusive , since the signal from the sensors relates to the amino acid sequence of the protein in a poorly understood way ., In this work we describe the first algorithm for protein identification based on nanospectra associated with translocation of proteins through pores with sub-nanometer diameters ., While identification accuracy currently does not allow reliable processing of complex protein samples yet , we believe , that the rapidly improving experimental protocols along with the new computational algorithms will transform into a viable protein identification approach in the near future .

protein transport, sequencing techniques, cell processes, dna-binding proteins, materials science, molecular biology techniques, materials by structure, research and analysis methods, amino acid analysis, proteins, biological databases, proteomics, recombinant proteins, histones, molecular biology, molecular biology assays and analysis techniques, biochemistry, proteomic databases, cell biology, proteomes, database and informatics methods, biology and life sciences, physical sciences, mixtures, protein sequencing

journal.ppat.1000762

The Disulfide Bonds in Glycoprotein E2 of Hepatitis C Virus Reveal the Tertiary Organization of the Molecule

The hepatitis C virus ( HCV ) is a major cause of chronic liver disease worldwide , leading to cirrhosis and hepatocellular carcinoma 1 ., In spite of being the focus of intense research efforts , no vaccine is available against HCV , and current therapeutic treatments have limited efficacy and significant side effects 2 ., HCV belongs to the Flaviviridae family of enveloped , positive-strand RNA viruses 3 ., Structural studies on this virus are difficult , in part because it propagates poorly in cell culture , and particles isolated from infected patients are heterogeneous and not amenable to a detailed structural characterization ., Little structural information is available on the envelope proteins , which are heavily glycosylated , display hypervariable loops , and are stabilized by numerous disulfide bridges 4 ., The folding kinetics of these proteins are slow , requiring several hours for completion of a complex process involving various ER chaperones of the infected cell 5 ., These properties make their recombinant production - in a native conformation and in sufficient amounts for structural studies - a difficult endeavor ., Yet structural information on the HCV envelope proteins would be extremely valuable , given that they carry the main antigenic determinants of the virus and play an essential role in cell entry by binding to specific receptors and inducing membrane fusion ., HCV has indeed been shown to depend on a number of cellular molecules for entry , including CD81 6 and the tight junction transmembrane proteins claudin 1 , 6 , 9 and occludin 7–9 , as well as the scavenger receptor B1 ( SR-B1 ) 10 ., The LDL receptor also plays a role in HCV uptake , in line with the observation that HCV particles in infected plasma are associated with LDL species 11 ., A direct interaction of the HCV envelope protein E2 with CD81 and SR-B1 has been demonstrated , and these interactions were shown to be necessary but not sufficient for cell entry ., The mode of interaction of HCV with the claudins and occludin is not understood at present ., The HCV genome codes for a single polyprotein precursor about 3000 amino acids long , spanning the ER membrane multiple times ., It contains , sequentially , the viral proteins in the order Nter-C-E1-E2-p7-NS2-NS3-NS4A/B-NS5A/B-Cter ., The N-terminal 1/4th of the precursor corresponds to the structural proteins C ( Core ) , E1 and E2 ( envelope proteins 1 and, 2 ) and p7 , which functions as a proton channel ., The remainder of the polyprotein contains the non-structural ( NS ) proteins , which have enzymatic and other activities that are necessary for virus replication ., The mature viral proteins are generated by proteolytic processing of the precursor by cellular and viral proteases 3 ., In particular , the envelope proteins are generated by host-cell signalases ., E1 and E2 are type 1 trans-membrane ( TM ) proteins with a large N-terminal ectodomain and almost no cytoplasmic tail ., In the best characterized HCV strain H77 , E1 and E2 are 192 and 366 amino acids long and contain 6 and 11 potential N-linked glycosylation sites , respectively ., Biochemical studies have shown that E1 and E2 fold as a heterodimer , which is found at the surface of viral particles and is thought to be the functional glycoprotein form 4 ., There are currently 6 identified HCV genotypes further divided into several subtypes 12 ., The amino acid sequence identity between envelope proteins from different genotypes is about 68% for the most distant genotypes ., E2 has been shown to contain 3 hypervariable regions that can be deleted without affecting the overall fold of the protein , as assayed by binding to conformation-sensitive mAbs and CD81 13–15 ., The genomic organization of HCV is characteristic of all members of the Flaviviridae family 3 ., In particular , the envelope proteins are present in tandem within the polyprotein precursor ., This arrangement of the structural part of the genome is characteristic of viruses encoding class II fusion proteins , reviewed in 16 ., These proteins have been extensively characterized , structurally and biochemically , for viruses in the flavivirus genus within the Flaviviridae family 17 ., Class II proteins have a common tertiary structure , which has also been observed in the fusion protein of Semliki Forest virus ( SFV ) , an alphavirus belonging to a separate family of enveloped , positive-strand RNA viruses , the Togaviridae 18 ., Togaviridae and Flaviviridae display the same gene order in the structural part of their genomes ., There is no amino acid sequence similarity in the alpha- and flavivirus fusion proteins , however , and in spite of sharing a common fold , they are stabilized by a different pattern of disulfide bonds ., Viruses within the Flaviviridae families have no sequence similarity across the various genera either , and the fusion proteins from each genus also appear to have their own characteristic pattern of disulfide bonds ., Yet the conservation of the class II fold across viral families in the absence of sequence conservation strongly suggests that it is also conserved across the different genera within the respective families ., A further feature of class II viral fusion proteins is that they fold as a heterodimer with the upstream glycoprotein in the polyprotein precursor ., This heterodimer later dissociates to drive membrane fusion upon interactions with the host cell ., The first glycoprotein in the tandem thus acts as chaperone for folding the second one , which has the membrane fusion role ., The chaperone function was experimentally demonstrated for the flavivirus prM 19 and the alphavirus p62 20 glycoproteins , which precede the fusion proteins E and E1 , respectively , in the precursor polyprotein ., The effect on folding appears to be reciprocal , since both p62 and prM also adopt their native conformation only in presence of the respective accompanying fusion protein ( unpublished observations ) ., Importantly , heterodimerization upon folding has also been characterized for viruses belonging to other genera in the two families , and in particular for HCV 5 , 21 ., Flavivirus E and alphavirus E1 change into a homotrimer upon interaction with lipids in the acidic environment of a target cell endosome , in a process that drives fusion of the viral and endosomal membrane and results in infection of the cell 22 , 23 ., This process involves homodimer ( E-E , flavivirus ) or heterodimer ( E2-E1 , alphavirus ) dissociation , followed by homotrimerization of E ( flavivirus ) or E1 ( alphavirus ) upon binding to lipids ., The tertiary structure of class II viral fusion proteins contains predominantly β-sheets segregated into three distinct domains arranged linearly , resulting in a rod-like molecule ., The central domain 1 ( DI ) is a β-sandwich with two long insertions in loops connecting adjacent β-strands ., These insertions form an elongated “fusion” domain ( DII ) , carrying the “fusion loop” in the first of the two insertions , at the distal end of the rod ., The fusion loop is a segment of the polypeptide chain that inserts into the target membrane in the first step of membrane fusion ., At its C-terminal end , DI is connected via a flexible linker to domain 3 ( DIII ) , which is located at the opposite side with respect to DII , giving rise to the linear organization of the molecule ., DIII plays an important role in the fusogenic conformational change , during which it relocates to the side of the molecule , resulting in the characteristic “hairpin” conformation of the protein , which drives membrane fusion ., This relocation involves a considerable stretching of the segment connecting DI to DIII , the region that changes most dramatically in conformation during the fusogenic transition ( reviewed in 16 ) ., Although there is no direct experimental evidence demonstrating the role of E2 as the HCV fusion protein , the compelling similarities to viruses with class II fusion proteins suggest that membrane fusion is at least one of its biological roles ., It is worth noting , however , that while totally unrelated viruses can have structurally homologous fusion proteins ( for example , rhabdoviruses , herpesviruses and baculoviruses , reviewed in 24 ) , related viruses can use non-homologous fusion proteins , as is the case with paramyxoviruses and rhabdoviruses , which belong to the Mononegavirales order ( reviewed in 25 ) ., Yet the fact that viruses belonging to different genera in the Flaviviridae and Togaviridae families display a genomic arrangement that is the signature of class II fusion proteins , together with the additional common features outlined above , makes it very likely that they code for envelope glycoproteins that are at least distantly related to class II proteins ., A model for E2 has actually been proposed based on the structure of the flavivirus E protein homodimer 26 , although no evidence is available for homodimerization of HCV E2 , which forms a heterodimer with E1 in infectious virions 4 ., More importantly , this model does not take into account the location of the strictly conserved cysteine residues forming 9 disulfide bonds 27 ., This model also lacks the third domain , which is important in the fusogenic transition ., Moreover , it was also proposed that the membrane fusion function could be carried by HCV glycoprotein E1 ( i . e . , the first glycoprotein in the tandem ) 28 , 29 , in spite of the similarities with flavi- and alphaviruses discussed above , and in the absence of experimental support ., Furthermore , a bioinformatics model for HCV E1 as a truncated class II protein was reported 30 , postulating that E1 has the fold of DII of an alpha- or flavivirus fusion protein , but neglecting the fact that in class II proteins , DII works in conjunction with the other two domains covalently linked within the polypeptide to induce membrane fusion ., The corollary is that controversial hypotheses have been reported concerning the identity of the HCV fusion protein ., It is therefore important to stress that the structural studies performed over the years on viral membrane fusion proteins strongly suggest that most animal enveloped viruses encode fusion proteins belonging to one of the three currently characterized structural classes 31 ., It is thus highly unlikely that HCV would have acquired a totally novel fusion machinery ( for instance , one in which E1 would be the membrane fusion protein ) , especially when taking into account the similarities to class II proteins presented above ., In order to bring more insight into the tertiary structure of HCV E2 , we report here the experimental identification of the connectivity of the 9 disulfide bonds present in the recombinant E2 ectodomain ( E2e ) generated by expression of the E1-E2ΔTM portion of the HCV genome in Drosophila S2 cells ( Fig . 1A ) ., The absence of the transmembrane ( TM ) segment in E2 leads to secretion of its ectodomain after folding in the presence of E1 ., This approach is based on previous results leading to production of recombinant dengue virus E protein in the presence of its viral chaperone prM 32 ., We tested the conformation of recombinant HCV E2e biochemically and functionally , showing that it reacts with conformation-sensitive antibodies and inhibits infection of Huh7 . 5 cells by infectious HCV particles ( HCVcc ) in a dose-dependent manner ., Knowledge of the disulfide bonds , along with functional data on deletion mutants 14 and CD81 binding 26 , 33 , 34 , together with secondary structure predictions , provide sufficient constraints to reconstitute the tertiary organization of the molecule ., This information allowed the threading of the E2e polypeptide chain onto a class II template by matching the predicted β-strands ., The resulting model reveals the distribution of the amino acids of HCV E2 among the different domains , maps the CD81 binding site to the DI/DIII interface , and highlights a strictly conserved segment of the polypeptide chain as a strong candidate for the HCV fusion loop ., We generated stable Drosophila S2 cell-lines expressing the E1-E2ΔTM segment of the precursor polyprotein ( Fig . 1A ) from 9 isolates spanning all 6 HCV genotypes and 4 subtypes ( Table 1 ) ., In order to ensure that the recombinant E2 proteins were functional , we selected isolates previously tested for entry of retroviral particles pseudotyped with HCV glycoproteins ( HCVpp ) with the corresponding sequences 35 ., Induction of expression at high cell density with CdCl2 resulted in accumulation of relatively high levels of secreted E2e in the cell culture medium ., We purified the protein to homogeneity from the supernatant ( described in Text S1 ) , with the yields listed in Table 1 ., E2e from the different isolates behaved similarly , as judged by size exclusion chromatography ( SEC ) followed by SDS-PAGE analysis under reducing and non-reducing conditions and Coomassie blue staining ( Fig . 1 ) ., In a typical SEC profile , the majority of the protein elutes at a volume corresponding to a monomer , with additional minor peaks corresponding to disulfide linked dimers and higher multimers , which vary depending on the construct analyzed ., The monomeric form was efficiently separated from the other species by pooling the corresponding fractions ., Analytical ultracentrifugation and small angle X-ray scattering confirmed the monomeric state of the protein eluted in these fractions ( data not shown ) ., Once isolated , E2e from all constructs listed in Table 1 remained monomeric and showed no tendency to associate into disulfide-linked aggregates over time ., The construct corresponding to the genotype 2b isolate ( UKN2b_2 . 8 ) reproducibly yielded the highest amounts of purified monomeric protein ( Table 1 ) ., The construct from genotype 4 ( UKN4_11 . 1 isolate ) yielded a significant fraction of disulfide-linked aggregates ( Fig . 1 ) , which are likely to correspond to misfolded protein ., E2e from the remaining 7 constructs yielded slightly lower yields of purified , monomeric protein than did the genotype 2b construct , the lowest yields being from the gentoype 6 isolate ( Table 1 ) ., The SEC profiles from the 7 other constructs were intermediate between the two chromatograms shown in Fig . 1 ., Because of the higher production yields , we pursued most of the biochemical characterization using E2e from genotype 2b , to which we will refer to as E2e in the rest of the manuscript , except when explicitly stated ., Yet because the best functionally characterized HCV strain is H77 ( genotype 1a ) , we use the amino acid numbering corresponding to the H77 polyprotein throughout the manuscript ., Pull-down assays showed that E2e efficiently binds the CD81 large external loop ( LEL ) , as well as conformation-sensitive mAbs CBH-4B and CBH-4D 36 ( Fig . S1A ) ., To further confirm that CD81 and the conformation-sensitive mAbs bind stoichiometrically to monomeric E2e , we used SEC to analyze the formation of various E2e/ligand complexes in defined ratios ., The resulting chromatograms display a quantitative shift of the peak from monomeric protein to an E2e/mAb complex with a 2∶1 stoichiometry , as expected ( Figs . 2A and S1B ) ., The SEC profile displayed in Fig . 2 shows the well-characterized conformation-sensitive mAb H53 that is specific for genotype 1a , whereas Fig . S1B shows the same analysis of E2e from the genotype 2b isolate and the human conformation-sensitive mAb CBH-4D ., Similarly , SEC analysis using the Fab fragment of the corresponding mAbs under the same conditions , yielded a 1∶1 E2e/Fab stoichiometry , as expected ( data not shown ) ., SEC analysis revealed that E2e also forms a stoichiometric complex with the CD81 LEL ( data not shown ) ., The monomeric fraction from all isolates listed in Table 1 yielded similar results - except perhaps for the genotype 6 isolate , which was not tested - strongly suggesting that recombinant E2e adopts a conformation closely resembling that of authentic E2 present on virions ., We further tested the ability of E2e to compete with infectious HCV particles for entry receptors , by measuring its ability to inhibit infection of Huh-7 . 5 cells by HCVcc ( Fig . 2B ) ., As a control , we tested in parallel the effect of the flavivirus E protein ectodomain ( sE ) from West Nile encephalitis virus ( WNV ) , as well as the ectodomain of pestivirus E2 ( pE2e ) from the bovine viral diarrhea virus ( BVDV ) produced under identical conditions ., In contrast to the control proteins , HCV E2e exerted a clear dose-dependent inhibition of the infection ., At the lowest concentration tested ( 0 . 05 µM ) , 10% inhibition was observed , which increased with protein concentration to reach 90% inhibition at 2 µM of HCV E2e ., This effect is in line with the observation that E2e makes a stoichiometric complex with CD81 , as described above ., Computer algorithms for secondary structure prediction using amino acid alignments of E2 from all 6 HCV genotypes predict predominantly β-strands in E2e ( Fig . 3 ) , consistent with the fold of class II fusion proteins ., We used recombinant E2e to experimentally analyze its secondary structure composition with two complementary methodologies , circular dichroism ( CD ) , which is sensitive to the presence of α-helices , and Fourier transform infrared ( FTIR ) spectroscopy , which readily detects β-sheets present in a protein ., We carried out these tests in parallel with recombinant control class II envelope proteins of known structure available in the laboratory ., For the CD measurements , the controls were WNV sE , ( 37 , PDB 2I69 ) and the ectodomain of glycoprotein E1 ( sE1 ) of Chikungunya virus ( CHIKV ) , which displays 62 . 5% amino acid sequence identity with the SFV E1 ectodomain , the crystal structure of which is known ( 18 , PDB 2Ala ) ., Unexpectedly , the far-UV spectra of the three proteins exhibited considerable differences ( Fig . 4A ) , the spectrum of HCV E2e being in agreement with a previous study 38 ., However , deconvolution to retrieve the percentage of the various secondary-structure elements suggests similar ratios for all three proteins , indicating , in particular , only about 5% α-helices in all three proteins ., The strong minimum observed at 203 nm in the spectrum of HCV E2e suggests the presence of natively unfolded regions that are absent in the control proteins ., Given that circular dichroism is not the most sensitive method to determine the amount of β-sheet in a protein , we used FTIR spectrometry in a comparative analysis of E2e with a class II protein of known 3D structure ., Because the WNV sE was not available at the time of the experiment , we used instead sE from dengue virus serotype 3 ( DV3 ) , for which the crystal structure is also known ( PDB entry 1UZG 39 ) ., The high-frequency region of the FTIR spectra of HCV E2e and DV3 sE is displayed in Fig . 4B ., As expected , both proteins have their absorption maxima in the amide I band at 1637 and 1640 cm−1 , respectively , close to the 1630 cm−1 value typical for β-sheet containing polypeptides ( reviewed in 40 ) , in agreement with the structure of the flavivirus sE and strongly indicating that HCV E2e also contains predominantly β-sheets ., In order to obtain a quantitative measure of the β-sheet content of E2e , we performed a further analysis to more precisely compare the secondary structure content of the two proteins by computing a difference spectrum after normalization to an identical area under the amide I band ., The DV3/sE – HCV/E2e difference spectrum showed a positive peak at 1630 cm−1 , as well as a broad negative region ranging from 1645 to 1680 cm−1 ( Fig . 4B ) ., The value of the positive peak indicated about 14% higher β-sheet content for DV3 sE , which , when using the value of 42% β-sheet estimated from the DV3 sE crystal structure , gives about 28% β-sheet for HCV E2e ., The negative area of the difference FTIR spectrum indicates that the HCV E2e polypeptide displays higher relative amounts of secondary structure other than β-pleated sheet ( random coil , β-turns , 3/10 helices , etc . ) ., This difference is likely to reflect the presence of the regions that give rise to the strong minimum at 203 nm in the CD spectrum ( Fig . 4A ) , i . e . , natively unfolded segments of the polypeptide chain ., We determined the identity of the disulfide bridges by N-terminal sequencing together with comparative reducing/non-reducing mass spectrometry analyses of peptides obtained by trypsin digestion of E2e ., For this purpose we selected E2e of three isolates , UKN2b_2 . 8 , H77 and JFH-1 ( genotypes , 2b , 1a and 2a , respectively ) , which display amino acid sequences with a different pattern of predicted trypsin cleavage sites ( Fig . S2 ) ., We fully deglycosylated the protein with PNGase F under denaturing conditions , then digested it with trypsin followed by separation of the resulting peptides by HPLC under reducing or non-reducing conditions ., Comparison of the HPLC elution profiles enabled the identification of peaks that were affected by reduction with TCEP ( asterisks in Fig . 5A ) ., We analyzed the samples in these peaks by surface-enhanced laser desorption/ionization ( SELDI ) with a time-of-flight ( TOF ) spectrometer ( Table S1 ) , and identified their N-terminal sequence by Edman degradation ( Fig . S3 ) ., This procedure allowed the unambiguous experimental identification of 8 out of the 9 disulfide bonds present in the protein , thereby also identifying the 9th by exclusion ( Table 2 ) ., This table also shows that 5 disulfides were independently identified in at least two different strains , validating the procedure ., A full account of the experiments made to determine the disulfide connectivity is provided as Supplementary Information ( Text S1 ) ., The connectivity of the disulfide bonds provides key information on distant segments of the E2 polypeptide chain that come near each other in the folded protein ., This knowledge can be used in conjunction with other available data to get a better picture of the tertiary structure of the protein , namely:, i ) the observation that E2e is rich in β-sheet and that secondary structure predictions suggest regions with consensus β-strands along its amino acid sequence;, ii ) the identity of residues that are far apart in primary structure and that are known to be part of the CD81 binding site;, iii ) the postulate that E2 is the HCV fusion protein and therefore has a characteristic 3-domain class II fold , in agreement with the organization of its precursor polyprotein , which also implies, iv ) that the third domain ( DIII ) should be connected to DI via a linker that can extend to stabilize a post-fusion trimer ., Finally , DIII should be followed by a flexible “stem” region - the presence of which has already been reported for HCV E2 41 - connecting to the TM segment ., Further information comes from the identification of “hypervariable” regions in HCV E2 that can be deleted without affecting the reactivity of the resulting deletion mutant with conformation-sensitive mAbs and with CD81 14 ., In addition , numerous reports have shown that the E2 ectodomain truncated at position 661 , which is in the loop closed by disulfide 9 ( Table 2 ) , also reacts with conformation-sensitive mAbs and CD81 38 , suggesting that the downstream segment is not part of the structured ectodomain ., About one third of the E2e residues are predicted to form β-strands ( Fig . 3 ) , which is in overall agreement with the estimated 28% β-sheet content determined by FTIR spectroscopy ., The pattern of predicted β-strands offers the possibility of threading the polypeptide chain along the template provided by the known fold of class II proteins , while simultaneously respecting all of the known constraints derived for HCV E2 by the functional studies discussed above ., A useful guide for this analysis is the comparison between predicted and experimentally observed β-strands in the crystal structure of alpha- and flavivirus fusion proteins - for instance , in the alphavirus E1 alignment provided in Fig . 3B ., The hallmark of the tertiary structure of class II proteins is the presence of an 8-stranded ( B0 through I0 ) central domain ( or DI ) folded as a β-sandwich with up-and-down topology ( Fig . 6 ) ., Two insertions in this domain , in the D0E0 and H0I0 loops , constitute the fusion domain bearing the fusion loop in the distal part of the D0E0 insertion ., DI is followed , after strand I0 , by a flexible segment connecting to a third domain ( DIII ) , the relocation of which is important for hairpin formation during the fusogenic conformational rearrangement of class II fusion proteins ., Functional studies have shown that deletion of the HVR1 region did not induce a loss of virus infectivity in experimentally infected chimpanzees 42 , indicating that this segment cannot be part of a folded domain ., We therefore began the threading process by assigning the 3 consecutive β-strands predicted immediately downstream of the HVR1 ( Fig . 3 , first three red boxes ) to the three conserved strands in the N-terminal part of DI , i . e . B0 , C0 and D0 ., These β-strands are followed by a long intervening region that is compatible with the D0E0 insertion of the class II fold ., For the assignment of strands E0 and F0 , the available data on the residues involved in CD81 binding ( small blue circles in Fig . 3 ) provide valuable information , since strand E0 must interact with D0 ( see diagram in Fig . 6B ) ., Thus , assigning E0 and F0 to the two consecutive strands predicted after residue 525 brings together a patch of residues that are apart in primary structure to the same face of DI , forming the site of interaction with CD81 ( Fig . 6 ) ., For the assignment of the remaining β-strands , there is crucial information provided by disulfide 1 ., This disulfide bond connects Cys429 , at the end of strand C0 , with Cys552 further downstream , which therefore must be at the same end of the DI β-sandwich ., This means that Cys552 must be located either at the G0H0 loop , or at the end of the I0 strand , if the molecule is to have a class II fold ( Fig . 6B ) ., However , after strand F0 , there is a long strand predicted to span residues 549–555 ( Fig . 3 ) , which would have Cys552 in the middle ., But the comparison of predicted versus experimentally determined β-strands of alphavirus E1 shows that , for several alphaviruses , the region of G0 and H0 is also predicted as a single long strand ( Fig . 3B ) ., Indeed , in both alphaviruses and HCV , there is a glycine residue ( Gly 551 in E2e ) forming a tight turn that reverses the chain orientation , going from G0 into H0 ( some alphaviruses have two glycines at this β-turn ) ., This shows that the prediction algorithms are not 100% reliable , suggesting that in HCV E2 , Gly551 is at the G0H0 turn , and that Cys552 is the first residue of strand H0 ., Indeed , running at the edge of the DI β-sandwich , the sequence of G0 ( as well as the sequence of the alphavirus B0 strand , at the other end of the bottom β-sheet , Fig . 3B ) appears to be less typical than the sequences of internal β-strands in a β-sheet , which are easier to predict by computer algorithms ., In addition , the short connections between strands F0 through H0 in both alpha- and flavivirus DI are also consistent with the assignment of H0 to a strand running between residues 552 and 555 in HCV E2 ., Having assigned the G0 and H0 strands , additional considerations are necessary to assign strand I0 ., In alpha- and flaviviruses , I0 is one of the two central β-strands of the bottom sheet of DI , and is directly followed by the linker connecting to DIII ., Because it is the only strand missing to complete the 8-stranded β-sandwich , it can only make disulfide bonds to cysteines located upstream in primary sequence ., In HCV , three β-strands are predicted directly downstream to the assigned H0 strand: one around residue 563 , one around 573 , and one around 593 ( Fig . 3 ) ., The strand around residue 573 is part of a segment that can be deleted without affecting protein conformation 14 , indicating that it cannot be I0 ., The strand around 593 ends at Cys597 , which forms disulfide 7 with Cys620 further downstream ., Because class II proteins can have no interdomain disulfides - which would be incompatible with their function - this strand cannot be assigned to I0 either ., Indeed , the interleaved nature of disulfide bonds 7 and 8 dictates that none of the strands predicted downstream can be in DI ., The only option compatible with a class II fold is , therefore , to assign the strand around residue 563 to I0 ., This assignment implies that the long insertion in the H0I0 loop of the alpha- and flavivirus fusion proteins is absent in HCV E2 ., This is in line with E2 from HCV and pestiviruses being shorter than the alpha- and flavivirus fusion proteins by about 80–110 amino acids , i . e . , roughly the length of the insertion in the H0I0 loop of the latter ., The assignment of the 8 strands in HCV DI also indicates that the linker connecting DI and DIII must be between disulfides 5 and 6 , encompassing the region called IgVR ( “intergenotypic variable region” ) , which can be deleted without affecting protein conformation , at least in the prefusion form of E2 ., As discussed above , the segment containing disulfide 9 is likely not to be part of the structured ectodomain , further implying that DIII is comprised between disulfides 6 and 9 , spanning about 70 amino acids ., The presence of two long-range disulfide bonds ( disulfides 7 and 8 ) suggests that this region is indeed structured into a separate domain ., However , the secondary structure predictions point to only 3 β-strands in this domain , indicating that the Ig-like fold of DIII in alpha- and flaviviruses may not have been maintained in HCV ., Moreover , we found no obvious way to propose an Ig-like arrangement of the polypeptide chain in this domain such that it would also satisfy the constraints imposed by disulfides 7 and 8 ., The resulting model for the tertiary structure of E2 is presented in Fig . 6A , with the diagram of Fig . 6B higlighting , as a guide , the essential features of the resulting “class II” organization of the protein ., The main features of the molecule are the following: This domain has an N-terminal extension in flaviviruses ( which includes β-strand A0 ) with respect to alphaviruses ( see review by 16 ) , and in HCV , the HVR1 also appears to be an N-terminal extension ., DI contains disulfides 1 and 5 , both at the DII distal end of the DI β-sandwich; i . e . , at its DIII interacting end ., Disulfide 5 connects two consecutive cysteines into a short loop at the end of strand I0 ., The C0D0E0F0 β-sheet ( or “top” sheet ) contains most of the determinants of CD81 binding ( blue circles in Fig . 6A ) , and 5 of the 11 N-linked glycosylation sites of E2 ( numbered 1 , 2 , 3 , 6 and 7 , Figs . 3 and 6A ) ., In contrast , the B0I0H0G0 β-sheet ( or “bottom” sheet ) has only site 8 ( Asn 556 , Fig . 6A ) , located in the H0I0 loop , at the site of the long insertion in alpha- and flavivirus fusion proteins ( yellow dotted line , Fig . 6B ) ., The presence of an insertion in the other class II proteins suggests that there is space at this end of the barrel for a glycan chain attached to Asn556 ., Importantly , glycan 8 was shown to be essential for the correct folding of E2 , in line with the key location in the H0I0 loop in the bottom sheet ., Overall , the distribution of glycans on HCV DI is compatible with the experimentally determined orientation of flavivirus E and alphavirus E1 at the virion surface , with the bottom sheet facing the viral membrane ., This pattern provides additional evidence validating our assignment of the DI β-strands ., This domain has two predicted glycosylation sites and three disulfide bonds ( 2 , 3 and 4 ) , all connecting consecutive cysteine residues very close in primary structure ., In the alpha- and flavivirus counterparts , the two

Introduction, Results/Discussion, Materials and Methods

Hepatitis C virus ( HCV ) , a major cause of chronic liver disease in humans , is the focus of intense research efforts worldwide ., Yet structural data on the viral envelope glycoproteins E1 and E2 are scarce , in spite of their essential role in the viral life cycle ., To obtain more information , we developed an efficient production system of recombinant E2 ectodomain ( E2e ) , truncated immediately upstream its trans-membrane ( TM ) region , using Drosophila melanogaster cells ., This system yields a majority of monomeric protein , which can be readily separated chromatographically from contaminating disulfide-linked aggregates ., The isolated monomeric E2e reacts with a number of conformation-sensitive monoclonal antibodies , binds the soluble CD81 large external loop and efficiently inhibits infection of Huh7 . 5 cells by infectious HCV particles ( HCVcc ) in a dose-dependent manner , suggesting that it adopts a native conformation ., These properties of E2e led us to experimentally determine the connectivity of its 9 disulfide bonds , which are strictly conserved across HCV genotypes ., Furthermore , circular dichroism combined with infrared spectroscopy analyses revealed the secondary structure contents of E2e , indicating in particular about 28% β-sheet , in agreement with the consensus secondary structure predictions ., The disulfide connectivity pattern , together with data on the CD81 binding site and reported E2 deletion mutants , enabled the threading of the E2e polypeptide chain onto the structural template of class II fusion proteins of related flavi- and alphaviruses ., The resulting model of the tertiary organization of E2 gives key information on the antigenicity determinants of the virus , maps the receptor binding site to the interface of domains I and III , and provides insight into the nature of a putative fusogenic conformational change .

Little is known about the structure of the envelope glycoproteins of the hepatitis C virus ( HCV ) , in spite of their essential role in the viral cycle of this major human pathogen ., Here , we determined the connectivity of the 9 disulfide bonds formed by the strictly conserved 18 cysteines of the ectodomain of HCV glycoprotein E2 ., We show that this information , together with important functional data available in the literature , impose important restrictions to the possible three-dimensional fold of the molecule ., Indeed , these constraints allow the unambiguous threading of the predicted secondary structure elements along the polypeptide chain onto the template provided by the crystal structures of related flavi- and alphavirus class II fusion proteins ., The resulting model of the tertiary organization of E2 shows the amino acid distribution among the characteristic class II domains , places the CD81 binding site at the interface of domains I and III , and highlights the location of a candidate fusion loop .

biochemistry/molecular evolution, virology/virion structure, assembly, and egress, biochemistry/protein folding, virology, virology/host invasion and cell entry

journal.ppat.1006703

Whole genome sequencing of extreme phenotypes identifies variants in CD101 and UBE2V1 associated with increased risk of sexually acquired HIV-1

The discovery of the protective deletion variant , CCR5-delta32 , in the chemokine receptor 5 gene , encoding a HIV-1 co-receptor 1–3 , generated great enthusiasm to search for additional host genetic variants and pathways associated with HIV-1 acquisition as a means of identifying targets for new HIV-1 prevention and treatment strategies ., This enthusiasm was further enhanced by documentation of HIV-1 exposed seronegative ( HESN ) individuals who had very high exposure and lacked CCR5-delta32 , 4–7 suggesting existence of additional genetic factors that alter the risk of sexually transmitted HIV-1 infection ( Online Mendelian Inheritance in Man OMIM phenotype #609423 ) 8 ., At least one in vitro experiment supports the hypothesis of a strong genetic component to infection risk , reporting 50% heritability in cellular susceptibility to HIV-1 infection 9 ., Nevertheless , genome-wide association studies ( GWAS ) to date searching for such genetic risk factors for HIV-1 infection risk have met with limited success 10–17 ., Most GWAS have had moderate ( ~80% ) average power to detect common variants , very low power to detect variants with minor allele frequency ( MAF ) = 5% ( approximately 1% power for an OR = 2 ) and even less power to detect associated rare variants ( RVs ) ( MAF≤1% ) ., Additionally , susceptibility to HIV-1 can only be assessed among individuals who are exposed to the virus ., While some assessment of HIV-1 exposure was used for most HIV-1 GWAS 10–17 , exposure measurement error and/or exposure misclassification , including that related to lack of information about the HIV-1 infected partners’ plasma HIV-1 RNA level ( s ) , can result in lower statistical power than anticipated ., Furthermore , out of necessity , some studies have been forced to attempt replication across different ancestral/racial groups 10 ., However , risk variants might differ between such groups , thereby lessening the power for replication ., Hence , major gaps in HIV-1 genetic association studies still exist , and focus on power to detect rare associated variants employing high accuracy in HIV-1 exposure measurements is warranted ., The contribution of rare variants to risk of HIV-1 acquisition is of particular interest because effect size is generally inversely correlated with MAF when an association does exist 18 and large effects can be expected to translate to strong interventional impact ( e . g . , HMG-CoA reductase inhibitors and familial hypercholesterolemia 19 ) ., With these issues in mind , we undertook an association study of HIV-1 acquisition using whole genome sequencing ( WGS ) of extreme phenotypes sampled from two large clinical trials and one observational study of African HIV-1 serodiscordant couples ( stable heterosexual couples with one partner HIV-1-infected and the other partner HIV-1-seronegative at enrollment ) ( n = 8 , 593 couples ) ., These studies ( S1 Table ) had similar clinical follow-up , including quarterly risk assessments , PCR-verification of HIV-1 infection , reports of protected and unprotected sexual activity from both partners , measurement of the infected partner’s plasma HIV-1 RNA level and molecular confirmation of transmission linkage through viral sequencing 20–22 ., The 100 Discovery stage genomes were sequenced by Complete Genomics Inc . ( CGI ) with high quality results ( S2 Table ) ., The RVT1 test ( “rare variant test 1” ) 24 , a statistical test designed specifically for rare variant association studies , was used to test the difference between extremes in functional variant burden ( defined as the total number of minor alleles ( aggregated variant scores ) by gene comparing cases and controls; see Materials and methods ) for each of 18 , 354 genic regions , including 284 , 632 functional variants in the tests ., The regions with the two lowest RVT1 p-values ( S2A Fig ) had an estimated 83% probability that at least one of these was a true positive , based on a False Discovery Rate ( FDR ) analysis 25 ., QQ-plots of the RVT1 results showed good adherence to expected behavior and no evidence of confounding by major ancestry nor by spatially-isolated pockets of ancestry 26 ( S2B and S2C Fig ) ., These two regions are transcribed regions of CD101 ( NCBI Gene ID: 9398 ) and UBE2V1 ( NCBI Gene ID: 7335 ) ., For both of these genes , individuals with greater numbers of polymorphic functional sites had increased risk of HIV-1 acquisition: CD101 odds ratio ( OR ) = 2 . 7 ( per functional site with at least one minor allele ) , 95% CI = 1 . 6–4 . 8 , p = 3 . 6x10-5 , and UBE2V1 OR = 3 . 7 , 95% CI = 1 . 8–7 . 5 , p = 4 . 7x10-5 ( Table 2 ) ., Within these two genes in the Discovery stage , eight variants were novel at the time of identification , and all eight validated by Sanger sequencing ( S3 Table ) ., Based on the FDR results showing >80% chance of a true positive , the CD101 and UBE2V1 regions were moved forward to the Replication stage for testing in a longitudinal analysis ., Test regions from the Discovery stage almost certainly include variants significantly associated , as well as , variants not significantly associated ( i . e . , noise ) with outcome ., The benefits , and perhaps even necessity , of using biological knowledge to increase signal-to-noise ratio for rare variant replication is well recognized 27 , 28 ., To increase the signal-to-noise ratio and increase power , we prioritized and grouped variants ( see Materials and methods ) in CD101 and UBE2V1 for Replication stage testing ., Fourteen functional variants in CD101 were designated as “primary replication variants” ( PRVs ) based on a direction of effect consistent with that for the Discovery result ( S4 Table ) and their degree of significance in by-variant tests ( S3A Fig ) ., These variants were subdivided into four sub-groups for replication testing ( see Materials and methods ) : ( 1 ) five missense variants in regions encoding extracellular CD101 immunoglobulin-like ( Ig-like ) protein domains 29 , ( 2 ) five missense variants in the CD101 cytoplasmic domain , ( 3 ) two 3’-UTR variants and ( 4 ) two splice site variants ( Fig 1A , S3A Fig , S4 Table ) ., Creating four separate a priori replication test groups increases the multiple-testing penalty , but is expected to further increase the signal-to-noise ratio within some of these variant test groups ., Similarly , 11 of 15 predicted functional variants in UBE2V1 were designated as PRVs , and these were divided into two groups: ( 1 ) six 5’-UTR and ( 2 ) five 3’-UTR variants ( Fig 1B , S3B Fig , S5 Table ) ., One of the 5’-UTR variants , rs6095771 , is identified as a missense variant in the canonical transcript , and as a 5’-UTR for other transcripts ., It was grouped with the 5’-UTR variants due to low predicted power to replicate a single variant with MAF = 0 . 02 ., Three of these variants were novel and another six are not found among Kenyans in the 1kG database 30 ( S5 Table ) ., Furthermore , two of the functional UBE2V1 Discovery variants that are indexed in the ESP 31 or ExAC 32 databases are extremely rare and found in ESP only among individuals of African descent ( rs187204768 , MAF = 4/4046 and rs6095771 , MAF = 20/4400 ) ( S5 Table ) ., These results indicate that the UBE2V1 association could not be detected at the variant level using GWA-type methods and can only be detected in a population with substantial recent African ancestry , such as the present study ., Across these three HIV-1 serodiscordant couples cohorts , the pool of participants who were available for the Replication stage had varying amounts of reported sexual exposure ., Because inclusion of individuals with no or little HIV-1 exposure could reduce power , we identified and excluded these individuals using a Protected-sex Index ( PI ) ., PI is defined as the proportion of study visits for which only abstinence or 100% condom use was reported; we considered this our measure of baseline behavior tendency ., PI predicted overall HIV-1 seroconversion rates among the three HIV-1 cohorts ( Fig 2 ) , indicating reasonably low measurement error and a high signal-to-noise ratio for PI ., Simulation studies predicted that power would be maximized when only individuals with PI ≤0 . 6 were included in the Replication analysis , despite a larger sample size when individuals with lower exposure are included ( see Materials and methods ) ., Accordingly , the Replication cohort was restricted to the 261 individuals HIV-1 uninfected at baseline , who each had PI ≤ 0 . 6 and were not selected for the Discovery stage analysis ., Genotyping of variants in CD101 and UBE2V1 for the 261 Replication stage individuals was completed using molecular inversion probe sequencing ( MIPs ) 33 technology ( see Materials and methods ) ., Given the cost efficiency of using MIPs , an auxiliary sample of 968 participants selected by more common methods rather than sexual exposure also was sequenced and used for verification of simulated power studies ( see Materials and methods ) for a total of 1229 individuals with MIPs data for CD101 and UBE2V1 ( S6 Table ) ., Neither CD101 splice variant from the Discovery stage was found in the Replication sample ( S7A Table ) , reducing the number of CD101 primary test groups from four to three ., In total , 83 CD101 SNVs were detected among the 1229 individuals ( S8 Table ) ., For UBE2V1 , three of five 3’-UTR PRVs were found in the Replication sample but only one of the six 5’-UTR PRVs was present ( S7B & S9 Tables ) ., This is consistent both with the rarity of these UBE2V1 variants and enrichment for these variants in the Discovery sample ., To test for further evidence of a true positive association between HIV-1 acquisition risk in women and Ig-like CD101 variants or UBE2V1 rs6095771 , we augmented the Replication data with variant data from the auxiliary sample and tested for an interaction between the reported PI score and each PRV score to ascertain whether a dose-response relationship was present ., “Dose” here is HIV-1 virus exposure quantified in terms of frequency of unprotected sex with a partner with the average plasma HIV-1 RNA in the Replication sample partners ., For UBE2V1 rs6095771 , this increased the number of women with 5’-UTR PRVs in the analysis from 4 to 28 ( S7B Table ) ., We found strong dose-response relationships for both genes , indicating that the association between variant scores and HIV-1 risk is positively related to the frequency of unprotected sex: p = 5 . 0x10-8 for the CD101 Ig-like dose-response and p = 8 . 2x10-6 for the UBE2V1 rs6095771 dose-response , with exposure assessed through the PI score and modeled on a log-scale ( Fig 4; S7 Fig ) ., Because the model is adjusted for PI , these significant associations with increasing dose are in addition to the effect of decreasing PI ., Overall model significance levels including the variant and dose-response variables were marked at p = 5 . 1x10-12 and p = 1 . 7x10-12 , respectively , for CD101 and UBE2V1 in explaining variation in HIV-1 seroconversion risk ( Tables 4 and 5 ) ., The interaction models also provide estimated HRs for the risk groups under the assumptions of no protection and the average frequency of heterosexual intercourse among those in the Replication Stage ( CD101 HR = 5 . 4 , p = 1 . 4x10-3 , 95% CI = 1 . 9 , 15 . 2; and UBE2V1 HR = 16 . 2 , p = 2 . 8x10-4 , 95% CI = 3 . 6 , 72 . 6 ) for the Ig-like PRV score and UBE2V1 rs6095771 , respectively ., Again , these HRs are adjusted for PI , which means that the increase in effect size is above that explained by the PI variable ., When we included all 1229 samples genotyped after the Discovery stage ( Replication sample and auxiliary sample ) in a model that does not account for sexual exposure/PI we found highly attenuated HRs ( Tables 4 and 5 ) and non-significant p-values ., This phenomenon occurs because the estimated HRs are averages over a group that contains individuals who have no risk due to no sexual exposure , “diluting” the effect we detected in those with higher exposure ., To illustrate these effects as a function of the exposure to HIV-1 viral quantity , HRs were estimated with step-wise decreases in sample size , excluding exposed individuals at each step ( Fig 4 , S6 & S7 Figs ) ., Levels of cytokines in blood are a useful measure for immunologic function and may indicate presence of a generalized host pro-inflammatory state 36 ., We evaluated whether the three CD101 Ig-like PRVs with FDR < 0 . 05 in the replication stage were also associated with altered plasma cytokine levels compared to individuals without any of these variants ( see Materials and methods ) ., Among the 163 individuals in this subset for whom CD101 genotypes were determined and for whom measurements of 25 plasma cytokines were available 37 , carriers of these CD101 risk alleles had significantly lower serum levels of IL1R1 ( right-shifted distribution ) compared to those without any CD101 risk alleles ( OR = 0 . 19 for achieving the 75th percentile IL1R1 value , 95% CI = 0 . 07 , 0 . 54 , p = 1 . 7x10-3; adjusted p = 0 . 04 ) ( S8 and S9 Figs ) ., There was also a tendency toward lower levels of sCD40L ( p = 0 . 0049; S10 Table ) ., Frequencies of the UBE2V1 5’-UTR PRVs were too low in the individuals with cytokine measurements to allow for assessment of any association with cytokine levels ., None of these variants were associated with HIV-1 plasma RNA set point among HIV-1 seroconverters ( S11 Table ) , indicating that the associations we have found are unlikely to act through altered viral replication with these gene products functioning as intracellular viral restriction factors 38 ., Our findings demonstrate that aggregates of host genetic variants , including variants with MAF<10% , can have a strong and replicated association with HIV-1 acquisition risk ., Our replication of CD101 Ig-like variants showed an HR for HIV-1 infection of 4 . 3 ( 95% CI = 2 . 1–8 . 9 , p = 6 . 4x10-5 ) ; and replication of association of UBE2V1 rs6095771 with HIV-1 acquisition risk showed an HR of 6 . 4 in women ( 95% CI = 2 . 1 , 19 . 1 , p = 9 . 5x10-4 ) ., Two CD101 missense/regulatory variants reached individual statistical significance after adjustment for multiple testing , four had individual FDRs < 0 . 05 , and a strong dose-response relationship with the frequency of unprotected sex collectively strengthened evidence that association with HIV-1 acquisition is real ., While many of these variants were individually rare or infrequent , nearly 18% of Kenyans evaluated in the 1K Genomes Project 30 had one or more of the three most common CD101 Ig-like HIV-1 risk variants ( rs17235773 , rs3754112 , rs12093834 ) ., Hence , these variants or others in CD101 and UBE2V1 could have a substantial impact on the population-based HIV-1 infection risk ., CD101 and UBE2V1 are both biologically plausible candidates for influencing HIV-1 acquisition ., CD101 is expressed on CD4+ and CD8+ T-cells , dendritic cells and monocytes , 39 and appears to alter CD4+/CD25+/FOXP3+ T regulatory cell ( Treg ) function based on both a murine graft-versus-host disease model 40 , and through IL10 secretion from human dendritic cells 41 ., Monoclonal antibody ligation of CD101 reduces T cell proliferation through a Ca2+ and tyrosine kinase-dependent pathway possibly by preventing translocation of nuclear factor of activated T cells ( NFAT ) and IL-2 production 42 ., Recent experiments using a murine model of chronic colitis demonstrate that adoptive transfer of CD101-/- Tregs is associated with Th17 cell proliferation and more severe colitis 43 ., CD101 expression is also strongly associated with the immune suppression function of Tregs in humans ., 44 Reduced expression of CD101 on mucosal CD8+ T cells has been associated with increased tissue inflammation in studies of human intestinal 45 , and pulmonary mucosa ., 46 Given that local inflammation and CD4+ 47 and CD8+ 48 , 49 T cell immune activation have been associated with increased HIV-1 acquisition risk , and reduced immune activation 50 , 51 or immune quiescence 52 , 53 have been associated with natural resistance to HIV-1 in HESN , our results support the idea that CD101 gene variants may modify HIV-1 heterosexual acquisition risk through altered levels of genital mucosal inflammation ., The finding that CD101 risk variants are associated with lower plasma IL1R1 levels , but not with HIV-1 RNA set point , suggests that these variants have systemic immunological effects in the seronegative partner while not directly acting on HIV-1 replication ., Recent studies in mouse models indicate that IL1 may inhibit Treg and enhance Th17 differentiation 54 ., However , it is unclear at this point how variants in CD101 , specifically those identified in CD101 Ig-like domains , might modify either IL1 or IL1R1 levels ., The IL1R1 rs2234650 genotype has been reported to be associated with HIV-1 acquisition in infants of HIV-1 infected mothers with modification of risk by IL1 gene family haplotypes 55 ., In addition to CD101 being associated with increased Treg function , IL1R1 expression has been associated with increased Treg and anti-inflammatory IL10 secretion 44 ., Combining these prior data with our findings , we hypothesize that CD101 Ig-like variants reduce Treg function with associated reductions in IL1R1 levels and an enhanced pro-inflammatory environment , which leads to increased risk of HIV-1 acquisition ., While efforts to test this hypothesis and to develop a more detailed understanding of CD101 function are underway , our results suggest that targeting CD101 activity could be a novel approach to host-directed , HIV-1 prevention ., UBE2V1 associates with TRIM5-α , a host restriction factor involved with HIV-1 capsid uncoating 56; however , rare and uncommon UBE2V1 variants have not previously been studied in association with HIV-1 acquisition risk ., Previous GWAS without rare variant burden/aggregation tests cannot adequately assess the UBE2V1 association observed here because of the rarity of the variants found to be associated with HIV-1 acquisition risk in this study ., UBE2V1 forms an ubiquitin-conjugating complex generating unattached polyubiquitin chains that may stimulate NF-κB activation and consequent pro-inflammatory cytokine production 56 , 57 ., This complements reports of reduced systemic immune activation associated with resistance to HIV-1 acquisition in Kenyan sex workers 50 , 53 , 58 ., Of note , UBE2V1 is one of only 23 genes differentially down-regulated by HIV-1 trans-activator of transcription ( TAT ) , an HIV-1 protein that is required for efficient replication of the HIV-1 virus and potential escape from the host immune system 59 ., Distinguishing between a group carrying variants protecting against HIV-1 acquisition versus a group carrying risk-increasing variants requires that both groups are exposed to HIV-1 , i . e . to assess susceptibility to HIV-1 in any given individual , that individual must be exposed to HIV-1 ., This translates directly to a mathematical proof that statistical power to detect a significant association with HIV-1 acquisition risk is increased by selectively identifying individuals with sustained high levels of HIV-1 exposure ., Indeed , the success of this study was dependent on our ability to quantify HIV-1 exposure with relatively high accuracy to identify exposed individuals ., Given that the overall per-contact probability of heterosexual HIV-1 transmission is intrinsically low ( estimated at ~1/1000 for vaginal intercourse among the study population ) 60 , our findings suggest that inclusion of individuals with lesser HIV-1 exposure may explain , in part , why it has been difficult to identify and/or validate genetic risk factors for HIV-1 acquisition ., The use of the extreme phenotypes design is another strength of this study ., The discovery of CCR5-delta32 was based on the observation that extreme resistance phenotypes existed 61 , 62 leading to case-control studies to identify the CCR5-delta32 variant among candidate genes 1–3 , including extreme hemophiliac controls 1 and the others employing just two to four extreme resistant controls 2 , 3 ., Identifying individuals with phenotypes that represent the extremes of risk of HIV-1 acquisition has been challenging because few studies are able to assess both the behavioral and biologic dimensions ( e . g . frequency of unprotected sex , and plasma HIV-1 RNA level in the HIV-1 transmitting partner ) that contribute to exposure ., Our use of data from both partners added accuracy to the phenotypes we used in this study—e . g . , plasma HIV-1 RNA level contributed by the HIV-1 infected partner , reported frequency of unprotected sex from both partners , and epidemiologic data from the HIV-1 uninfected partner ( e . g . , male circumcision status ) 23 ., Further improvements in accurate exposure quantification could increase power even more and perhaps very extreme phenotypes can be identified that provide superb power with small samples but lead to generalizable treatments ., Recent examples that underscore the value of the extreme phenotype analysis approach include discovery using a sample size of a dozen individuals of a human antibody to a malaria protein that prevents death and provides a promising new malaria vaccine target 63 , and discovery of the protective effect of PKC9 loss of function variants against cardiovascular disease in a small group of extreme individuals 64 , leading to the development of PKC9 inhibitors for lowering LDL cholesterol ., Our analysis identified variants in CD101 and UBE2V1 associated with increased risk of HIV-1 acquisition , but we did not identify variants associated with reduced risk of HIV-1 acquisition ., Statistical power is lower to identify protective variants than it is to identify risk variants ( of the same magnitude but inverse ) when the outcome has low incidence ., Discovery of protective variants for HIV-1 infection in a population with average sexual contact and some use of protection against transmission would require extended observation time to detect differences in the survivor rates ( “rates of non-seroconversion , ” ) while differences in rates of seroconversion of the same magnitude can be detected statistically within a shorter time period ., A limitation of these results is that confounding cannot be ruled out with certainty as the source of the associations ., This is true for any observational study , though observational studies remain a fundamental part in building a step-wise scientific case for causal association for many exposures that cannot be tested experimentally in humans ( including exposure to genetic variants , the epidemiological exposure being tested here ) , with smoking as a cause of lung cancer being a prime example ., 65 We have guarded against confounding to the extent possible by evaluating for known potential confounders , including ancestry group ( which likely includes HLA ) , BV , age , cohort , ethnic group affiliation and spatially isolated ancestry ., The next general step in establishing causal association is replication by different groups and functional studies ., We are currently engaged in the latter and encourage validation of our results in genetic association studies with HIV-1 exposure measurement as well as functional studies by others of the variants/genes discovered and replicated here ., In summary , we used quantitative measures of HIV-exposure to select individuals with extreme HIV-1 acquisition phenotypes and thereby optimize our power to detect genes associated with risk of HIV-1 acquisition ., We identified variants , including rare variants , in CD101 and at least one in UBE2V1 that are significantly associated with increased HIV-1 acquisition risk ., More detailed dissection of the molecular basis for this association may identify unique interventions that use these pathways to improve public health prevention of HIV-1 ., We identified individuals for this study from HIV-1 serodiscordant couples recruited into three cohorts of African heterosexual HIV-1 serodiscordant couples: the Partners in Prevention HSV/HIV Transmission Study 20 ( ClinicalTrials . gov number , NCT00194519 ) , the Couples Observational Study 14 , and the Partners PrEP study 22 ( ClinicalTrials . gov number , NCT00557245 ) ( S1 Table ) ., Detailed procedures have been reported elsewhere for each of these studies 14 , 20 , 22 ., Briefly , routine follow-up visits with both partners were scheduled at least every 3 months , with clinical , behavioral and demographic data collected ., HIV-1 seroconversion ( SC ) was assessed by HIV-1 rapid test at the study clinic; positive rapid tests were confirmed by HIV-1 ELISA at the site laboratory , and by Western Blot in batch at the University of Washington ( UW ) ., Plasma virus sequencing performed on both partners for each couple associated with SC was used to confirm transmission linkage 66 ., All participants provided written informed consent for participation in the clinical study , and samples for this genotyping study were selected from among those participants recruited at 14 sites across all three cohorts who additionally consented to host genetic studies ., Relevant study documents went through ethical review and approval by the following committees: An extreme phenotypes case-control design was employed for the Discovery stage ., The extreme phenotypes design provides the greatest statistical power for a fixed Discovery sample size of 100 individuals ( with power increasing as the percentiles of phenotype become more extreme in the two arms ) ., Hence , individual phenotype was a primary consideration in Discovery stage participant selection ., Extreme cases here comprise individuals with relatively low estimated exposure who seroconverted during the study , especially those who converted early in the study ., Extreme controls comprised individuals with high estimated risk who remained seronegative over the full observation period with follow-up for at least nine months ., Highly-exposed HIV-1 exposed seronegative control individuals with longer follow-up time were considered more extreme based on cumulative exposure scores across all study visits ., Cumulative exposure score to rank extremes was calculated as previously described using plasma HIV-1 RNA level of the infected partner , frequency of unprotected sex and male circumcision status 23 , with the modification that all participants must have reported unprotected sex by at least one partner in the couple to be eligible for the Discovery stage sample ., It is possible for an individual to have a fairly high exposure score in this model even if no unprotected sex is reported , because the original risk score is based on empirical estimates of risk of seroconversion given the variable values , and some participants who reported no unprotected sex did seroconvert with plasma HIV-1 genomes matching those of their infected partners ., Hence , the risk among the group that reported “never unprotected sex” is not zero ., Nevertheless , the latter risk is smaller than that for those who report unprotected sex , all else equal , and the risk rises as the proportion of reports with unprotected sex rises ( S6 Fig ) ., Seronegative individuals from couples that did not report unprotected sex were excluded for statistical power reasons: including unexposed individuals lowers the statistical power to the point where a p-value of 6 . 3x10-5 in the Replication stage ( N = 261 ) ( Table, 3 ) becomes p = 0 . 03 ( N = 1229 ) ( Table 4; replication stage methods below ) ., Given these exposure scores/conditions defining the extremeness of phenotype within the potential cases and controls , we then incorporated both gender balance within-group and pairwise ethnicity/sex matching between-groups to this design to avoid happenstance confounding by differing proportions of sex or ancestry in the relatively small samples ., The potential impact of ancestry-confounding in this subpopulation was largely unknown when the Discovery stage was designed , and we opted to take this precaution against confounding ., Specifically , seroconverter ( SC ) “cases” were selected from the Partners in Prevention HSV/HIV Transmission Study and COS cohorts among couples with laboratory confirmed linked HIV-1 transmission 67 , who were HIV-1 polymerase chain reaction ( PCR ) negative at enrollment and had the lowest exposure scores , conditional on relatively equal numbers of males and females ., Individuals from the Partners PrEP cohort ( S1 Table ) were not available for Discovery stage sampling because the trial had not come to complete closure with data available for ancillary studies at that time ., For selection of Discovery stage controls , we excluded from consideration: SC individuals ( with either linked or unlinked transmissions ) , couples in which HIV-1 infected partners reported use of any antiretroviral therapy ( ART ) , couples with no reported unprotected sex during the study and couples with less than nine months follow-up time ., After these exclusions , for each selected case , all HESN of matching sex and self-reported ethnicity were identified , and from these matched individuals , the HESN individual with the highest cumulative exposure score was selected as the matching control ., A total of 65 case-control pairs were identified in this manner , with identification of low exposure ( extreme ) cases being the highest selection priority , followed by ethnicity/sex matching for controls , followed by criteria for high exposure among the matching controls ., We quality controlled these case-control pairs for gender check , cryptic relatedness and genetic heterogeneity across multiple longitudinal whole blood DNA samples , using a custom Illumina Goldengate chip with 384 single nucleotide polymorphisms ( SNPs ) ., These test SNPs were selected as being the most predictive of ancestry clusters and individual identity from a principal component analysis ( PCA ) on data from a previous genome-wide association study 14 that included samples from these same cohorts ., Specifically , we first performed a PCA on a pruned set of 133 , 991 SNPs from that GWAS that had low linkage disequilibrium ., The first five PCs for this analysis were effective at distinguishing participants from East and southern Africa , by country ( Kenya , Uganda , Tanzania , South Africa and Botswana ) and by self-reported ethnicities reported in >2% of participants ( S10 Fig ) ., Using the first five PCs , we then assigned all participants to one of nine ancestry clusters based on model-based clustering , which has previously been shown to reduce population stratification bias ., Subsequently , we used the Random Forests algorithm to identify 357 SNPs that were most predictive of geographic region ( East Africa versus southern Africa ) and the nine ancestry clusters ., These 357 SNPs were collectively able to differentiate the ancestry clusters but were much less important for predicting ancestry than self-reported ethnicity and geographic region ( S11 Fig ) ., The final Goldengate SNP chip included the 357 ancestry SNPs along with 27 SNPs that maximized the probability that all participants had a different genotype at one or more loci in order to ensure that DNA samples came from unique individuals ., The genotyping chip was used on DNA from 65 potential case-control pairs ., After eliminating samples that failed QC ( 1 case failed cryptic relatedness , and longitudinal samples from 2 controls suggested potential sample heterogeneity ) , verifying matching on ancestry cluster and identifying controls with highest cumulativeHIV-1 exposure scores over all visits , 50 case-control pairs were selected for complete genome sequencing for the Discovery analysis ( 24 male cases with matched controls , and 26 female cases with matched controls ) ., When characterized by the two strongest components of the exposure score , namely mean plasma HIV-1 RNA and proportion of follow-up visits where no-condom use was reported , the Discovery stage controls were verified as sampled from the highes

Introduction, Results, Discussion, Materials and methods

Host genetic variation modifying HIV-1 acquisition risk can inform development of HIV-1 prevention strategies ., However , associations between rare or intermediate-frequency variants and HIV-1 acquisition are not well studied ., We tested for the association between variation in genic regions and extreme HIV-1 acquisition phenotypes in 100 sub-Saharan Africans with whole genome sequencing data ., Missense variants in immunoglobulin-like regions of CD101 and , among women , one missense/5’ UTR variant in UBE2V1 , were associated with increased HIV-1 acquisition risk ( p = 1 . 9x10-4 and p = 3 . 7x10-3 , respectively , for replication ) ., Both of these genes are known to impact host inflammatory pathways ., Effect sizes increased with exposure to HIV-1 after adjusting for the independent effect of increasing exposure on acquisition risk ., Trial registration: ClinicalTrials . gov NCT00194519; NCT00557245

Antiretroviral drugs for pre-exposure prophylaxis ( PrEP ) or treatment significantly reduce risk of HIV-1 acquisition and transmission , but face challenges of increasing access , maintaining high adherence , and selecting viral resistance ., Improved understanding of the molecular determinants of HIV-1 sexual transmission could provide new public health HIV-1 prevention interventions ., Factors proven to impact sexual HIV-1 transmission risk include epidemiologic exposure ( e . g . , level of virus in the transmitting partner and frequency of unprotected sex ) , presence of genital inflammation , and host genetic variants common in the population ., Rare or intermediate frequency genetic variants are an increasingly recognized reservoir of complex human disease-causing factors , but are not well studied in HIV-1 infection ., However , the low frequency of these variants reduces statistical power to detect disease associations ., Aggregating variants in a common biological domain ( e . g . , a gene ) can increase power for identifying variants with a common direction of effect ., We report comparison of whole genome sequences from HIV-1 exposure extremes—highly-HIV-exposed individuals who remained HIV-uninfected and lower-exposed individuals who became HIV-infected ., We discover and replicate associations between HIV-1 risk and aggregate variation in two genes , CD101 and UBE2V1 that increase directly with the level of HIV-1 exposure ., These genes may modulate host inflammation thereby identifying molecular mechanisms linking genital inflammation to HIV-1 infection , possibly leading to novel candidate host-directed HIV-1 prevention interventions .

blood cells, genome-wide association studies, innate immune system, medicine and health sciences, immune physiology, cytokines, pathology and laboratory medicine, body fluids, immune cells, pathogens, immunology, microbiology, alleles, retroviruses, viruses, immunodeficiency viruses, ethnicities, developmental biology, rna viruses, genome analysis, molecular development, white blood cells, genomics, animal cells, medical microbiology, hiv, microbial pathogens, t cells, hiv-1, viral replication, genetic loci, blood plasma, immune system, people and places, blood, anatomy, cell biology, virology, viral pathogens, physiology, genetics, biology and life sciences, population groupings, cellular types, computational biology, lentivirus, regulatory t cells, organisms, human genetics

journal.pcbi.1001079

First Principles Modeling of Nonlinear Incidence Rates in Seasonal Epidemics

A plethora of deterministic epidemic models involving susceptible , infected and recovered individuals have been proposed 1 , 2 , carefully analyzed 3–8 and confronted with data sets in the biomathematics and ecology literatures 9–12 ., A well defined topic within this mathematical ecology research area is the study of -type models with seasonal forcing 13–16 ., These models have proved to be useful for understanding the observed patterns and the natural processes behind human and non-human epidemics 17–21 ., Here , we restrict our attention to the and models in which we introduce seasonal forcing while varying the structural form of the incidence rates ., Two hypotheses pertaining the RSV and the measles transmission mechanisms were modeled with two simple functional forms of the incidence rates ., We show that in doing so , we are able to attain a clear separation of the roles of the environmental forcing and the mechanics of the disease transmission in shaping the epidemics dynamics ., The construction of deterministic incidence rates functions is a critical building block of epidemiological modeling ., In a seminal paper , Hethcote 1 showed that because there are many choices for the form of the incidence , demographic structure and the epidemiological-demographic interactions , there really is a plethora of incidence rate functional forms to choose from ., Not surprisingly , the biomathematics literature abound in qualitative mathematical analyses of many of these functional forms 22–26 ., However , biological first principles derivations of incidence rate functional forms are not too common ., As we show in this study , using such first principles derivations greatly enrich the reaches of the practice of confronting models with data while testing biological hypotheses ., Thus , despite the big amount of available functional incidence rates forms 1 , we believe that the set of models chosen to be confronted with data should be restricted to those forms derivable from first principles ., To illustrate this argument , in this study we first show that a simple probabilistic setting wherein infectious encounters are modeled with a pure birth stochastic process leads to a general nonlinear incidence form proposed previously by Liu 24 and later analyzed by Hethcote and Van Den Driessche 23 ( hereafter we refer to the Liu , Hethcote and Van Den Driessche incidence rate as the LHD incidence rate ) ., The LHD incidence rate leads to models with qualitatively different dynamics compared with the ones obtained using the classical incidence rate ., In the SIRS model with either incidence rate and seasonal forcing , becomes a periodic function of time and the trajectory “pursuits” a moving target thus giving rise to limit cycles ., That moving target is the former endemic equilibrium that bounces back and forth between two points ., In either model , the target switches between that moving point and the disease free equilibrium when crosses 1 , giving rise to a period doubling bifurcation ., In the SIRS model with classical incidence rate this mechanism does not depend on the initial conditions ., In this work we show that the disease free equilibrium ( DFE ) is unconditionally an attractor in the SIRS model with LHD incidence rate ., This leads to a scenario where two regions of attraction can coexist ., The trajectory will either reach the disease free equilibrium or have periodic solutions depending on the initial conditions ., Furthermore , after carrying a formal model selection we show that the SIRS model with LHD incidence rate leads to a significant fit improvement over the classical SIRS model with the same seasonal forcing ., Finally , we compared the applicability and generality of the classical and LHD incidence rates functions by fitting them to two measles time series data sets ., Using the later function leads to a vast improvement of model fit in both cases ., Since we were fitting a deterministic SEIR model , we chose to use the data from the two largest cities in the measles data set ( London and Birmingham , see http://www . zoo . cam . ac . uk/zoostaff/grenfell/measles . htm ) , where the effects of demographic stochasticity are expected to be less influential in the dynamics of the epidemics 10 ., Varying the form of the contact rate function while including environmental stochasticity in the SIRS and SEIR models leads to a better understanding of the dynamics of an infectious disease transmission ., Depending on the model and contact rate , the disease free equilibrium ( DFE ) is either a saddle point or an attractor ., In the first case , if a trajectory located originally in the basin of attraction of the endemic equilibrium ( EE ) basin of attraction is perturbed with environmental noise , it may transiently visit the DFE stable submanifold and then return to the EE basin of attraction ., If however the DFE and the EE coexist as stable equilibria , a trajectory initially at the EE basin of attraction may end up in the DFE basin of attraction ., The interaction between stochasticity and the different contact rate models was studied using computer intensive simulations of the Poincaré map 27 ., The classical model has been extensively studied in order to predict and understand various disease dynamics behaviors , as well as their spread and persistence 28 ., For many infectious diseases , the pool of susceptible individuals is replenished due to the waning of immunity 17 , 18 ., To account for the lost of immunity , the classical susceptible , infected and recovered model is adjusted by allowing a fraction of the recovered individuals to move back into the susceptible pool at a rate ., This susceptible , infected , recovered and susceptible model is expressed as ( 1 ) ( 2 ) ( 3 ) where is the rate of loss of infectiousness and the total population size remains constant ( i . e . ) ., The constant represents both , the birth and mortality rates ., Assuming that birth and mortality rates are equal is justified on the grounds that the annual infection rate is considerably higher than the population growth ., The constant is the contact rate , the average number of individuals with whom one infected individual makes sufficient contact to pass on the infection 29 ., The fraction represents the average number of infections per susceptible individual and hence represents the expected number of infections when susceptible individuals are available 5 ., Note that the above definition of as a per individual constant leads to a consistency of the units within each of the model equations and assumes homogeneous mixing ., In the following sections we will discuss different ways to model the incidence rate ., The equations for the classic SEIR ( Susceptible-Exposed-Infectious-Recovered ) model are as follows 30: ( 4 ) ( 5 ) ( 6 ) ( 7 ) where represents both , the birth and mortality rates per capita ., The mean latent and infectious periods of the disease are and ., As written , the SEIR model has a stable endemic equilibrium provided ., Further biological realism to model recurrent epidemics can be incorporated to both this SEIR model and the SIRS model above by assuming that the transmission rate varies seasonally ., Indeed , Earn et al 30 study the range of the dynamical behavior of the SEIR model with seasonality and find it useful for explaining the measles numerous transitions between regular cycles and irregular , possibly chaotic epidemics ., Also , Alonso et al . 31 show that noise amplification provides a possible explanation for qualitative changes from regular to irregular oscillations of lower amplitude ., In this paper , we follow the suggestion made by Hethcote 1 and couple Liu , Hethcote and Van Den Driessches incidence rate with seasonal forcing in both the SIRS and SEIR models ., To incorporate the claim that epidemics of recurrent infections is driven by seasonality , it is customary to depart from the standard incidence rate by assuming that the average number of incidences sufficient for transmission per infected individual , is a periodic or quasi-periodic function of time ( ) ., Often , the incidence rate is assumed to have a sinusoidal form of the type ( 8 ) where stands for the strength of the seasonality and year ., Various authors have shown that such a generic description of the seasonal variation in transmission rates is not as revealing as a detailed description of the actual processes underlying the seasonal drivers of transmission through mechanistic seasonal forcing functions 11 , 18 , 30 , 32 , 33 ., However , as we show in the results section , in some cases this sinusoidal function may unequivocally represent a linear transformation of a weather covariate ., Although other authors have used a more flexible Haar step function for the seasonal forcing ( e . g . 30 ) , we restrict ourselves to the incorporation of the sinusoidal form above ( eq . 8 ) as the seasonal forcing ., This has the advantage of ease of interpretation and qualitative analysis ., In any case , the main purpose of incorporating the forcing is to explore the main qualitative characteristics of coupling the seasonally varying disease transmission and different incidence rate functional forms ., Brauer 34 generalizes the incidence rate definition in the following way: if the average member of the population makes contacts in one unit of time with , and if is the probability of choosing one infected individual from the population at random , then is the rate of new infections per unit of time ., The mass-action incidence rate model is recovered using and the classic incidence rate is recovered by picking ., A general incidence rate function was proposed by Hethcote and van den Driessche 23:where and are constants ., Consider the special case where and ., Using Brauers generalization and idea , Hethcote and van den Driessches model is recovered using the function ., Then , the incidence rate function becomeswhere and ., Although the mathematical properties of the general function are known in general 23 , 35 , 36 a mechanistic , first principles derivation of it is still lacking ., Such a derivation can be obtained using a probabilistic reasoning analogous to the argument used by 37 to model the Allee effect through stochastic mating encounters: Through physical movement or any other means of dispersion , an infected individual will have contact with a given number of susceptible individuals in the population ., The potential to effectively disperse the disease ( virus ) could be thought of as being proportional to that number of susceptibles with whom the infected individual makes contact: indeed , the more contact the infected individual has with susceptibles , the more likely he is to effectively transmit the disease ., It then follows that the magnitude of the realized disease dispersion could be measured for example , in terms of the dispersion ability ( i . e . vagility ) of the infected individual ., Accordingly , every infected individual will be expected to realize a certain virus ( or micro-parasite ) dispersion potential ., Let the realized disease dispersion made by one infected individual be denoted by ., Then , the number of successful transmission encounters per infectious individual can be modeled with a random variable ., By writing , we are stressing the fact that the infection process is a function of the magnitude of the realized dispersion ., Furthermore , we assume that the probability that an infected individual encounters and infects a susceptible individual given a realized change in dispersion is proportional to the previous number of successful infection encounters times a function of the number ( or density ) of the infected individuals in the population ., Often 7 , a non-linear function is chosen to account for factors such as crowding of infected individuals , multiple pathways to infection , stage of infection and its severity or protective measures taken by susceptible individuals ., These assumptions allow us to specify a new infection event as the conditional probability ( 9 ) where is a non-negative function such that is a constant ., Towards the end of this section we discuss possible functional forms for ., We remark that if counts the number of successful transmission encounters of an infected individual that recently invaded a population consisting only of susceptible individuals , then the expected value of is in fact equal to the mean number of secondary infections in the context of the SIRS model ., If the SEIR model dynamics is in place , then , when there is only one infected individual in the population , ., Assuming that the probability that more than one successful infectious encounter occurs after an extra dispersion amount is negligible , then can be modeled using a simple homogeneous birth process where the quantity being born is the number of successful virus transmission encounters ., The probabilistic law of this stochastic process is completely defined by the terms ., To solve for these terms , first note that according to eq ., ( 9 ) which leads toIn the limit when , the above equation leads in turn to the following system of differential equations:Then , it is well known 38 that solving this system of equations leads to ( 10 ) ( 11 ) Furthermore , approximating using a Taylor series expansion around leads to specific quantitative definitions of the stochastic process ., For example , if or if , the one-step transition probability mass function ( pmf ) of adopts the negative binomial and Poisson forms respectively 37 ., The Negative Binomial transition pmf would bring into the picture over-dispersion ( higher variance to mean ratio ) as a key qualitative property of the moments of the pure birth process describing the evolution of the number of successful transmission encounters ., In any case however , the probability that one infected individual successfully passes on the infection isThis expression is readily interpretable: for a fixed value of , the probability of successfully passing on the infection converges to as the product grows large ., Therefore , in this expression we are recovering the model property that the probability of successfully passing on the infection increases with the realized disease dispersion effort ., Each individuals realized dispersion is in turn related to the individuals ‘effort’ to transmit the infection ., In a given population , the magnitude of the realized disease dispersion for each infected individual can be expected to vary widely ., Accounting for this demographic source of heterogeneity could be achieved by assuming that each individuals dispersion ability is drawn from a given probability distribution ., That is , we would be modeling the variation in disease dispersion per infected individual with a random variable whose pdf has support on ., Without loss of generality , here we model randomness in the product instead of just in the realized disease dispersion ., Then , the probability that an infected individual chosen at random from the population realizes more than one successful secondary infection is found by averaging over all the possible realizations of ., That is , A suitable probabilistic model for with empirical and theoretical support can be difficult to find ( see for instance the models in 39 ) ., A flexible positive , continuous distribution such as the gamma distribution could therefore be used ., Here , we assume that the magnitude of the disease dispersion brought about by an infected individual is distributed according to a special case of the gamma pdf , the exponential distribution ., Accordingly , letting we get that the probability of successfully transmitting, the infection is ( 12 ) As mentioned before , various biological hypotheses pertaining the behavior of the transmission as a function of the abundance of infected individuals have been advanced to justify various functional forms of ., Suitable candidates for should satisfy the conditionsThese conditions guarantee the basic requirement that the probability of a new infective encounter ( eq . 9 ) is null in the absence of infected individuals and that the overall chance that a new infection occurs increases proportionally with when is small ., Furthermore , if such proportionality decreases in magnitude as grows large ( that is , is concave down ) ., Consider the following two functional forms: Many other functional forms for the incidence rate could be derived using the above arguments ., If for instance other heavy-tailed distributions are used instead of the exponential distribution , other incidence rate functional forms will arise and this could certainly be the topic of further research ., However , in this work we limit ourselves to the exploration of the reaches of using the LHD model because it explicitly incorporates heterogeneity in transmission potential , because of its bi-stability properties ( see “qualitative analysis of the SIRS models” section ) and to formally test if it arises as a better explanation for bi-annual epidemic patterns using data from different localities and diseases ., Thus , from this point on , in this work we will only consider the LHD incidence rate function and the classical incidence rate ., In his seminal paper , Hethcote 1 also mentions that the LHD general incidence rate function could be eventually coupled with any seasonal forcing function ., Motivated by this comment , in the results section we explore the reaches of doing so ., The two different SIRS models were fitted to time-course data of reported cases of syncytial virus infections ., The data come from Gambia and Finland ( Figure 1 ) ., Two ML formulations were used ., The first one consisted of a Poisson likelihood that only required the available observed counts of infected individuals ( eq . 13 ) ., The second formulation consisted of the joint likelihood of the counts and of the observed weather covariate and thus used information present on the time series of reported cases and on the corresponding time series of mean monthly temperature range for both locations ., The ML estimates according to the first formulation for each model and data set combination are displayed in Table 1 ., Both information criteria used indicate that for Finland , the best model was the SIRS model with LHD incidence rate function ., For Gambia , both information criteria for the SIRS model with classic incidence rate function are lower by three points approximately ., This implies that given the data and the two information criteria ways of penalizing the likelihood score , both models are nearly indistinguishable for any practical purpose 49 ., In Gambia , the extra parameter introduced by the LHD model is penalized: given the data set at hand , incorporating one extra parameter does not lead to a clear improvement In Figure 2 we plotted the model predicted number of infected individuals versus the observed values for the classical and the LHD SIRS model respectively ., Note that , even though the best model is deterministic , the dynamics displayed by the data ( small epidemics followed by a big epidemic peak ) is very well recapitulated by the predicted solutions ., The results of the second ML formulation are qualitatively identical to the results with the Poisson likelihood ( see table in the Text S1 ) ., For Finland , the BIC statistic for the classical model was 10376 . 2000 and for the LHD model 9893 . 5780 ., For Gambia , the BIC for the classical model was 729 . 1133 whereas the LHD model had a BIC of 733 . 2750 ., Hence , here again , for Finland the LHD is the best model whereas for Gambia , the classic model is better ., Because the BIC can be used only to compare models for which the numerical values of the dependent variable are identical for all estimates being compared , it cannot be used to select between the two ML formulations ., Indeed , in the second likelihood formulation the data fitted consist not only of the time series of infected counts but also of the monthly temperature range , thus it uses twice as much data for parameter estimation ., Zeng et al 55 mention that an indication of which likelihood formulation is better can be obtained by comparing the per datum BIC score ., Take for instance the BIC for the LHD model for Finland , 9893 . 5780 ., Dividing that BIC by the total number of data points used ( , we get a per datum BIC of 48 . 4979 ., Now , the BIC for the LHD model for the Poisson likelihood formulation is ( Table 1 ) 3196 . 9330 ., Dividing that number by the number of data points used ( ) we get 31 . 34248 ., Thus , the Poisson likelihood formulation yields a better per datum BIC for Finland ., For Gambia , the Poisson likelihood formulation seems to be better than the Poisson-Normal sampling model: for the classic model with Poisson likelihood this statistic is , whereas for the classic model with Poisson-Normal likelihood it is ., The SEIR model with classic and LHD incidence rate were fitted to measles time series data from London and Birmingham ., In both cities , the SEIR-LHD model was selected as best ( see Table 2 ) ., Notably , the difference in AIC and BIC is at least 2000 points in each case ., The predictions for each model and city combination are shown in Figure 3 ., We remark that assessing and comparing the quality of the model predictions visually may be misleading ., Indeed , according to our likelihood formulation , the parameter estimation process does not weight equally a deviation from the model prediction at low and high infected counts ., In fact , the variance of the Poisson sampling error varies according to the mean predictions ., In this section we discuss the differences in the qualitative behavior of the SIRS model ( 1 ) – ( 3 ) with both classical and LHD incidence rates with and without seasonal forcing ., We refer the interested reader to the Text S1 for proofs of the following claims ., By construction , the set is a positively invariant set of the SIRS model ( 1 ) – ( 3 ) ., If we set the coefficients constant , the Dulac criterion guarantees that the SIRS model with neither the classic nor the LHD incidence rate function has periodic solutions in ., Regarding the classical incidence rate , the SIRS model has two stationary solutions: a disease free equilibrium ( ) and an endemic equilibrium ( ) ., It is well known that is a threshold for this model: If the disease remains endemic , while implies that the disease dies out ., On the other hand , the SIRS model with LHD incidence rate has one disease free equilibrium and two endemic equilibria and ., The is unconditionally a local attractor ., However , only one of the endemic equilibria denoted as , lies inside the positively invariant set ., If the endemic point is locally an attractor ., Thus , when the LHD model exhibits bi-stability ., Introducing seasonal forcing has the following effects on the SIRS dynamics with classic incidence: first , it is well known that by letting the contact rate to be a periodic function of the form ( 8 ) where is small , the SIRS model with classical incidence rate has a periodic solution with period ., This behavior is shown in Figure 4 A and B . Also , when seasonal forcing is introduced , the basic reproductive number becomes a periodic function of time , , that oscillates between the values and ., The endemic point also becomes a periodic function of time that bounces back and forth between two extreme points , and ., The expressions for and are given in the Text S1 ., The associated limit cycle of the models solution inherits the stability behavior of the endemic point: if , then the limit cycle is asymptotically stable ., A stable limit cycle is displayed in Figure 4 C . Because the function can cross the boundary of periodically depending on the value of , the dynamic behavior of the models trajectory with respect to the nature of the endemic point ( stable/unstable ) can be described with a race analogy: The models solution can be thought of as a hopeless ‘pursuer’ engaged in a race against the endemic solution who plays the role of the fast ‘leader’ that cannot be caught upon ., Just as in a cycling race , as soon as the leader changes its strategy , so does the pursuer behind the leader ., In that way , if is such that and only while , the leader ( ) is deemed as stable and the solutions trajectory pursues the endemic point ., As soon as becomes less than , the leader ‘changes its strategy’ and is deemed unstable whereas the becomes stable ., At that moment , the trajectory switches its objective and pursues the and keeps doing so while ., That sudden change of objective gives rise to a period doubling bifurcation of the limit cycle as seen in Figure 4 D . This change of objective ( period doubling bifurcation ) happens as grows large ., We remark that at least one route to chaos in the associated Poincaré map of this model when is taken as the bifurcation parameter has been shown 53 , 56 , 57 ., Finally , in the SIRS model with LHD incidence rate ( see Figure 5 A and B ) , if we let the contact rate to be a periodic function of the form ( 8 ) , a limit cycle also arises ( see Figure 5 C ) ., Here again , as increases , the trajectory engages in the same pursuer/leader dynamics and the limit cycle undergoes a period doubling bifurcation ( Figure 5 D ) ., However , contrary to what happens in the classical SIRS model with seasonal forcing , periodicity or extinction of the epidemics depends also on the initial conditions: if the initial proportion of infected individuals is too high , the disease will die from a subsequent depletion of the susceptible pool of individuals ., Only if the epidemic begins with a small number of individuals will it slowly work its way up and attain a persisting limit cycle ., Multiple lines of evidence show that the forced SIRS and SEIR models with LHD incidence rate function constitute a better explanation of the seasonal epidemic patterns than the corresponding classical models with seasonal forcing , for the data sets and cases explored here ., The first line of evidence is statistical in nature: when confronted with different time series of seasonal epidemics , the LHD model was selected as best in three out of four cases and in the fourth case , the LHD model was nearly indistinguishable from the classic model ., By formulating the fitting and the model selection problems using likelihood-based inference and information theoretic model selection criteria we were able to conclude that given the data and the models at hand our model embodies the most likely explanation of how the observed data arose ., Our models nonlinear incidence rate takes into account heterogeneity in the ability to transmit the infection while modeling the infectious process as a pure birth stochastic process and hence , it is a more realistic model formulation ., This new level of model complexity was achieved by incorporating only one extra parameter ., The emphasis we give to a first principles derivation that hinges on interpretability and simplicity is not always sought in other SIR-type model formulations and modeling exercises 6 , 17 , 18 , 24 , 58 ., Hence , our results show that a careful exploration of other incidence rate functions before resorting to mathematically more complex , high-dimensional models may bring new insights into the current understanding of the functioning of epidemics ., Another line of evidence in favor of the LHD model comes from its qualitative predictions ., The classical SIRS model without the seasonal forcing predicts somewhat artificially that regardless of the initial proportion of infected and susceptible individuals , provided , the endemic equilibrium will be reached 28 ., On the other hand , the LHD model without seasonal forcing predicts that the disease-free equilibrium is always an attractor , thus exhibiting bi-stability ( see qualitative analysis section ) ., Hence , if the initial proportion of infected individuals is too high , the disease will die from a subsequent depletion of the susceptible pool of individuals , contrary to what the classical model predicts ., For the disease to persist in the population , the initial proportion of infected individuals has to be very low ., Only then the infection process will proceed steadily to the endemic solution ., This qualitative prediction matches the virus transmission strategy that the syncytial virus seems to have evolved: recall that in our model the extra parameter is the density of infected individuals at which the probability of successfully transmitting the infection is ., In every locality , the ML estimates of were in the order of to , thus indicating that a very low density of infected individuals is needed in order to maximize the transmission rate of the measles and RSV diseases ., Incorporating weather covariates to our nonlinear SIRS model further improves the biological insights that can be concluded from the parameter estimation and model analysis exercises ., A simple look at the strong auto-covariation patterns and at the pure weather trends , in particular for Gambia ( Figure 1 ) indicate that modeling weather and weather effects with a sinusoidal function seems a natural add-on to the classic SIRS model , for this data set ., For Gambia , the fact that the per datum BIC for the LHD model with the joint Poisson-Normal likelihood is very similar to the per datum BIC for the classic model indicates that the weather can indeed be viewed as a simple rotation and translation ( eq . 15 ) of the weather effects ( eq . 8 ) ., Thus eq ., 15 may not always be viewed only as a phenomenological artifact 18 ., For Finland , however , this was not the case ., The per datum BIC favors much more clearly the Poisson likelihood formulation ., Hence , we consider that in Finland the weather effects model ( eq . 8 ) would be better expressed as some unknown nonlinear transformation of the weather ., In other words , in this country with more extreme weather , a change in the temperature range of a certain size is not translated as an equivalent change in the weather effects in the transmission rate ., Also embedded within our weather effects model formulation ( eq . 8 ) is the hypothesis that weather affects incidence rates in a nonlinear fashion ., In particular , when the strength of seasonality is high enough , the limit cycles predicted by both weather forced models undergo a period doubling bifurcation such that relatively small epidemic outbreaks are followed by big ones ., Notably , these effects of the strength of seasonality were detected in Finland , the locality where the amplitude of the relative weather oscillation is larger ., The model selection exercise should by no means be the ending point of the analysis ., Instead , if appropriateness of one model vs . the other cannot be resolved , a near-tie in a model selection situation should lead to the search and reformulation of each models scientific predictions in a way that can be clearly tested in further experiments ., Hence , the model selection results presented here should be rather viewed as the starting point of further analyses ( see 59 ) ., Even for simple deterministic models , parameter estimation for dynamic data can be non-trivial ., Dynamic models often present multimodal likelihoods thus complicating the parameter estimation process 42 ., In these cases , the type of inferences possible is limited due to the presence of wide confidence sets that include parameter values with different qualitative predictions ., If for instance the ML estimate of a bifurcation parameter is in a 2 limit-cycles region but its confidence interval includes parameter values for which these cycles do not appear , then there is not enough evidence in the data at hand to properly infer something about the size of the parameter of interest and hence , about the dynamic properties displayed by the data ., In our case however , the precision of our parameter estimates and in particular , of the bifurcating parameter ( Tables 1 and 2 ) is en

Introduction, Model, Results, Discussion

In this paper we used a general stochastic processes framework to derive from first principles the incidence rate function that characterizes epidemic models ., We investigate a particular case , the Liu-Hethcote-van den Driessches ( LHD ) incidence rate function , which results from modeling the number of successful transmission encounters as a pure birth process ., This derivation also takes into account heterogeneity in the population with regard to the per individual transmission probability ., We adjusted a deterministic SIRS model with both the classical and the LHD incidence rate functions to time series of the number of children infected with syncytial respiratory virus in Banjul , Gambia and Turku , Finland ., We also adjusted a deterministic SEIR model with both incidence rate functions to the famous measles data sets from the UK cities of London and Birmingham ., Two lines of evidence supported our conclusion that the model with the LHD incidence rate may very well be a better description of the seasonal epidemic processes studied here ., First , our model was repeatedly selected as best according to two different information criteria and two different likelihood formulations ., The second line of evidence is qualitative in nature: contrary to what the SIRS model with classical incidence rate predicts , the solution of the deterministic SIRS model with LHD incidence rate will reach either the disease free equilibrium or the endemic equilibrium depending on the initial conditions ., These findings along with computer intensive simulations of the models Poincaré map with environmental stochasticity contributed to attain a clear separation of the roles of the environmental forcing and the mechanics of the disease transmission in shaping seasonal epidemics dynamics .

Nonlinearity in the infection incidence is one of the main components that shape seasonal epidemics ., Here , we revisit classical incidence and propose a first principles derivation of the infection incidence rate ., A qualitative analysis of the SIRS model with both the classical and the proposed incidence rate showed that the new model is physically more meaningful ., We conducted a statistical analysis confronting the SIRS and SEIR models formulated using both incidence rate functions with four data sets of seasonal childhood epidemics ., Two data sets were hospital records of cases of syncytial respiratory virus ( RSV ) ., The other two data sets were taken from the well-known UK measles epidemics database ., We found that seasonal epidemics is better explained using our incidence rate model embedded in a Poisson sampling process ., The results presented here are not by any means an exhaustive exploration of the interplay between nonlinear dynamics and stochasticity ., Our results may be viewed as the starting point of multiple research avenues ., Three such research topics could be: the first-principles derivation of non-linear incidence rate functions , the role of bistability and demographic stochasticity for disease persistence and the simulation of environmental and demographic stochasticity in the Poincaré map .

ecology/theoretical ecology, computational biology/population genetics, genetics and genomics/microbial evolution and genomics, mathematics/statistics

journal.pcbi.1004043

Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks

The mammalian cortex presents a challenging complex system for the study of information processing , behavioral adaptation , and self-organization ., At rest , a state in which there is no obvious sensory input or motor output , neural activity in the cortex is predominantly spontaneous , or ongoing ., At the single neuron level , resting activity has been characterized as persistent and irregular firing of action potentials , or spikes ., A well-known aspect of cortical spiking is that , at rest , the correlation between distant , single neuron spiking is very low 1 ., Persistent asynchronous background activity ( PABA ) , however , is typically interpreted as a largely independent activity ., Independence does not seem concomitant with the cortex as a complex system , which typically displays interactions among most system elements and long-range structure as detailed below ., Demonstrations regarding the exquisitely high sensitivity of cortical networks to the addition of even a single spike 2 have further fueled the debate concerning robust cortical computation in the presence of apparently uncorrelated contributions from single neurons 2 , 3 ., Other research , however , has demonstrated that spontaneous cortical activity in vitro 4–6 and in vivo 1 , 7 , 8 at the population level manifests as precisely organized spatiotemporal cascades of activity termed neuronal avalanches ., For critical networks , the scale-invariance of avalanche sizes is reflected by a power-law with exponent −3/2 ., Such a power-law is expected when cortical networks are balanced so that spiking activity neither tends to increase nor decrease , a state quantified by the critical branching ratio σ = 1 4 ., Theory predicts that networks with critical dynamics optimize numerous aspects of information processing 9 ., Specifically , experiment and modeling show maximized information capacity and transmission 5 , maximized number of metastable states 10 , 11 , optimized dynamic range 12 , 13 , and optimum variability of phase synchrony 6 ., The ubiquity of scale-invariance in nature , combined with its advantages for information processing , suggests that each of the foregoing properties would be beneficial for neuronal models and artificial systems , i . e . physical embodiments of neuronal networks , as well 14 , 15 ., In all of these cases , the networks in question are critical and not merely balanced ., Recently , both conservative 16 and non-conservative 17 neural networks featuring short-term synaptic plasticity ( STP ) have been demonstrated to be critical , or display neuronal avalanches ., Likewise , neural networks that incorporate long-term synaptic plasticity , such as spike-timing dependent plasticity ( STDP ) , have displayed balanced networks as well 18 , 19 ., These models , however , did not exhibit self-generated PABA 3 , 20 and were not capable of self-tuning to criticality after being steered away from it by strong perturbations ., A neural network that was capable of self-tuning to stable regimes based on short-term plasticity was described in 21 , however that network did not exhibit critical dynamics and did not include long-term STDP that can create lasting changes to synaptic conductances ., We show that it is possible for a single neural network to exhibit four of the above properties simultaneously , namely PABA , self-tuning due to short-term plasticity ( as in 16 , 17 ) and long-term plasticity ( as in 18–19 , 22 ) and critical balance ( as in 16 , 17 ) ., We also show that such a network undergoes a lasting change in synaptic strengths , thereby effective connectivity , suggesting the capability of learning ., Combining all of these properties into a single system greatly diminishes the need for external controls in order to establish the desirable network dynamics and behavior ., To that end , we demonstrate in a 10 , 000 neuron network model with 20% inhibitory neurons including AMPA and GABA-receptor dynamics , how a network self-tunes to criticality ., This deterministic network spontaneously displays PABA and undergoes changes in network dynamics and structure due to short- and long-term synaptic plasticity in the response to perturbations , while self-tuning back to a critical regime ., We believe that this capability will lead to the realization of future synthetic physical systems that self-tune to be optimally sensitive in response to multi-scaled stimuli and adapt to changing environmental conditions , thus paving the way for synthetic intelligent systems 14–15 , 23 , 24 ., A 10 , 000 neuron network with both excitatory ( 80% ) and inhibitory ( 20% ) neurons was simulated for 900 s in 1 ms time-steps ., The simulator itself 25 was based on the dynamics and feature set of a specific neuromorphic hardware implementation 15 , 26 ., These features are short term plasticity ( STP ) , spike-timing dependent plasticity ( STDP ) , and AMPA and GABA-receptor kinetics ., For details of the network model and simulation parameters see Materials and Methods ., The simulation began by injecting Poisson-distributed spikes at a 300 Hz firing rate into a randomly chosen set of 20 excitatory ( E ) neurons ., After 15 ms , the initializing external drive ceased and the network was left to develop its own internal dynamics ., The effect of varying these initial perturbation parameters is not well known , and is a subject open for further study ., After the initial 15 ms , the network stabilized to a spontaneous firing mode where it maintained an average firing rate of 31 . 8 ± 2 . 6 Hz ., Average rate is defined as the number of spikes produced by the network per unit time , divided by the number of neurons ., Spiking was asynchronous and irregular as quantified by both pairwise correlations between spike-trains and the coefficient of variation of inter-spike-intervals ., Specifically , pairwise correlation distributions from spontaneous activity in the model were centered around 0 , as shown in Fig . 1b ., Such weak correlation in spiking in our model is in line with the weak pairwise correlations in spiking found in ongoing activity of awake monkeys that demonstrate neuronal avalanche organization in the local field potential 1 , 27 ( Fig . 1a ) ., They are also similar to near zero-mean correlations found for spiking in vivo in both rats and monkeys , and simulations of large balanced networks that do not exhibit continuous synaptic plasticity 28 , 29 ., While the firing rate achieved in our model is higher than observed in mammalian neuronal networks 3 , 20 , we find that as network size increases , firing rate decreases to below 5 Hz for networks larger than 100 , 000 neurons ( Fig . 1c ) ., In order to quantify the irregularity of spiking in the network , we calculated distributions of the coefficient of variation , C o V = σ μ , of inter-spike-intervals ( ISIs ) ., That is , the ratio of the standard deviation of a series of ISIs to the mean ISI ., If the standard deviation is greater than the mean , i . e . if CoV ≥ 1 , the ISIs are considered irregular ., Fig . 1d shows the distribution of firing rates for the network ., For a range of ISIs between 3 ms and 1000 ms , equivalent to a range between 300 Hz and 1 Hz , the distribution of CoV is as shown in Fig . 1e ., This CoV distribution is heavily skewed and centers near 2 . 5 , demonstrating highly irregular spiking ., If the range of ISIs is restricted further to those between 10 ms and 1000 ms ( 100 Hz to 1 Hz ) ( Fig . 1f ) , then the distribution of CoV peaks slightly below unity , suggesting an exponential distribution of ISIs close to that expected from a Poisson process ., Thus , a considerable contribution to spike irregularity originates from action potential bursts at 100–300 Hz ., Treating the firing rate of the network as a dynamical system in its own right , it is possible to estimate fixed points in order to identify stable ( and unstable ) firing rates ., Assuming a Langevin model for the firing rate , Fig . 2b shows a reconstruction of the deterministic dynamics 30 , 31 ., Firing rates greater than 50 Hz exist entirely in the first 500 ms of the simulation , when the network goes through a stabilization period ., During this period , firing rates visit a sequence of multi- and meta-stable states until settling into a stable fixed point near 30 Hz ., More study is required to know whether the fixed points at greater firing rates still exist or have been annihilated due to bifurcations , a bifurcation being a change in the number or type of fixed points ., It is worth noting that a stable and unstable fixed point pair near 10 Hz ( see Fig . 2b inset ) is very near a bifurcation ., Even though the spiking behavior of the network is classified as uncorrelated , i . e . asynchronous and irregular , there might still be identifiable structure ., Specifically , causal spikes , or spikes that cause other spikes , can be grouped into avalanches of particular sizes; the inflation or deflation of causal spikes as they propagate through the network can be balanced or unbalanced; and fluctuations in ISI can be correlated or uncorrelated ., If these three measures take on particular values , the network is said to be in a critical state , which we detail below ., The first measure is based on avalanche sizes , where an avalanche is identified as a set of contiguous spiking events ., If a neuron spikes without any input from a presynaptic neuron , it is the beginning of an avalanche ., If a neuron spikes due to incoming spikes , the new spike is a member of the same avalanche as the spiking presynaptic neurons ., In a causally closed network , this definition only makes sense for a subgraph of the network , where inputs that start avalanches are allowed to come from neurons outside of the subgraph ., Avalanche size is defined as the number of spikes that belong to an avalanche , and the distribution of sizes is particular to the properties of the network ., If this distribution follows a power-law , it will produce a straight line when plotted on a log-log scale ., The slope of this line , λ , is related to the power-law exponent ., It has been observed , both experimentally and following from theory 4 , that neuronal networks behaving in the critical regime have λ = −3/2 ., Using an avalanche tracking algorithm ( see Methods ) , we measured the avalanche size distribution for a sliding 100 s window , producing estimates for λ over time , which were further grouped into sliding windows of 20 λ estimates to produce histograms over time ( Fig . 2a ) ., After about 300 s into the simulation , λ ≈ −3/2 ., In our analysis , 12 . 5% of neurons were randomly sampled by the avalanche tracking algorithm ( see Methods ) ., Random sampling produces a relatively normal distribution of λ estimates ., After 500 samples for the period of time between 500 s and 600 s , the distribution of corresponding λ estimates had a mean of -1 . 620 with standard deviation 0 . 0700 ., The second measure assessed the branching ratio σ of the network ., The branching ratio σ measures the average ratio of postsynaptic spikes to presynaptic spikes ., If σ < 1 , or is sub-critical , the spiking activity in the network decays ., If σ > 1 , or is super-critical , then spiking activity in the network grows ., A branching parameter σ = 1 signifies a stationary network where , on average , the number of spikes received by a neuron results in about the same number of spikes emitted by postsynaptic neurons 32 ., The branching ratio over the course of the simulation was measured and is plotted in Fig . 2c ., Oscillations of σ about unity indicate that the network is stable in the context of critical branching ., Such stability is a necessary , but not sufficient , condition for the more subtle property of criticality as measured by avalanche size distributions above ., As a third measure of criticality , the network simulation was analyzed using detrended fluctuation analysis ( DFA ) 8 , 33 , which measures how the variance of fluctuations in spiking activity changes over changes in measurement scale ( see Methods ) ., DFA estimates a scaling exponent α , which , when in the range 0 . 5 < α < 1 , indicates positive long-range correlations in the fluctuations ., Results of DFA for individual spike-trains are presented in Fig . 2d as a distribution of scaling exponents with mean α = 0 . 68 ( SD = 0 . 061 ) ., This distribution shows that scaling exponents are spread among the range that coincides with correlated fluctuations ., A network could be balanced , or positioned , at a critical state by , among other possibilities , the adjustment of network parameters or input properties ., In order for a network to seek criticality , instead of merely being positioned there , it must be able to alter itself ., The ability of the simulated network to alter itself was tested by subjecting a subset of excitatory neurons to externally applied perturbations ., Perturbations were organized into a series of 10 pulses of 300 Hz spiking , each lasting 500 ms and separated by 500 ms of silence ., Three such perturbations were applied , at 300 , 400 , and 500 s , respectively ., The three criticality analyses were repeated to confirm that criticality was reattained after such perturbations ., These three measures , λ , σ , and α are shown for the perturbed simulation in Fig . 5 ., Together , they show that the network did indeed reattain criticality ., As a point of interest , the deterministic dynamics of the network firing rate were again reconstructed , this time showing the dynamics present during external perturbation ., The stable point reached during a perturbation is visible as a stable fixed point near 100 Hz ., Notably , the perturbations have appeared to push the low firing rate fixed point near 10 Hz through a bifurcation ( see Fig . 5b inset ) ., Care must be taken with this interpretation , however , as the data only represent an approximation of deterministic dynamics ., We may also note the effect of constant random perturbations ., Such input to the network would provide starting points for new avalanches throughout the whole simulation ., Providing a constant 1 Hz input of Poisson-distributed spikes causes the network to tune towards criticality faster ( possibly due to increased activity of STDP ) , but does not alter avalanche size distributions ., Another method of disturbing the network is to add variation to the network parameters in Table 1 ., We added 1% noise to each parameter throughout a simulation and observed that self-tuning to criticality was still manifested ., As argued above , self-tuning to criticality requires change within the network , which is most readily effected by altering synaptic conductances ., Fig . 6 shows the changing distribution of these synaptic “weights” over the course of the simulation ., While the effect of perturbations on E and I synaptic weights is evident visually , by the three ridges in the weight-time plot respectively ( Fig . 6a , c ) it can be further quantified by comparing the perturbed and unperturbed weights using a simple mean square error measure ( see Methods ) ., This measure shows that each perturbation caused strong change to the synaptic conductances in both E and I weights , which outlasted the perturbation ., Thus , these perturbations also significantly changed the effective network topology as well ., Speaking more directly to topology , it is possible to define an in-degree , the number of incoming connections to a neuron , using a synaptic weight threshold ., In this manner , a connection was considered present if its strength had a value of at least 0 . 1 ., Applying this threshold , the unperturbed simulated network began ( Fig . 7a ) with a mean excitatory in-degree of 80 . 07 ( SD = 8 . 862 ) and a mean inhibitory in-degree of 20 . 06 ( SD = 4 . 471 ) ., Since pre and post neurons for all connections were chosen using the same random selection procedure , in- and out-degrees were approximately equal ., After 300 s of simulation time ( Fig . 7b ) the mean in-degree of excitatory synapses dropped significantly to 15 . 19 ( SD = 3 . 845 , t ( 15998 ) = 600 . 7 , p < 0 . 001 ) ., Inhibitory in-degree remained largely unchanged at 19 . 93 ( SD = 4 . 341 , t ( 15998 ) = 1 . 901 , p = 0 . 057 ) ., Fig . 7c shows the in-degree time-series for both perturbed and unperturbed cases ., It is unclear from the relatively stable mean in-degree depicted in Fig . 7c whether the degree of connectivity between neurons is statically or dynamically stable ., That is , the stability of the mean could be due to slowly changing connectivity , or connectivity could be changing rapidly while maintaining a near constant mean value ., This question was addressed by examining which and how often synapses transitioned between strong and weak ( see Methods ) ., The occurrence of these so-called “flips” is summarized in Fig . 8 , showing that connectivity is most likely the result of rapidly changing synapses that combine to a mean value that changes over a slower time-scale ., Here we show how synaptic plasticity allows neuronal networks to attain a number of desirable network dynamics and properties ., First , it helps to produce PABA , persistent asynchronous background activity , which acts as a foundation for more specific behavior ., Second , this foundational activity takes on characteristics of so-called critical networks ., Lastly , synaptic plasticity enables the critical network , once established , to remain critical in the face of perturbations ., Spiking in the simulated network is only weakly correlated and irregular , shown by examining pairwise correlations between spike-trains and coefficient of variation within spike-trains ., By restricting the range of ISIs considered , it can be concluded that spike bursts are responsible for a high coefficient of variation in otherwise Poisson-like spiking ., Spiking with bursts can result from tonic input 35 , but in this network bursts are favored by a Vreset voltage that is higher than Vrest ., After a spike , membrane voltages are decreased to Vreset , which gives integration a head-start toward reaching the spiking threshold ., This mechanism produces spikes on a time-scale near the refractory period of the neuron ( see Fig . 12 ) ., Another possible source of bursts , most likely on a longer time-scale , is the action of STDP to create a bimodal distribution of synaptic strengths , which could take the place of manually increasing the J parameter in 35 ., In any case , spike bursts contribute to high dimensional network activity , which increases input separability and dynamical memory capacity35 ., Avalanche size distributions , branching ratio , and correlated spike-interval fluctuations leading to DFA α > 0 . 5 show that the apparently irregular spiking activity is consistent with a critical network ., A hallmark of self-organizing systems is a composition of relatively “dumb” units connected together and constrained by “interaction dominant dynamics” 36 ., In the case of the simulated network presented above , the connection strength between units is altered by synaptic plasticity , effectively changing the network topology ., The individual units , however , remain primarily unchanged ., The structure of the network self-organizes such to combine uncorrelated units in a balanced way to produce network-level behavior that meets several criteria for criticality ., This approach is different from other robust , balanced networks that rely on pre-constrained synaptic weights 37 and do not address the more subtle features of criticality , such as power-law avalanche sizes ., In contrast , the network presented here adapts to perturbations with lasting changes to synaptic conductances ( see Fig . 6 ) while maintaining the ability to self-tune towards a critical state ., The balance created is especially evident when following the reaction of the network to a single extra spike ., Since the network dynamics are fully deterministic , we were able to follow two parallel realities for the network: one in which a particular spike occurred and one in which it didn’t ., What results are two spike histories that begin to diverge exponentially , meaning that the network is sensitive to small changes in state ., Since the network is finite , the spike-vector difference settles at a new value near the expected difference for two random spike-vectors ., Balance in the network is also present at the level of excitatory and inhibitory currents ., These currents are observed to balance each other , leaving the resultant current near zero ., The level at which the currents balance is slightly excitatory , which makes sense for a spontaneously active network ., This last type of balance points towards the key mechanism for self-tuning ., There are a limited number of ways that current into a neuron can be altered ., In the present model , those ways are limited to changing synaptic conductance , via STDP , or changing synaptic efficacy , via STP ., It cannot be overstressed that both excitatory and inhibitory long-term plasticity are important , as it is the interplay between these two effects that results in the balanced , critical network achieved above ., Networks without inhibitory STDP fail to reach this state for any of a large set of possible parameters ., Even otherwise balanced networks without inhibitory STDP succumb to runaway positive feedback when stimulated by strong perturbations 38 ., Fig . 9b shows a drift away from criticality after inhibitory STDP is switched off ., A close inspection of the size distributions reveals that they contain a large amount of large avalanches , i . e . global bursts ., Such global bursts tend to interrupt temporal correlations and spatial heterogeneity by globally depleting network resources ., This interpretation is further supported by the finding that while a balance between excitatory and inhibitory current is maintained , the net positive current has increased making the network too excitable ., Our simulations suggest that inhibitory STDP allows the network to respond rapidly enough to transient over-excitability to prevent resource depletions , which is crucial to maintain long-term temporal correlations in the system ., It is not only a practical matter that inhibitory STDP is required , but there are deep connections to self-organizing systems as well ., Self-organization , especially self-organized criticality , is usually the result of two opposing effects , often some mutually-referring function of each other 39–42 ., Here , excitatory and inhibitory STDP play these roles , and together produce various forms of compensatory feedback , depending on temporal differences between pre- and postsynaptic spikes 43 , 44 ., Fig . 11 shows a schematic description of how these two STDP functions combine to create a balanced network , in the hopes of attacking the “how” and “why” of the mechanisms involved ., Further study on these points is required ., We can discriminate roughly 4 different types of feedback depending on these temporal differences ., The inhibitory STDP function is symmetrical , supporting an increase in synaptic conductance , i . e . synaptic inhibition , for closely timed pre- and postsynaptic spikes regardless of their order ., In contrast , the excitatory STDP function is anti-symmetric and biased towards depressing action ., Together , averaged over the firing activity of a network , these two STDP functions combine such that along the Δt = tpre − tpost time-line , there are four qualitative regions: proximal causal and anti-causal , for those spikes that occur relatively close together , and distal causal and anti-causal , for those that occur farther apart ., The difference in symmetry between E-STDP and I-STDP causes these regions to behave asymmetrically at the population level ., In the Balanced regime , causal spikes that occur close to each other lead to a similar strong increase in excitation and inhibition ., In the Accelerated Potentiation regime , where causal spikes occur at larger temporal distance , excitatory potentiation dominates whereas inhibitory STDP is absent or slightly negative ., These leads to a temporal tightening of these causal spikes ., In contrast , Pruning affects anti-causal spikes that are close in time ., The decrease in excitatory drive and strong increase in inhibition for these spike pairs should loosen their temporal tightness and greatly reduce their probability of occurrence ., Finally , in the Decelerated Depression regime , we encounter anti-causal spikes that are far-apart in time ., In this regime E-STDP slightly dominates leading to reduction of the corresponding excitatory synapses , tempered by slight decreases in inhibition ., The combined effect of the symmetry breaking is to foster balance among neurons that form networks of causal spiking , quickly reduce those that are strongly anti-causal , and maintain all other connections at a low , but non-vanishing level ., It is not surprising that inhibitory plasticity leads to balanced networks , as this phenomenon has been shown many times before 22 , 43 , 44 ., The stabilizing nature of inhibitory plasticity is not the main issue here , but rather how that stability allows tuning towards critical spiking behavior ., Furthermore , a stable network is not necessarily a critical one , as stability is necessary but not sufficient for criticality ., Running the parameter search described in Code S1 on networks without plasticity results in some balanced networks that are not critical ., Stability itself might come from or be enhanced by another homeostatic mechanism , such as synaptic scaling 45 , however such mechanisms normally occur on much longer time-scales than the duration of the simulations presented above ., Future investigations could focus on such questions ., The co-existence of PABA and self-tuning to criticality in our model is consistent with several experimental observations of the mammalian cortex ., The cortex is spontaneously active both during development and in a fully developed cortex and this activity is asynchronous with low firing rates 28 ., Neuronal avalanches are observed in animals 1 , humans 7 , 8 , and in vitro cell cultures 4–6 ., This suggests that natural neuronal activity exhibits PABA and has a tendency , on average , to operate close to its critical state ., While the model presented here takes many of its features from biological networks , and as just argued , shares important behaviors as well , there are still many differences ., These differences reflect themselves in the model parameters that best show self-tuning ., For instance , there is empirical evidence that the ratio of τF to τAMPA sits near 20 46 ., For this model , however , that ratio sits near 2 ., As such , this model is not intended to be a biological model in itself , but to encapsulate the driving dynamics for spiking networks that exhibit critical branching and avalanches ., Networks of different size , topology , and with more or fewer biological features are expected to have different parameter sets for optimal self-tuning ., Here , the qualitative dynamics of STDP and STP , and their role in critical networks , have been clarified ., Furthermore , they have been quantified for a specific neuromorphic implementation of an E-I neuronal network using LIF neurons , two different STDP rules , and synaptic short-term depression ., When considering neuromorphic systems in general , biological networks may be taken as a special case ., Discovering which mechanisms in the general case are responsible for desired behavior , such as self-tuning to a critical state , provides insight into their presence in biological systems , but may also help to direct the design of large-scale artificial neuromorphic systems ., The recurrent neuronal network ( Fig . 12a ) consisted of 10 , 000 neurons composed of 8000 excitatory ( E ) neurons and 2000 inhibitory ( I ) neurons with a connection probability of 1% ., Neurons were simulated as single compartments with leaky integrate-and-fire ( LIF ) dynamics ., For this non-conservative network , synaptic input currents were modeled as exponentially decaying functions with a temporal time course approximating excitatory α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid ( AMPA ) and inhibitory gamma-aminobutyric acid ( GABA ) postsynaptic currents ( Fig . 12b ) ., Each combination of pre- and postsynaptic connections based on neuronal type were modeled , resulting in two excitatory ( E → E , E → I ) and two inhibitory ( I → I , I → E ) types of synapses ., All synapses in the model continuously exhibited both short- and long-term plasticity ., A notable aspect of our model is that its dynamics are completely deterministic and there is no explicit source for asynchronous or irregular firing such as external and irregular input or probabilistic synaptic transmission or action potential generation ., Instead , PABA in the model emerged from intrinsic , deterministic dynamics ., As shown , this deterministic design of the network allows for a detailed and precise analysis of the network response to extremely small changes such as the addition or removal of a single spike ., Two types of plasticity were implemented in the network ., Short-term plasticity ( STP ) , which transiently changes synaptic efficacy as a function of spike frequency in the presynaptic neuron ( Fig . 12c ) was simulated using a phenomenological model 47 , 48 that combines short-term synaptic facilitation and depression ., Long-term synaptic plasticity was implemented based on spike timing-dependent plasticity ( STDP ) rules at the level of network connectivity ., In STDP , the temporal relationship between the arrival of synaptic input at a postsynaptic neuron and the action potential generation in the presynaptic neuron determines the magnitude and direction of the change at that particular synapse 22 , 49–52 ., We implemented STDP for excitatory synapses using the well established asymmetrical function ( Fig . 12d ) ., For inhibitory synapses ( Fig . 12e ) , we used a recently reported symmetrical function 34 , 53 ( Fig . 12e ) ., We first performed a parameter search ( see Discussion for comments about self-tuning ) to identify neuronal networks that exhibit PABA with relatively low firing rates when compared to other simulated networks of this type , e . g . 17 ., The role of a parameter search in a model that claims to be self-tuning is worthy of further discussion ., At first , any parameter search seems to be at odds with the concept of “self-tuning” ., The search , however , is at a broad level that identifies networks that subsequently have the self-tuning property ., That is , there are at least two levels at which manual parameter tuning might take place ., Here , a parameter search is performed at one level , such that no manual tuning is needed at the other level ., The benefit provided by this process is that the parameter tuning that is done is not problem specific , but yields networks that self-tune at the problem level ., While plasticity allows some amount of self-tuning , the parameters of a network must be within particular ranges for it to do so ., To find reasonable values for the parameters in Table 1 , we employed a biased random-walk searching algorithm ., This search was primarily used to find networks with lower firing rates than would otherwise have occurred for random or arbitrary selection of parameters , as well as lower firing rates than other simulated networks of this type , e . g . 17 ., As such , recent ( r ) and maximum ( rmax ) firing rates were placed into a rate performance index , R r a t e = r 2 + r m a x 2 ., The eight parameters comprising the search space were: four parameters for STDP ( g e x c m a x , g i n h m a x , β , A+ ) , two STP time constants for facilitation ( τF ) and depression ( τD ) , and two receptor kinetic parameters τAMPA and τGABA for the neuron 48 ., Networks with various combinations

Introduction, Results, Discussion, Materials and Methods

During rest , the mammalian cortex displays spontaneous neural activity ., Spiking of single neurons during rest has been described as irregular and asynchronous ., In contrast , recent in vivo and in vitro population measures of spontaneous activity , using the LFP , EEG , MEG or fMRI suggest that the default state of the cortex is critical , manifested by spontaneous , scale-invariant , cascades of activity known as neuronal avalanches ., Criticality keeps a network poised for optimal information processing , but this view seems to be difficult to reconcile with apparently irregular single neuron spiking ., Here , we simulate a 10 , 000 neuron , deterministic , plastic network of spiking neurons ., We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic , i . e . self-sustained , asynchronous spiking ., Brief external perturbations lead to adaptive , long-term modification of intrinsic network connectivity through long-term excitatory plasticity , whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state ., The critical state is characterized by a branching parameter oscillating around unity , a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0 . 5 and 1 .

Neural networks , whether artificial or biological , consist of individual units connected together that mutually send and receive parcels of energy called spikes ., While simply described , there is a vast space of possible implementations , instantiations , and varieties of neural networks ., Some of these networks are critically balanced between randomness and order , and between death by decay and death by explosion ., Selecting just the right properties and parameters for a particular network to reach this critical state can be difficult and time-consuming ., The strength of connections between units may change over time via synaptic plasticity , and we exploit this mechanism to create a network that self-tunes to criticality ., More specifically , the interplay of opposing forces from excitatory and inhibitory plasticity create a balance that allows self-tuning to take place ., This self-tuning takes relatively simple spiking units and connects them in a way that creates complex behavior ., Our results have implications for the design of artificial neural networks implemented in hardware , where parameter tuning can be costly , but may provide insight into the critical nature of biological networks as well .

journal.ppat.1005226

Memory Th1 Cells Are Protective in Invasive Staphylococcus aureus Infection

Staphylococcus aureus is a leading cause of community- and hospital-acquired bacterial infections ., It is one of the most common causes of bloodstream infection ( BSI ) , and carries a higher mortality than any other bacteraemia ( 20–40% within 30 days ) despite appropriate treatment 1 ., It is also the leading cause of other serious infections including osteomyelitis , septic arthritis , endocarditis and device-related infections , and leads to significant healthcare costs 2 ., The burden of S . aureus disease is amplified by the fact that resistance has been demonstrated to every licensed anti-staphylococcal agent to date 3 ., Consequently , there is an urgent unmet clinical need to develop a vaccine against S . aureus ., Several model vaccines have shown an ability to prevent or attenuate S . aureus infection in murine models , but no candidate vaccines have yet shown efficacy in human clinical trials ., Progress towards an efficacious S . aureus vaccine has been significantly compromised because murine models do not sufficiently recapitulate human exposure to S . aureus 4 ., Mice are not natural hosts for S . aureus–they require high bacterial inocula to establish systemic disease and , critically , are immunologically naïve on first infection ., In contrast , humans are imprinted with an immune response following multiple exposures to S . aureus , and its successful commensalism facilitates an intimate association with the host immune system that has enabled the evolution of multiple bacterial mechanisms to evade innate and adaptive immunity 5 ., S . aureus vaccine development is significantly impeded by a fundamental lack of understanding of the correlates of immune protection in humans , and our knowledge of which elements of the immune response are important in recovery from or prevention of human S . aureus infection is extremely limited ., Antibody responses to S . aureus antigens have traditionally been used as biomarkers for vaccine immunogenicity ., However , vaccines that have produced robust humoral immunity have not prevented or attenuated the course of infection in clinical trials , nor has passive immunisation 6 ., This is possibly to be expected as it is unclear whether B cell deficiency states in humans or in mice result in greater incidence or severity of invasive S . aureus disease 6–8 ., Given that S . aureus persistently or transiently colonises most of the population , human serum has a pre-existing and variable repertoire of anti-S ., aureus antibodies 9 ., Limited clinical data suggests that higher levels of anti-toxin antibodies may attenuate disease in S . aureus BSI 10 ., However , it has proven difficult to consistently correlate the presence or titre of anti-staphylococcal antibodies with improved clinical outcomes 11 , 12 ., On the other hand , defects in cellular immunity–both in mice and in humans–are reliably associated with increased risk of S . aureus infection ., There is accumulating evidence that T helper ( Th ) cells play important roles in protection against human S . aureus infection 13 ., Underlying conditions such as cancer , HIV infection , end-stage renal disease and diabetes mellitus present the greatest relative risk for S . aureus BSI acquisition , and these pathologies are all associated with impaired Th1 cell responses 14–17 ., Patients with the rare autosomal dominant hyper-IgE syndrome ( AD-HIES ) are also prone to recurrent staphylococcal skin and lung abscesses 18 ., In these patients , mutations in STAT3 ( signal transducer and activator of transcription 3 ) result in impaired Th17 cell development 18 , while CD4+ cells retain the ability to differentiate into other subsets 19 ., Interestingly , these patients do not seem more prone to S . aureus bloodstream infection , suggesting that a Th17 response is critically important at skin and respiratory sites only 20 ., Other cohorts who present with life-threatening and recurrent S . aureus infections retain normal T helper cell activity , but instead exhibit defects in phagocyte function 21 ., Phagocytes from patients with chronic granulomatous disease ( CGD ) , for example , are unable to generate reactive oxygen species ( ROS ) , which markedly impairs their ability to eliminate certain pathogens , including S . aureus 22 ., Overall , clinical observations suggest that defects in either T helper cell or phagocyte function are associated with increased susceptibility to S . aureus infection ., Importantly , these cell populations are intricately linked ., T cells do not directly kill bacteria but , via cytokines such as IFNγ and IL-17 , they can orchestrate many downstream effects on phagocytes that enhance their microbicidal activity 23 , 24 ., Murine studies have illuminated the protective roles played by individual T cell subsets in S . aureus infection , although there have been some contrasting reports on their importance 25 ., It is likely that the nature of the infecting stain 26 , and the site of infection significantly impact upon the nature of the T cell response elicited 27 , 28 ., T helper cells are crucial to survival following experimental intravenous S . aureus inoculation 8 , and a protective role for Th1 cells is suggested by the finding that IFNγ-deficient mice are hypersusceptible to such infection 29 ., IL-17 has also been identified as an important correlate of immune protection ., We have previously demonstrated that IL-17-producing γδ+ T cells were protective in a peritonitis model of systemic S . aureus infection 30 ., Mice deficient in IL-17A and IL-17F had increased susceptibility to opportunistic mucocutaneous S . aureus infection compared to wild type mice , but no difference in pathology was observed following systemic infection 31 ., This mucocutaneous site-specific protective role for IL-17 during S . aureus infection parallels the clinical picture in patients with IL-17 deficiencies 20 ., Evidence from animal models , in conjunction with clinical observations in patients particularly susceptible to S . aureus infection , implies that an intact pathway of S . aureus-responsive T cells mediating phagocyte activation via pro-inflammatory cytokine signalling ( IFNγ ± IL-17 ) is essential to S . aureus immunity ., Accordingly , model vaccines based on this premise of cell-mediated immunity have succeeded in generating antibody-independent protection against systemic infection in mice , providing there is sufficient activation of effector T cell subsets 8 , 32 ., Th1 and Th17 cells seem necessary to mediate protection in certain vaccines , and achieve this by enhancing phagocyte recruitment and activation 29 , 33 ., Consequently , T cell subsets are now being investigated as potential targets for human immunisation ., Surprisingly , in the case of previously studied anti-S ., aureus vaccines , cellular immune responses were not assessed prior to entering clinical trials and preclinical findings demonstrating the importance of cell-mediated immunity have thus far not been translated into human studies 4 ., This has surely contributed to the failure of previous vaccines–especially in light of recent findings which suggest that the majority of adults possess significant levels of circulating antigen-specific memory T cells , indicative of their prior exposures to this organism 34 ., A more comprehensive understanding of the role played by specific T cell subsets in site-specific clearance of infection is urgently required to inform development of vaccines that can efficiently promote protective immunity to S . aureus ., Here , we examine the development and function of S . aureus antigen-specific T helper cells in mice and , for the first time , translate these findings to humans by profiling memory T cell responses during S . aureus bloodstream infection ., We show that exposure to S . aureus , in both mice and humans , critically imprints a Th1 cell response ., In mice , antigen-specific Th1 cells conferred protection , in part , by promoting macrophage activation–identifying an underappreciated role for these phagocytes in facilitating S . aureus clearance ., Finally , we generate a novel Th1-inducing S . aureus vaccine comprised of a S . aureus antigen with the capacity to activate human Th1 cells combined with a potent Th1-inducing adjuvant , and demonstrate the effectiveness of this vaccine in conferring Th1-mediated protective immunity during systemic S . aureus infection in mice ., Using a previously described 30 murine model of recurrent S . aureus exposure , followed by a period of recovery prior to subsequent challenge , we identified that local production of IFNγ in the peritoneal cavity was significantly increased soon after challenge of prior exposed mice , while it remained undetectable in naïve mice infected for the first time ( Fig 1A ) ., Twenty percent of CD4+ and 10% of CD8+ T cells in the peritoneal cavity were making IFNγ ( Fig 1B ) ., We have previously shown that γδ+ T cells were not contributing to IFNγ production in this model 30 ., Similarly , there was no evidence for IFNγ production by NK cells ., Thus , CD4+ T cells were the primary source of IFNγ in the prior exposed mice ., CD4+ T cells–but not CD8+ T cells–with the capacity for IFNγ production were sustained in the peritoneum for up to 5 d post-challenge ( S1A Fig ) ., CD4+IFNγ+ ( Th1 ) cells were evident in the spleen by 12 h post-challenge , with no similar systemic expansion of CD8+ cells ( S1B Fig ) ., Neither CD4+ nor CD8+ IFNγ-producing cells were found in the draining mediastinal lymph nodes ( MLN ) ., With the exception of TNFα , innate cytokine secretion ( IL-1β , IL-23 , and IL-12 ) in the peritoneal cavity did not differ between naive and prior exposed mice at 3 h post-challenge ( S1C Fig ) ., IL-1α and IL-18 were also undetectable in both groups of mice ., This suggests that the enhanced T cell production of IFNγ observed in prior exposed mice was not directly related to increased innate cytokine signalling , and indeed these Th1 cells may contribute to the elevated levels of TNFα observed ., Repeated exposure to S . aureus preferentially enhanced local and systemic Th1 ( CD4+IFNγ+ ) responses , which likely contributed to the accelerated bacterial clearance seen during subsequent S . aureus infection 30 ., We have previously shown that IL-17 levels are also significantly elevated in previously exposed mice and that γδ+ T cells , as opposed to CD4+ T cells , are the primary source 30 ., The Th2 cytokine IL-4 was undetectable in both groups of mice ., To establish whether S . aureus antigen-specific Th1 cells are capable of conferring protection against infection , we expanded S . aureus antigen-specific T cells under Th1-polarising conditions in vitro and then adoptively transferred these cells to naïve mice ., Prior to transfer it was confirmed that these Th1-polarised cells were producing significant levels of IFNγ and only background levels of other cytokines ( S2A Fig ) , and greater than 99% of the CD4+ T cells transferred were viable ., S . aureus antigen-specific Th1 cells , or an equivalent number of naïve non-polarised non-antigen-specific T cells , were then adoptively transferred to naïve syngeneic hosts prior to intraperitoneal ( i . p . ) S . aureus challenge ., At 24 h , there was no difference in bacterial burden in the peritoneal cavity ( 5 . 7 log10 CFU/ml versus 6 . 1 log10 CFU/ml ) or kidney ( 4 . 4 log10 CFU/ml versus 4 . 8 log10 CFU/ml ) between S . aureus Th1 cell transfer and control cell transfer groups , respectively ., However , S . aureus-specific Th1 cell transfer significantly reduced bacterial burden ( ~2 log10 reduction ) in the peritoneal cavity at 72 h post-challenge as compared with transfer of control T cells ., Dissemination of bacteria to the kidneys and spleen was also reduced in recipients of S . aureus antigen-specific Th1 cells at 72 h post-challenge ( Fig 2A ) ., OVA-specific Th1 cells were polarised under similar conditions , and produced significant levels of IFNγ ( 41 ng/ml ) , but minimal IL-17 , in vitro ., Importantly , transfer of these OVA-specific Th1 cells did not confer protection against systemic dissemination of S . aureus , illustrating the specificity of the response ( S2B Fig ) ., To establish a mechanism for the Th1-conferred protective effect , we assessed phagocyte responses at the site of infection ., Transfer of S . aureus antigen-specific Th1 cells did not affect local production of the neutrophil-recruiting chemokine CXCL1 ( Fig 2B ) , but was associated with a significant elevation in CCL5 secretion in the peritoneal cavity early in the course of infection ( Fig 2C ) , inferring a bias towards monocyte and memory T cell recruitment ., The effect of local chemokine production on downstream phagocyte influx was then established ., Macrophage ( CD11b+F480+Ly6G- ) recruitment to the site of infection was significantly elevated in Th1 transfer recipients by 24 h post-infection ( Fig 2D ) , with the number of MHC II-expressing macrophages also increased ( Fig 2E ) ., Consistent with the CXCL1 result , neutrophil ( CD11b+F480-Ly6G+ ) recruitment to the peritoneal cavity was not significantly altered in the Th1 transfer recipients as compared to the control mice ( Fig 2F ) ., Transfer of OVA-specific Th1 cells did not result in increased activation of recruited macrophages , as evidenced by comparable MHC II expression to PBS-treated controls ( S2C Fig ) ., To confirm that the protective effects observed in the Th1 recipient mice were exclusively mediated by IFNγ-producing cells , we demonstrated that local IL-17 production was not increased in the Th1 recipient mice as compared to control mice ( S2D Fig ) ., Furthermore , transfer of S . aureus antigen-specific Th17 cells ( S3A Fig ) did not reduce bacterial burden at any site in the recipient mice ( S3B Fig ) ., Taken together , these results suggest that the presence of S . aureus antigen-specific Th1 cells promotes monocyte/macrophage activation , facilitating accelerated clearance of infection at local and systemic sites ., To further investigate the role of macrophages in the immune response against S . aureus , we depleted peritoneal macrophages using clodronate-loaded liposomes prior to infection ., This treatment completely depleted macrophages in the peritoneum by 24 h post-administration , compared to control mice injected with PBS-loaded liposomes or PBS alone ( Fig 3A ) ., Following S . aureus infection , macrophages were rapidly recruited to the peritoneum in control mice , whereas no macrophage recruitment was observed in clodronate-treated mice ., Conversely , significant peritoneal neutrophil recruitment was observed in the first 24 h post-treatment with either liposomal clodronate or liposomal PBS ( Fig 3B ) ., While neutrophil numbers in liposomal PBS-treated mice had returned to PBS control levels 24 h post-S ., aureus challenge , neutrophils in clodronate-treated mice remained significantly higher than control mice until 72 h post-infection ( Fig 3B ) ., Despite this elevated neutrophil response , the macrophage-depleted animals demonstrated a significant impairment in their ability to control S . aureus infection ., By day 5 post-challenge , bacterial burdens in the peritoneal cavity and in the kidneys were significantly elevated as compared to PBS-treated controls ( Fig 3C ) ., In addition , 26% of macrophage-depleted mice subsequently infected with S . aureus had died by 72 h post-challenge ., There were no deaths in the liposomal PBS or PBS-treated cohorts ., Having shown that macrophage responses were crucial to controlling S . aureus infection , we next looked to confirm that S . aureus-specific Th1 cells were positively associated with this macrophage recruitment or activation ., We adoptively transferred S . aureus-specific Th1 cells to groups of macrophage-depleted ( clodronate-treated ) mice , before S . aureus challenge ., In the presence of clodronate liposomes , Th1 cells failed to control systemic bacterial infection ., Fifty percent of the mice had died and there were significantly elevated levels of bacteria present in the kidneys ( of surviving mice ) at 72 h post-bacterial challenge , compared to PBS-treated controls ( Fig 3D ) ., Together , these results highlight the critical role of macrophages in containing S . aureus infection and suggest that macrophages play an active role in direct bacterial clearance ., We have recently demonstrated that macrophages possess a significant capacity for direct killing of S . aureus 35 , and our preliminary experiments have shown that treatment of murine peritoneal macrophages with recombinant IFNγ enhanced their capacity to kill S . aureus in vitro 64 . 6% vs 81 . 37% killing of S . aureus strain PS80 ( multiplicity of infection 1 ) . Human adaptive cellular immune responses to S . aureus infection have not been previously examined . To investigate whether our murine findings translated to humans , thirty-five immunocompetent adult patients with bloodstream infection were recruited between February 2013 and November 2014 from three tertiary care centres . Twenty-four S . aureus ( SA ) and eleven Escherichia coli ( EC ) BSI patients who met inclusion criteria ( S1 Table ) consented to participate . Patient demographics are listed in Table 1 . Initial cytokine analysis of serum from BSI patients demonstrated that IFNγ was detectable in 60% ( n = 15 ) of SA BSI and only 18% of EC BSI patients ( p = 0 . 03 ) . SA BSI patient serum showed significantly higher levels of IFNγ than EC patients ( Fig 4A ) . Low levels of IL-17A were detectable in the sera of a minority of patients , but these levels did not differ significantly between groups ( Fig 4B ) . Having observed that signature T cell cytokines were present and elevated in some SA BSI patients , we proceeded to establish if antigen-specific T helper cell populations were expanded . This strategy may be more sensitive than serum ELISA at detecting differences as it can discriminate cytokine production at a single-cell level . Human T cell proliferative and cytokine responses are known to be significantly reduced following sepsis 36 , and as such healthy volunteers may be a suboptimal control group for comparing antigen-specific responses in infected patients ., To confirm this , we measured proliferative responses to a superantigen ( staphylococcal enterotoxin A , SEA ) in a subgroup of patients and healthy volunteers ., Non-specific CD4+ T cell proliferation was significantly reduced in SA and EC BSI patients compared to healthy volunteers ( S4A Fig ) ., Consequently , the E . coli BSI cohort was chosen as the more appropriate control group to establish antigen-specific T cell responses during BSI , despite some expected clinical differences between the groups ., Whole-genome sequencing of S . aureus bloodstream isolates showed that clonal complex ( CC ) 5 accounted for the biggest group of isolates ( 31 . 8% , n = 7 ) , followed by CC22 ( 22 . 8% , n = 5 ) and CC1 ( 13 . 6% , n = 3 ) ., The majority ( 83% , n = 20 ) of BSI strains were methicillin-sensitive ( MSSA ) ., All cases of methicillin-resistant SA BSI were healthcare-associated and belonged to CC22 ., The clinical S . aureus bloodstream isolates displayed notable genetic diversity , and also differed from the two laboratory reference strains ( S5 Fig ) ., S . aureus antigen-specific CD4+ T cell responses were determined by incubating CFSE-labelled PBMCs with each patient’s own infecting strain and a panel of reference strains of heat-killed S . aureus , resulting in a challenge of four or five different CC types per patient ., Despite this diverse challenge , intra-individual patient responses to the various S . aureus strains did not differ significantly ( S2 Table ) , reflecting the fact that most potential epitopes are likely conserved across strains ., Therefore results are presented as pooled responses to all strains of heat-killed S . aureus ., The proportion of CD4+ T cells proliferating in response to in vitro S . aureus re-stimulation was significantly elevated in the SA BSI patients compared to the EC BSI patients ( Fig 5A ) ., In addition , the proportion of CD4+ T cells showing both proliferation and secretion of IFNγ was significantly greater among SA patients , and S . aureus antigen-specific Th1 responses were almost completely lacking in EC BSI patients ( Fig 5B ) ., Interestingly , in a subset of patients approximately 14% of the proliferating Th1 ( IFNγ+CD4+ ) cells were co-producing TNFα , suggesting that polyfunctional T cells may be involved in the immune response to S . aureus infection ., S . aureus antigen-specific CD8+IFNγ+ T cells were minimal in both patient groups ( Fig 5B ) ., CD4+ T cell proliferation in response to in vitro stimulation with heat-killed E . coli was minimal and did not differ significantly between the two patient groups ( S4B Fig ) ., Despite the fact that serum IL-17 levels in both groups were low or undetectable , we investigated for the presence of S . aureus antigen-specific Th17 cells ., Th17 cell expansion following S . aureus re-stimulation was significantly lower than the predominant Th1 expansion already described ., However , these S . aureus antigen-specific Th17 cells were preferentially expanded in SA BSI patients and minimally present in EC BSI patients ( Fig 5C ) ., To establish if proliferating cells possessed a memory phenotype we investigated expression of CD45RO–a marker of previously activated or antigen-experienced T cells ., The majority of the proliferating antigen-specific CD4+ T cells present were CD45RO+ ., In SA BSI patients , the proportion of these cells that produced IFNγ was significantly elevated as compared to the EC BSI patients ( Fig 6A ) ., Taken together , these results demonstrate for the first time that invasive S . aureus infection in humans predominantly expands a population of memory Th1 cells ( with Th17 cells expanded to a lesser extent ) ., These cells are primed and expanded in vivo in patients with recent bloodstream infection and thus may play a role in recovery ., The staphylococcal cell wall-anchored protein clumping factor A ( ClfA ) has been proposed as a promising target for inclusion in multivalent vaccines given its importance as a virulence factor and its ubiquitous expression among clinical isolates 37 ., Whole-genome sequencing revealed the presence of the clfA gene in all S . aureus bloodstream isolates in our patient cohort ., We then confirmed that ClfA was expressed by the four reference S . aureus strains used in our patient assays ( S6A Fig ) , and remained present on the bacterial cell surface after heat-killing ( S6B Fig ) ., This indicated that the antigen-specific T cell response seen in patients could be at least partially attributable to ClfA ., In our SA BSI patient cohort , serum anti-ClfA antibodies were elevated more than in the EC BSI group , validating its humoral immunogenicity ( Fig 6B ) ., To confirm that ClfA had capacity for human T cell activation , CD4+ T cells were isolated from buffy coats of healthy adult blood donors and co-cultured with autologous irradiated APCs in the presence or absence of antigen ., Given that the normal healthy population have multiple prior exposures to S . aureus , immune recall responses to staphylococcal antigens are expected but are likely to vary significantly , depending on individuals’ exposure histories ., Indeed , humoral responses ( in healthy volunteers and patients with S . aureus BSI ) 9 and cellular responses ( in healthy volunteers only ) 34 have been demonstrated to a limited number of antigens ., Thus , the use of buffy coats presents a valuable opportunity to screen antigens for T cell-activating capacity ., Stimulation with purified ClfA induced antigen-specific CD4+ T cell expansion of a similar order to that of heat-killed S . aureus in 71% of individuals , while 88% of individuals were in possession of T cells capable of responding to heat-killed S . aureus ( Fig 7A ) ., Substantial IFNγ secretion was also seen in response to stimulation by ClfA and heat-killed S . aureus ( Fig 7B ) , and we confirmed that a proportion of CD4+ T cells were both proliferating and producing IFNγ in response to ClfA ( Fig 7C ) ., In this assay system , ClfA-specific Th17 cells were not evident by flow cytometry and IL-17 was not detectable in the culture supernatant by ELISA ., Having confirmed the human T cell-activating potential of the S . aureus surface antigen ClfA , we designed a model S . aureus vaccine comprising ClfA formulated with CpG as an adjuvant to induce Th1 responses ., Groups of naïve mice were vaccinated with CpG alone , CpG+ClfA , or PBS alone ., ClfA-specific humoral and T cell responses were assessed post-vaccination ., ClfA+CpG drove a marked increase in IFNγ production by inguinal lymph node ( ILN ) cells following 72 h in vitro restimulation with ClfA ( 10μg/ml ) ( 51 vs . 16 pg/ml , for ClfA+CpG vs . CpG-vaccinated mice respectively ) and spleen cells ( 219 vs . 2 pg/ml for ClfA+CpG vs . CpG-vaccinated mice ) , with an IL-17 response in ILN cells only ( 84 vs . 22 pg/ml for ClfA+CpG vs . CpG-vaccinated mice ) ., Anti-ClfA IgG titres were significantly elevated in the sera of ClfA+CpG-immunised mice as compared to sham-immunised ( PBS alone or CpG alone ) mice ( Fig 8A ) ., Neutralising antibodies were also present in the sera of immunised mice and effectively inhibited the binding of ClfA to fibrinogen ( Fig 8B ) ., Having confirmed that immunisation with ClfA+CpG successfully induced antigen-specific Th1 cellular and humoral immunity , we sought to establish if such vaccine-induced immunity could protect against S . aureus challenge ., The efficacy of this vaccine against systemic S . aureus infection was shown by the significant reduction in bacterial burden at 72 h post challenge at the peritoneal infection site ( ~2 log10 ) , as well as systemically in the kidneys ( ~2 . 5 log10 ) and spleen ( ~1 log10 ) in mice that were immunised with ClfA+CpG as compared to those that received antigen alone , adjuvant alone or vehicle ( Fig 9 ) ., No protection was seen at 24 h post-challenge in the peritoneal cavity ( 4 . 5 log10 CFU/ml versus 5 . 2 log10 CFU/ml ) or kidney ( 3 . 5 log10 CFU/ml versus 3 . 7 log10 CFU/ml ) in ClfA+CpG immunised as compared to control mice , respectively ., To determine if this vaccine-induced protection was associated with an increased Th1 response , the cells infiltrating the peritoneal cavity post-infection were assessed ., CD4+IFNγ+ cells were significantly increased in the peritoneal cavities of ClfA+CpG-immunised mice compared to control groups at both 24 and 72 h post-challenge ( Fig 10A ) ., CD8+IFNγ+ cells were increased in these mice by 72 h ( Fig 10B ) ., The memory phenotype of these responding Th1 cells was confirmed by their expression of CD44 and CD62L –effector memory cells being identified as CD44hiCD62Llo and central memory cells as CD44hiCD62Lhi ., Thus , ClfA+CpG vaccine recipients showed a marked Th1 total memory ( CD44hi ) response at the site of infection during S . aureus challenge , with a prevailing central memory phenotype ( Fig 10C ) ., Although a transient increase in local Th17 responses was observed in the immunised group this was not significantly different to control groups ( Fig 10D ) ., Finally , we established the downstream effects of this protective Th1 immunity ., While CCL5 production in the peritoneal cavity was slightly increased in the vaccinated mice ( Fig 10E ) , this did not reach statistical significance and notably did not translate to an increase in total macrophage influx to the peritoneal cavity ( Fig 10F ) ., Similar to what was observed in the Th1 adoptive transfer studies this Th1-inducing vaccination strategy had no effect on neutrophil recruitment ., Importantly , however , the macrophages present at the site of infection displayed increased activation ., The number of infiltrating macrophages expressing MHC II was increased in the CpG+ClfA vaccine recipients , suggesting an activated phenotype ( Fig 10G ) ., Using 123-dihydrorhodamine staining to measure ROS in a subgroup of mice , significantly elevated respiratory burst activity was seen in macrophages ( Fig 10H ) –but not in neutrophils ( 68 . 6% vs 65 . 7% for ClfA+CpG vs CpG only groups , respectively ) –of vaccine recipients compared to control groups at 72 h post-challenge ., Taken together , these results demonstrate that vaccination with ClfA+CpG can drive Th1 responses , which in turn enhance macrophage effector functions to accelerate clearance of S . aureus infection ., This study demonstrates , for the first time , a correlate of protective immunity in S . aureus infection in mice that is also evident in human infection ., S . aureus antigen-specific IFNγ-producing CD4+ ( Th1 ) cells are expanded in both humans and mice following infection , and these cells are definitively protective in mice ., This protection is manifested as a significantly accelerated clearance of bacteria and mediated–at least in part–by enhanced macrophage responses ., We have utilised this information in rational vaccine design , and generated a novel S . aureus vaccine to specifically target memory Th1 cells ., Critically important in that process was establishing that our chosen antigen can induce a human Th1 response , and then combining this antigen with a proven Th1-driving adjuvant to enhance anti-S ., aureus Th1 protective immunity ., Immunological memory is induced by S . aureus exposure during infection or colonisation ., Analysis of this memory response has largely focused on S . aureus-specific antibodies to date , but induction of an antibody response alone may be incapable of mediating full protection against S . aureus infection ., Clinical observations and data from experimental models now imply that cellular memory responses play a prominent role in anti-staphylococcal immunity 13 ., While immunological memory may not equate to complete protection against re-infection , it may influence and improve outcome during subsequent infection ., A clinical study intriguingly showed that prior exposure to S . aureus via nasal carriage was associated with a lower all-cause and S . aureus-attributable mortality in BSI , as compared with non-colonised individuals 38 ., The mechanism behind such protection was not identified and whether it might indicate antigen-specific T cell memory and immunomodulation during invasive infection is unknown ., We have previously shown that S . aureus exposure can favourably modulate T cell responses to improve outcomes during later infection ., Using a model of recurrent exposure followed by subsequent challenge , we demonstrated that prior exposure to S . aureus accelerates clearance of infection in murine S . aureus peritonitis 30 ., Acquired immunity in this model was associated with the expansion of a population of memory γδ+ T cells exclusively producing IL-17 , but no detectable IFNγ ., We now demonstrate that prior exposure to S . aureus also induces an antigen-specific CD4+IFNγ+ ( Th1 ) memory response that may also contribute to protective memory ., Adoptive transfer of S . aureus-specific Th1 cells conferred significant protection during systemic challenge of naïve mice potentially by enhancing macrophage effector function , leading to improved clearance of bacteria ., Macrophage depletion increased mortality and appeared to result in increased bacterial dissemination ., Importantly , the protection seen following transfer of S . aureus-specific Th1 cells occurred in the absence of an enhanced IL-17 response ., Furthermore , transfer of S . aureus-specific Th17 cells failed to confer any significant protection against systemic S . aureus challenge ., Considering the results of our previously published study 30 together with our current findings , it appears that in the context of S . aureus systemic peritonitis , protection induced by prior exposure is associated with both type 1 immunity and IL-17+γδ+ T cell responses ., In contrast , during primary infection , an IL-17+γδ+ T cell response appears to predominate 30 , with no detectable

Introduction, Results, Discussion, Materials and Methods

Mechanisms of protective immunity to Staphylococcus aureus infection in humans remain elusive ., While the importance of cellular immunity has been shown in mice , T cell responses in humans have not been characterised ., Using a murine model of recurrent S . aureus peritonitis , we demonstrated that prior exposure to S . aureus enhanced IFNγ responses upon subsequent infection , while adoptive transfer of S . aureus antigen-specific Th1 cells was protective in naïve mice ., Translating these findings , we found that S . aureus antigen-specific Th1 cells were also significantly expanded during human S . aureus bloodstream infection ( BSI ) ., These Th1 cells were CD45RO+ , indicative of a memory phenotype ., Thus , exposure to S . aureus induces memory Th1 cells in mice and humans , identifying Th1 cells as potential S . aureus vaccine targets ., Consequently , we developed a model vaccine comprising staphylococcal clumping factor A , which we demonstrate to be an effective human T cell antigen , combined with the Th1-driving adjuvant CpG ., This novel Th1-inducing vaccine conferred significant protection during S . aureus infection in mice ., This study notably advances our understanding of S . aureus cellular immunity , and demonstrates for the first time that a correlate of S . aureus protective immunity identified in mice may be relevant in humans .

Staphylococcus aureus is a leading cause of skin , soft tissue and bone infections and , most seriously , bloodstream infection ., When S . aureus does get into the bloodstream , it is more likely to kill than any other bacterial infection , despite all the treatments modern medicine has to offer ., It has thus far developed resistance to all antibiotics licensed to treat it ., Thus , there is an urgent need to develop a vaccine against S . aureus ., However , such a vaccine remains elusive ., This is largely due to the fact that we have a very limited understanding of how our immune system fights this infection ., Here , we examine how certain T cells of the mouse immune system effectively recognise and respond to S . aureus , and show that transferring these cells to other mice improves their ability to clear infection ., We then demonstrate that a vaccine which drives these specific T cells also improves clearance of infection ., Until recently , it was not known if human T cells could recognise and respond to S . aureus ., Here we show , for the first time , that these cells are expanded in patients with S . aureus bloodstream infection , and suggest that they should be targeted in anti-S ., aureus vaccines .

journal.pntd.0004480

Towards Improving Point-of-Care Diagnosis of Non-malaria Febrile Illness: A Metabolomics Approach

The introduction of malaria rapid diagnostic tests ( RDT ) has revealed that febrile illnesses in the tropics and subtropics are more commonly caused by non-malaria pathogens than by malaria 1–5 ., Bacterial bloodstream infections ( BSI ) are considered the most severe non-malaria febrile illness , with mortality rates of 10–25% 6–8 ., The high BSI mortality rates highlight the importance of accurate diagnosis and immediate correct case management , particularly in malaria-endemic regions where clinical presentation of BSI and severe malaria are similar 9 ., However , in most malaria-endemic regions , children with non-malaria severe febrile illness are not easily diagnosed , which reflects a number of deficiencies in current WHO guidelines that still rely largely on malaria diagnostic tests ., First of all , health care workers do not have access to tests to diagnose non-malaria febrile illness , which would inform the most effective treatment ., For instance , BSI diagnosis still relies on traditional microbiology , which requires laboratory infrastructure and highly trained staff ., Even when available , it takes two to three days for a result , which is too slow to timely inform case management ., This can lead to ineffective first-line treatment choices that result in poorer survival outcomes 10 , or alternatively leads to over-treatment with broad-spectrum antibiotic therapy that wastes limited resources and fuels emerging antibiotic resistance 11 ., Secondly , the current guidelines also fail to alert for primary non-malaria febrile illness in patients with asymptomatic or recent malaria that can have a positive malaria RDT 12 ., Finally , the current guidelines fail to recognize concomitant non-malaria febrile illness in patients with severe malaria ., BSI/malaria co-infections affect 5–7% of all children with malaria in Africa , and have a 2–3 fold higher mortality rate compared to patients with malaria alone 6 ., An ideal test to assess severe febrile illness in malaria-endemic settings should detect more than a single pathogen ., In particular , it should provide information on the type of infecting pathogen ( malaria and non-malaria ) in order to immediately inform on the best treatment and referral options ., Such information is unlikely to be captured by a single biological measurement or molecule ( biomarker ) , but is expected to require a combination of molecules ( signature ) ., With the development of ‘omics’ profiling approaches it is now possible to measure hundreds of biological analytes simultaneously and thus enable assessment of the diagnostic performance of a molecular signature ., This study utilized metabolomics , which in contrast to studies of DNA , RNA and proteins , enables characterization of the metabolome that is the final product of all cell regulatory processes ., Hence , a metabolome profile provides a read-out of the ( patho ) physiological status of a patient at the time of sampling that cannot be obtained directly from the genome , transcriptome , or proteome ., In addition , metabolomics analyses only require small sample volumes ( 20–50 μL ) to determine a comprehensive metabolome profile , which is a particular advantage when studying pediatric populations ., We hypothesized that the pathophysiological processes triggered by malaria and non-malaria febrile illness induce distinctive changes in the > 4 , 000 blood metabolites and explored for the first time whether such characteristic metabolites can be used for differential diagnosis of severe febrile illness ., The study was conducted according to the principles expressed in the Declaration of Helsinki and was approved by the national ethics committee of Burkina Faso , the institutional review board of the Institute of Tropical Medicine Antwerp , the ethics committee of the University Hospital of Antwerp and the human research ethics office of the University of Western Australia ., Written informed consent was given by all parents or guardians of enrolled children ., Patients were recruited at the district hospital Centre Médical avec Antenne Chirurgicale Saint Camille in malaria-endemic Nanoro , Burkina Faso ., Admitted children < 15 years of age presenting with axillary temperature ≥ 38°C , or clinical signs of severe illness were enrolled ., Medical history , physical examination and outcome of febrile episode were registered on a standardized form ., At time of hospital admission , venous whole blood for blood culture , malaria diagnosis , full blood count , glucose measurements , plasma metabolome analysis , and 16S rRNA deep sequencing were collected from all participants by trained study nurses ., Details of sample processing and diagnostic procedures are provided in S1 Methods ., Malaria was defined as the presence of asexual Plasmodium falciparum parasites in blood smear confirmed by microscopy ., All patients with a negative blood smear were classified as non-malaria ., Recent malaria was defined as positive malaria HRP-2 RDT ( which can remain positive up to 6 weeks after successful treatment ) , but a negative blood smear 13 , 14 ., Confirmed BSI was defined as the growth of clinical significant organisms from blood culture and/or reproducible detection of clinical significant organisms in two 16S deep sequencing experiments ., Patients for which 16S deep sequencing could not be performed ( n = 5 ) were classified as ‘BSI diagnosis incomplete’; and patients with unusual but possible clinically relevant bacteria ( n = 4 ) were classified as ‘possible BSI’ ., Severe anemia was defined as patients with hemoglobin levels < 5 g/dL ., Patients who died in hospital following admission for severe febrile illness were classified as non-survival ., Mass spectrometry-based metabolomics data were collected for all patients with 3 complementary analytical platforms: gas chromatography mass spectrometry ( GC-MS ) , C8 column liquid chromatography mass spectrometry ( C8-LC-MS ) in positive ionization mode , and C18 column ultra-high pressure liquid chromatography mass spectrometry ( C18-UHPLC-MS ) in positive and negative ionization mode ., These three analytical techniques each capture a different part of the plasma metabolome due to the different chromatography column chemistries used , and thus provide a very broad coverage of the metabolome ., Detailed procedures for sample preparation and data acquisition are provided in S1 Methods ., Raw data were processed with dedicated data processing pipelines , which are described in S1 Methods ., All metabolite data has been deposited in the metabolomics data repository , MetaboLights ( study identifier MTBLS315 ) ., All data processing and statistical analyses were performed using the R software environment ( version 3 . 1 ) ., This language comprises a selection of packages suitable for the implemented statistical methods: multivariate analysis ( partial-least-squares ( PLS ) regression ) , receiver-operating curve ( ROC ) analysis , correlation analysis , hierarchical cluster analysis , biomarker validation and Bayesian latent class modeling ., The specific statistical methods and R packages used are explained in S1 Methods , the R scripts can be obtained upon request from the authors ., A total of 2 , 635 reproducibly detected plasma metabolite features showed a minimal degree of variation ( relative standard deviation < 15% ) in the study sample ( Table 3 ) , these features were further analyzed to fathom the metabolic nature of severe febrile illness ., We first investigated with partial-least-squares regression analyses which characteristics of our study participants have a considerable influence on the plasma metabolome composition ( Fig 1 ) ., Of all the tested patient characteristics , the correlated factors age/weight/height had the biggest impact on the measured metabolome ( Q2 = 61 . 5/53 . 1/69 . 3% ) , closely followed by blood glucose level ( Q2 = 48 . 1% ) and disease outcome ( survival Q2 = 46 . 5% ) ., In comparison , the type of infection ( BSI , malaria ) had little impact on the measured metabolome ( Q2 = 18 . 4% and 23 . 1% respectively ) , which does however not preclude the existence of individual metabolite features that are characteristic for the type of infection ., We identified the metabolite features characterizing the following five patient groups with ROC analysis:, ( i ) patients with non-malaria febrile illness ,, ( ii ) malaria patients ,, ( iii ) BSI patients ,, ( iv ) non-survival patients and, ( v ) severe anemia patients ( Table 4 ) ., The details of the metabolite features are provided in S1 Data ., This section focuses on the metabolite characteristics of non-malaria illness , malaria and BSI; the results for non-survival and severe anemia are described in S1 Results ., We identified a group of 10 correlating metabolite features that characteristically appeared in non-malaria patients ( sensitivity range: 0 . 90–0 . 67; specificity range: 0 . 89–0 . 63; Fig 2 ) ., These features include four corticosteroids , of which three were putatively identified as glucocorticoids ., The correlation map shows that these corticosteroids were also markers of non-survival patients ( S1 Results ) ., In addition , the non-malaria features included three highly correlating features of which one was putatively identified as an eicosanoid ( leukotriene F4 ) ., Malaria patients on the other hand were characterized by a higher concentration of 16 lipids , predominantly triglycerides and ether phospholipids ( sensitivity range: 0 . 83–0 . 60; specificity: 0 . 96–0 . 63 ) ., The latter group included four lipids that showed a fairly positive quantitative correlation with Plasmodium parasite density ( Pearson’s correlation: 0 . 38–0 . 45 ) ., The heatmap of the 26 metabolite features characteristic for non-malaria/malaria further clarifies the distinct metabolic character of the two patient groups ( Fig 3 ) ., Non-malaria patients formed two subclusters ., Subcluster A had moderate increases of corticosteroids and predominantly consisted of patients who survived the febrile illness episode , while subcluster B had the highest concentration of corticosteroids seen in our study , and was associated with non-survival ., The malaria patients also grouped in two major subclusters ., Subcluster C was characterized by more moderate increases of the typical malaria lipids compared to subcluster D which may reflect a differential response that varies with age and/or Hb-levels ( higher median age and Hb levels in subcluster C compared to subcluster D ) ., Finally , we expected patients with concomitant malaria and non-malaria febrile illness to have an increase in both the typical malaria lipids/triglycerides and the non-malaria metabolites ., Such a cluster of 8 presumptive co-infection patients was indeed observed ( far right within subcluster D ) , of whom two were confirmed as a BSI/malaria co-infection and one as a recent malaria case with a BSI infection ., BSI patients were found to have eight upregulated lipids , including several bile acids and alcohols , compared to patients without BSI ( sensitivity range: 0 . 83–0 . 67; specificity range: 0 . 91–0 . 73; Fig 4 ) ., We could not find a plausible identification in the checked databases for half of the upregulated features , however given their correlating signal intensity profiles and chromatographic retention-time they were likely biologically and structurally related to the identified bile acids and alcohols ., The plasma concentration of these upregulated bile features did not seem to be related to the particular infecting bacterial species ., The metabolome features that had lower concentrations in BSI patients compared to non-BSI patients were consistent with a lower rate of lipolysis ( e . g . lower concentration of lysophospholipids ) and a disruption of fatty acid ß-oxidation ( e . g . lower concentration of plasma acylcarnitines ) ., We selected a signature of two metabolites for non-malaria patients , and also for BSI patients ., We assessed the diagnostic performance of the sum of the signal intensity of the 2 individual metabolites for non-malaria febrile illness and BSI respectively ( Fig 5 and Fig 6 ) ., The metabolite signature for non-malaria illness consisted of a C21-corticosteroid ( m/z M-H = 377 . 196 , putative ID: C21H30O6 18-hydroxycortisol , ID_2094_ON in S1 Data ) and a steroid glucuronide ( m/z M-H = 481 . 243 , putative ID: C25H38O9 11-beta-hydroxyandrosterone-3-glucuronide , ID_3388_ON in S1 Data ) ., The first steroid had an individual area under the curve or AUC = 0 . 81 ( 95% CI = 0 . 68–0 . 91 ) , adjusted p-value = 0 . 01 , fold change = 4 . 90; while the second steroid had an AUC = 0 . 79 ( 95% CI = 0 . 67–0 . 90 ) , adjusted p-value = 0 . 01 and fold change = 24 . 6 ., The ROC curve analysis for the 2 steroids combined including 57 patients ( excluding confirmed malaria/BSI co-infections ) had a more favorable AUC value of 0 . 82 ( Fig 5A ) ., As shown in the boxplot of Fig 5A , when considering all 61 patients , there were 8 malaria patients falsely classified as non-malaria by this signature , which included six patients that were presumptive malaria/non-malaria co-infections as indicated in subcluster D of Fig 3 ., The non-malaria signature failed to identify six non-malaria patients , including two BSI patients with grown blood culture ., As explained above , when present at high concentrations the same corticosteroid metabolites were predictive of non-survival ., This is confirmed by the ROC-curve analysis on 60 patients ( excluding the single patient who left hospital against medical advice ) which had a very good AUC of 0 . 86 , and a cut-off value that was indeed almost two-fold higher than the cut-off value to predict non-malaria febrile illness ( Fig 5B ) ., A test based on the two combined metabolites missed only 2/15 non-survival patients and thus had a good sensitivity ( 87% ) to predict non-survival ., However the specificity ( 73% ) was rather poor , in our patient sample 12/45 survival patients tested false positive ( Fig 5B ) ., We selected a bile acid ( m/z M+FA-H = 493 . 316 , putative ID: C27H44O5 C27 bile acid , ID_6663_ON in S1 data ) and bile alcohol ( m/z M+FA-H = 483 . 331 , putative ID: C26H46O5 27-Nor-5b-cholestane-3a , 7a , 12a , 24 , 25-pentol bile alcohol , ID_4741_ON in S1 data ) for the BSI signature ., The individual diagnostic performance of the bile alcohol was AUC = 0 . 82 ( 95% CI = 0 . 69–0 . 93 ) , adjusted p-value = 0 . 07 , fold change = 2 . 54; and for the bile acid AUC = 0 . 79 ( 95% CI = 0 . 63–0 . 92 ) , adjusted p-value = 0 . 1 , fold change = 2 . 8 ., The ROC-curve analysis for the 2 bile metabolites combined including 51 patients ( excluding confirmed malaria/BSI co-infections , patients with incomplete or possible BSI diagnosis ) had a good AUC of 0 . 85 ( Fig 6 ) ., The signature falsely classified 4/18 confirmed BSI cases as non-BSI ( Fig 6 ) ., Notably these false positives represented 4/5 BSI patients older than 5 years , suggesting that the signature was most suitable to diagnose BSI in under-fives ., Eight patients ( all malaria patients ) in whom we could not detect BSI were classified by the metabolite signature as BSI ( Fig 6 ) ., It is difficult to conclude whether they were falsely classified by the metabolite signature as BSI-positive , or whether they were misdiagnosed by blood culture/sequencing ., We therefore estimated sensitivities and specificities of the three BSI diagnostic tests with Bayesian latent class models ( Table 5 ) ., Although the confidence intervals were fairly wide given the limited sample size , the results were in line with the expectations ., Regardless which age group we considered , the sensitivity of blood culture ( 50% ) was inferior to 16S sequencing ( 70% ) ., The BSI metabolite signature had the best sensitivity and negative predictive value of the three tests , and reached an impressive 98 . 1% and 98 . 3% respectively when considering under-fives only ., However , the specificity of the BSI signature ( 85 . 8% in all patients , 82 . 9% in under-fives only ) was inferior to that of blood culture and 16S sequencing ( > 95% for both tests ) ., We report here for the first time that malaria and non-malaria severe febrile illnesses each trigger a distinct metabolic host response affecting plasma lipid profiles and this opens new options for differential diagnosis of severe febrile illness ., For BSI , one of the most severe non-malaria febrile illnesses , we identified a simple bile metabolite signature with a superior sensitivity and negative predictive value than the current tests ( blood culture and 16S sequencing ) ., If our findings can be validated in large-scale studies , then such a simple metabolite signature could be the basis of a new rapid diagnostic test that could potentially reduce BSI mortality by facilitating early diagnosis and timely hospital referral of BSI patients , and reduce empirical antibiotic usage ., Severe malaria was shown to affect the plasma lipid profile , with triglycerides and phospholipids being most significantly changed ( Fig 3 ) ., Hypertriglyceridemia was also found in all patients with severe anemia ( Table 4 , S1 Results ) ., A meta-study on the impact of malaria on plasma lipids supports our findings and demonstrated that malaria is characterized by, ( i ) low serum total cholesterol , HDL and LDL which seems to be unique for malaria , and, ( ii ) high triglycerides which is common to other febrile conditions 15 ., The underlying biological mechanism of lipid profile changes during malaria is not fully understood yet 15 , but may be related to metabolic changes induced by both the parasite and host immune responses ., Our data further suggests that some malaria phospholipid markers are moderately quantitatively correlated with parasite density , and that the overall concentration of the typical malaria lipids increases with disease severity ( young age and low Hb-levels , Fig 3 ) ., Similar findings have been reported by malaria metabolomics and clinical observational studies 16 , 17 , and suggest that host metabolites characteristic for malaria may be more suitable to assess malaria disease progression than to diagnose malaria patients amongst febrile patients ., In contrast to most malaria patients , non-malaria patients were characterized by the presence of immunoregulatory metabolites including corticosteroids , of which the majority were glucocorticoids , and presumably several metabolites related to the eicosanoids ., Glucocorticoids are produced by the adrenal glands in response to activation of the hypothalamic-pituitary-adrenal ( HPA ) axis by the immune system when sensing challenges like infectious agents 18 ., During infection these steroid hormones have immuno-suppressive effects to prevent overshooting of inflammatory and immune responses that would be detrimental 19 ., Numerous studies have documented HPA activation and subsequent glucocorticoid release upon bacterial and viral infection 18 , but little is known about the HPA axis response during malaria 20–24 ., One study also reported an inappropriately low glucocorticoid release in severe malaria 24 ., The reason for this apparent HPA dysfunction in severe malaria is unclear , but could reflect low levels of the corticosteroid precursor cholesterol seen in malaria ( see above ) , or a deregulation of the pituitary-adrenal function caused by P . falciparum parasites ., Further research is needed to understand this phenomenon , but our results already suggest that whatever the cause of the HPA dysfunction in malaria , it may be overcome by concomitant infections ., Indeed , patients with the presumptive co-infections in our study were characterized by both the typical malaria lipids and the immunoregulatory metabolites found in non-malaria illness ( Fig 3 ) ., The immunoregulatory metabolites also appeared to be predictive of poor clinical outcome ( non-survival ) when present in high concentrations ( Fig 3 and Fig 5B ) ., There is indeed increasing evidence that an excessive proinflammatory response and an abnormal activation of the HPA axis are key determinants in progression to organ failure and death in patients with critical illness 25 , 26 ., The high circulating levels of glucocorticoids are a symptom of either impaired glucocorticoid clearance or growing glucocorticoid tissue resistance which opens the door to uncontrolled systemic inflammation associated with high mortality 26 ., BSI patients were marked by increased plasma levels of bile acids and alcohols , which appeared to be independent of the infecting bacterial species ., Elevated plasma levels of bile acids points to endotoxemia-related cholestasis in the liver 27 , 28 , and has also been reported as a marker of sepsis patients with community-acquired pneumonia 25 ., Cholestasis and the associated elevated plasma levels of bile metabolites occurs in many conditions affecting liver function but in the target population of severe febrile illness it appeared to be fairly specific for BSI patients ., With an estimated sensitivity and specificity > 80% , the BSI bile signature had, ( i ) a superior sensitivity than the current diagnostic tests based on pathogen detection ( sensitivity < 70% ) ,, ( ii ) a superior diagnostic performance than clinical assessment ( estimated at sensitivity 83% , specificity 62% ) 29 , and, ( iii ) a performance that very well approaches the reported minimal requirements that should be met by a BSI rapid diagnostic test to be cost-effective in low-resource settings ( sensitivity of 83% , specificity of 94% ) 29 ., The excellent sensitivity ( 98 . 1% ) and negative predictive value ( 98 . 3% ) in under-fives is particularly promising as those characteristics would allow the BSI test to be used as a screening test for patients with severe febrile illness attending primary health care service ., The results of the test would allow first line health care workers to confidently withhold antibiotics from under-fives that are negative for the bile signature test , while those with a positive test result could be immediately referred to hospital for further diagnosis and care ., Such a metabolite screening test needs to meet the ‘ASSURED’ criteria ( affordable , sensitive , specific , user- friendly , rapid and robust , equipment free and delivered ) to ensure that it can be used in resource-limited and remote settings 30 ., Immunoassays generally meet ASSURED and are now available in two formats ., The simple lateral-flow “dipstick” test is currently the most widely used format ., One test-strip generally carries one or two antibodies for the detection of one or two biomarkers , and different tests can be combined in one device ., A new emerging format is the microfluidic chip that allows measurement of multiple biomarkers ., The chip is attached to mobile phones to read and display the test results to the healthcare worker ., Prototypes of this digital test format are already being tested in Central Africa 31 ., Metabolite detection with immunoassays has already been developed by the food & agriculture industry ( e . g . dipstick tests to detect bacterial toxins , pesticides , veterinary drugs ) whereby metabolite-binding antibodies are designed with computer-assisted molecular modelling 32 ., The design of this study has several limitations ., We acknowledge that the presented results are hypothesis-generating and large-scale validation studies are essential to validate the performance of the candidate diagnostic signatures for BSI and non-malaria illness ., In this discovery study , we did not include a diagnostic test panel for viral and non-malaria parasitic infections , which could have helped to better characterize the non-malaria patient group ., We did not check the identified candidate diagnostic signatures in an asymptomatic control population and in patients with non-severe febrile illness ., These 2 groups should be included in future validation studies ., However a recent study conducted in Tanzania reported that over 70% of pediatric febrile patients without severe clinical signs had a viral disease 33 , thus minimizing the urgency for diagnostic tests for this target population ., Hence , we deem it a priority to validate the performance of the non-malaria and BSI signatures in a larger sample of patients with severe febrile illness ., In conclusion , this study demonstrates that malaria and non-malaria patients with severe febrile illness have some fundamental differences in host response that could be exploited for differential diagnosis of severe febrile illness ., In combination with the current malaria RDT , a rapid test assessing the plasma levels of 3–4 metabolites could inform on the likelihood of malaria , non-malaria illness , BSI , and survival and could thus empower primary health care centers in making informed treatment and referral decisions .

Introduction, Materials and Methods, Results, Discussion

Non-malaria febrile illnesses such as bacterial bloodstream infections ( BSI ) are a leading cause of disease and mortality in the tropics ., However , there are no reliable , simple diagnostic tests for identifying BSI or other severe non-malaria febrile illnesses ., We hypothesized that different infectious agents responsible for severe febrile illness would impact on the host metabololome in different ways , and investigated the potential of plasma metabolites for diagnosis of non-malaria febrile illness ., We conducted a comprehensive mass-spectrometry based metabolomics analysis of the plasma of 61 children with severe febrile illness from a malaria-endemic rural African setting ., Metabolite features characteristic for non-malaria febrile illness , BSI , severe anemia and poor clinical outcome were identified by receiver operating curve analysis ., The plasma metabolome profile of malaria and non-malaria patients revealed fundamental differences in host response , including a differential activation of the hypothalamic-pituitary-adrenal axis ., A simple corticosteroid signature was a good classifier of severe malaria and non-malaria febrile patients ( AUC 0 . 82 , 95% CI: 0 . 70–0 . 93 ) ., Patients with BSI were characterized by upregulated plasma bile metabolites; a signature of two bile metabolites was estimated to have a sensitivity of 98 . 1% ( 95% CI: 80 . 2–100 ) and a specificity of 82 . 9% ( 95% CI: 54 . 7–99 . 9 ) to detect BSI in children younger than 5 years ., This BSI signature demonstrates that host metabolites can have a superior diagnostic sensitivity compared to pathogen-detecting tests to identify infections characterized by low pathogen load such as BSI ., This study demonstrates the potential use of plasma metabolites to identify causality in children with severe febrile illness in malaria-endemic settings .

In the tropics , malaria is commonly attributed to be the cause of most childhood fevers , while in fact this condition is more commonly caused by other pathogens that are clinically indistinguishable from malaria ., These so-called non-malaria febrile illnesses include bacterial bloodstream infections , which are associated with a higher mortality than malaria ., Most health care facilities in the tropics have malaria diagnostic tests available , but tests for non-malarial febrile illnesses are extremely limited ., There is the critical need for new tests that can address the question ‘if a febrile patient is not suffering from malaria , then what is it and what treatment will be effective ? ’ Using metabolomics , we have comprehensively screened the biochemical profile of patients with severe febrile illness for biological markers of non-malaria febrile illness ., The results show that severe malaria and non-malaria febrile illness trigger a distinct metabolic response in the host ., We demonstrate that this pathophysiological difference can be exploited for differential diagnosis of severe febrile illness and identification of patients with bacterial bloodstream infections .

medicine and health sciences, body fluids, tropical diseases, bile, microbiology, parasitic diseases, parasitic protozoans, metabolomics, protozoans, metabolites, malarial parasites, hematology, biochemistry, diagnostic medicine, blood, anatomy, virology, physiology, co-infections, biology and life sciences, malaria, metabolism, organisms

journal.pgen.1004698

Ancient Expansion of the Hox Cluster in Lepidoptera Generated Four Homeobox Genes Implicated in Extra-Embryonic Tissue Formation

The characterization of Hox genes in the 1980s awakened the idea that there may be similar processes controlling body patterning in divergent animals and gave the first opportunity to compare the control of developmental processes between taxa at a molecular level ., In animals as evolutionarily divergent as insects , annelids and vertebrates , Hox genes encode transcription factors deployed in early development , most notably to control spatial identity along the anteroposterior axis of the developing embryo 1 ., Conservation of Hox gene function is reflected in their constrained evolution ., First , there is high conservation of encoded protein sequence , particularly within the 60-amino acid homeodomain motif ( encoded by the homeobox ) containing three alpha helices ., Second , Hox genes are often arranged in a genomic cluster , which was generated by tandem gene duplication early in animal evolution 2 , 3 ., Gene order is generally constrained , partly through shared and long-range regulatory elements 1 , 4 , 5 ., Third , after expansion of the Hox cluster in early animal evolution there has been relatively little variation in gene number ., The ancestor of all Ecdysozoa , Lophotrochozoa and Deuterostomia possessed 7 to 10 Hox genes 3 , and most bilaterian animals still have approximately this number despite hundreds of millions of years of subsequent evolution ., The lack of expansion of the Hox gene cluster within Bilateria is intriguing and is in contrast to the pattern of evolution seen for many other sets of genes 6 , 7 ., Exceptions are Hox cluster expansion to 15 genes in amphioxus 8 , 9 and duplication of the entire gene cluster in vertebrates 2 , 5 , 10 ., There are few recorded cases of tandem duplication within the Hox gene cluster ., The best characterised example relates to the Hox paralogy group 3 ( PG3 ) gene of insects , called zerknullt ( zen ) , which has duplicated in a beetle ( Tribolium castaneum ) to yield zen and zen2 11 , and in cyclorrhaphan flies to generate zen and the highly derived bicoid ( bcd ) 12 ., A further duplication specific to the genus Drosophila generated zen2 13 ., Furthermore , early in insect evolution the zen/PG3 gene lost its ancestral function of providing positional identity along the anteroposterior axis , and acquired a novel role in extra-embryonic tissue formation 14 , 15 , 16 ., There are indications that the Hox gene cluster also expanded in Lepidoptera ., Analysis of the Domesticated Silkmoth Bombyx mori genome revealed a large array of divergent homeobox genes , named Shx ( Special homeobox ) genes , between pb and zen 17 ., With 12 Shx loci described , in addition to zen , the canonical Hox genes and another divergent gene ftz , the Silkmoth has the largest Hox gene cluster described 17 ., The Silkmoth Shx sequences are highly divergent; some loci have internal duplications manifest as two or three homeobox sequences per gene , and some have disruptive mutations and are probably pseudogenes ., The Hox gene cluster has also been characterised in the nymphalid butterflies Heliconius melpomene and Danaus plexippus ( Monarch ) where four homeobox genes were found between pb and zen 18 , 19 ., To date , the timing of the gene duplications , the ancestral condition for the Lepidoptera , variation in Shx gene number and gene expression have not been addressed ., Here we investigate the origin and evolution of Shx genes through sequencing and assembly of genomes from six species representing successively diverging lepidopteran lineages as well as an outgroup from Trichoptera ( caddisflies ) ., We find that four distinct Shx genes arose from the zen gene in the ancestor of the Ditrysia , the clade encompassing most Lepidoptera , and that this complement , not the expanded number found in Bombyx , is the norm across lepidopteran evolution ., By modelling tertiary structure , we show that Shx protein sequence is compatible with folding into helix-loop-helix-turn-helix homeodomains ., Finally , we determine the expression of Shx genes in early developmental stages of the Speckled Wood butterfly Pararge aegeria ., These data suggest that Shx genes encode homeodomain proteins with probable roles in extra-embryonic tissue specification and formation ., The lepidopteran zen gene may play a more downstream role in extraembryonic membrane function following serosal closure ., We generated low coverage genome sequences for six species chosen for their phylogenetic positions ( Figure 1B ) ., Shx sequence data were also extracted from genome projects of the Silkmoth 20 , the Diamondback moth Plutella xylostella 21 , and the butterflies H . melpomene 18 and the Monarch D . plexippus 19 ., The last two species are members of the Nymphalidae , the largest butterfly family , which we elected to sample further using the Comma and Speckled Wood butterflies ( Polygonia c-album and Pararge aegeria ) ., To deduce the ancestral condition for the major ditrysian clade encompassing all butterflies and the majority of moths 22 , 23 , 24 , we also selected the Scarlet Tiger moth Callimorpha dominula ( family Arctiidae ) ., To examine deeper in the evolutionary history of Lepidoptera , we chose the Horse Chestnut Leafminer moth Cameraria ohridella ( family Gracillariidae ) which , along with the Diamondback moth ( Yponomeutoidea ) represents one of the earliest evolutionary lineages of Ditrysia 21 , 22 , 23 , 24 ., As an outgroup to Ditrysia we selected the Orange Swift moth Hepialus sylvina ( synonym Trioda sylvina , family Hepialidae ) , and for an outgroup to the Lepidoptera we used a caddisfly Glyphotaelius pellucidus ( order Trichoptera ) ., The Trichoptera and Lepidoptera together form the sister clade to the Diptera ( flies ) ., Genomic DNA was sequenced using Illumina HiSeq technology , and multiple assemblies constructed using a range of k-mer sizes ., For each species , we sequenced between 31 . 6 and 83 . 1 million paired-end reads granting coverage ranging from 6× to 17× as determined using a k-mer spectrum approach ., We generated draft genome assemblies from 337 Mb to 1 . 4 Gb using de Bruijn approaches , yielding N50 values up to 5 . 3 kb ., These datasets also provide the first estimates of genome size for these species ( Table 1 ) ., Since our goal was gene and homeobox sequence hunting , rather than large-scale synteny analysis , relatively low N50 sizes are sufficient ., To determine if the coverage generated was suitable , we searched the assemblies for the canonical Hox genes ( lab , pb , Dfd , Scr , Antp , Ubx , abdA , AbdB ) and ftz ., All Hox genes were identified for all species , apart from the homeobox of Orange Swift Ubx , affording confidence in our sequencing approach to identify novel Hox genes in non-model lepidopteran species ., In order to confirm that we did not lose genes during assembly of the raw read data , we also applied an alternative assembly strategy that maximally includes all sequence reads ., This did not reveal any additional homeobox sequences ., We were able to reconstruct genomic scaffolds around the Shx , zen , pb and Dfd genes by manually inspecting and aligning contigs from multiple assemblies , enabling the definition of gene models spanning multiple exons , as well as confirmation of linkage between adjacent genes in several species ( Figure 1 , Table S1 ) ., To examine the gene duplication events that generated Shx genes , we used molecular phylogenetic analysis and comparison of gene content between different species ., Homeodomain phylogenetic trees demonstrate that the Shx genes form a monophyletic group ( BP 86 , PP 0 . 99 ) and are more closely related to zen than to any other Hox gene ( Figure 1A , Figure S1 ) ., This suggests that Shx genes originated by tandem duplication from an ancestral zen gene , consistent with their genomic location between pb and zen ( Figure 1B ) ., Sequence alignments incorporating conserved domains outside the homeodomain confirmed this result ( Figure S2 ) ., In phylogenetic analyses , Shx genes divide into four distinct orthology groups each present in Speckled Wood , Comma , Scarlet Tiger moth , Horse Chestnut Leafminer and the Diamondback moth ., The ShxA , ShxB , ShxC and ShxD groups identified in the butterflies H . melpomene and Monarch therefore originated in the clade Ditrysia , which radiated 100 to 140 Myr ago and encompasses the vast diversity of lepidopteran species 25 , 26 ., The identity of putative ShxC genes of the Diamondback moth and Horse Chestnut Leafminer is not clear when only the homeodomain is used , but the existence of conserved motifs outside the homeodomain strongly argues for orthology with ShxC , as does overall protein sequence similarity , gene linkage and phylogenetic analysis with an extended alignment ( Figure 1B , Figures S2 , S3 ) ., Our re-analysis of the Silkmoth genome identifies the previously reported Shx1 to Shx11 17 , plus four additional homeodomain-containing open reading frames which fall within the ShxA and B clades and lie between pb and zen , here named Shx13-16 ( Figure 1 , Figure S4 ) ., This observation contrasts with the stability of Shx genes through most of ditrysian evolution ., We also investigated the Hox complement in the Orange Swift Moth , an outgroup to Ditrysia but within Lepidoptera , and the caddisfly ( order Trichoptera ) , the sister order to Lepidoptera ., We find the Orange Swift moth has no bona fide Shx genes , but several copies of zen gene that do not branch within established Shx groups in our phylogenetic analysis ( Figure 1 , Figure S1 and S3 ) ., Three ( zen2 , zen3 and zen4 ) cluster with lepidopteran zen genes while zen1 has a more ambiguous affinity ( Figure 1 , Figures S1 and S3 ) ., Presence of diagnostic motifs C-terminal to the homeodomain suggests all are duplications of zen ( Figure S2 and Figure S5G ) ., It is less probable that they share a common origin with Shx , with extensive divergence causing ambiguity of orthology assignment ., Exons coding for the homeodomains plus a single probable 5′ exon of a zen gene are located on separate scaffolds that could not be linked ., The absence of zen duplication before lepidopteran radiation was confirmed by recovery of only a single zen gene in the caddisfly genome ., Duplication and divergence of zen is therefore independent in Lepidoptera and Diptera ., Shx homeodomains have undergone faster sequence change than homeodomains encoded by zen or the canonical Hox genes ., Homeodomain sequence of lab , pb , Dfd , Scr , Antp , Ubx , ftz , abdA and AbdB have 97% to 100% invariant sites across the four ditrysian Lepidoptera genomes sequenced in this study , canonical zen has 98% invariant sites and ShxA , ShxB , ShxC and ShxD have only 83% , 55% , 38% and 38% invariant sites respectively ., Although lepidopteran zen and Shx genes are paralogues , and both descend from an ancestral zen , we retain the name Shx established in Bombyx 17 to reflect the more extreme sequence divergence in their homeodomains and to avoid confusion with earlier work ., A number of conserved sites within the homeodomain are retained in Shx and zen , and S10 has been identified as unique to Hox3 orthologues ( Figure S5I , red boxes ) 15; however , outside the homeodomain Shx proteins are radically different from each other and from zen ( Figure S2 , Figure S5C–F ) ., All lineages of ditryisian Lepidoptera ( except Bombyx ) have maintained a consistent complement of four different Shx genes , in addition to canonical zen , suggesting the genes have distinct functions ., We examined whether gene-specific functions might be reflected in distinct protein motifs ., Shx proteins have several short conserved motifs C-terminal to the homeodomain; these are different between the four proteins suggesting they may interact with different co-factors ( Figure S2 , Figure S5C–G ) ., Lepidopteran zen shows more extensive protein conservation between species; these motifs are non-overlapping with those of the dipteran zen ., Furthermore , analysis of caddisfly shows that motifs shared between basal Diptera and caddisfly have been lost in the Lepidoptera ( Figure S2 , Figure S5G , H ) ., Rapid sequence evolution between closely related insect orders is consistent with a previous observation that outside the homeodomain there are no well conserved sequence motifs in zen genes of insects 27 ., To investigate the dynamics underpinning diversification of Shx genes , we tested for signatures of selection by comparing synonymous ( dS ) and non-synonymous ( dN ) rates of substitutions in the homeobox region of Shx , zen and Hox genes in a maximum-likelihood framework ., These analyses confirmed that there is strong purifying selection acting on the zen homeodomain in Lepidoptera ( dN/dS or ω\u200a=\u200a0 . 002 ) comparable to that inferred for canonical Hox genes ( ω ratio of 0 . 001 ) ., However , the Shx genes show a marked increase in coding substitutions with a dN/dS ratio of 0 . 06; ShxB ( ω\u200a=\u200a0 . 1 ) , ShxD ( ω\u200a=\u200a0 . 09 ) and ShxC ( ω\u200a=\u200a0 . 05 ) show more coding divergence than ShxA ( ω 0 . 02 ) ., Accordingly , an excess of non-synonymous substitution is detected on the branch leading to the ShxB , ShxC and ShxD clade with an inferred ω ratio greater than 1 suggesting an episode of positive selection ( Figure S6 ) ., We compared substitution ratios among codons within Shx proteins to determine whether some amino acids show evidence of positive selection ., Using a site-model applied to Shx homeodomains only , we found an increased ω ratio at some sites but no statistical support ( Table S2 ) ., However , taking the zen outgroup into account , the branch-site model found significant support ( 2Δℓ\u200a=\u200a4 . 94 , p<0 . 05 ) for positive selection at five sites ( BEB pp>0 . 95 ) ., These sites are located between alpha helices and not known to be functionally involved in protein-DNA interaction ( Table S2 ) ., As the Shx homeodomains have diverged extensively from the ancestral zen sequence , we asked whether they had undergone disabling mutations that might prevent them forming stable tertiary folds compatible with binding DNA ., We deployed homology modelling based on a well-resolved experimentally-determined tertiary structure of a related Hox protein: that of the Drosophila Antp homeodomain bound to a 13-mer DNA sequence ., Using the Comma and Speckled Wood butterfly sequences of ShxA , ShxB , ShxC , ShxD and zen , we first computed the native energy of the deduced structures modelled on the known Antp protein structure ., Each yielded a stable predicted helix-loop-helix-turn-helix structure typical of a homeodomain ( Figure 2 ) , although stability was lower when modelled in complex with the specified 13-mer DNA sequence ( Note S1 ) ., This suggests that the DNA sequence used was not optimal for these homeodomains ., To find more suitable DNA sequences , we used an in silico evolution approach and applied this to protein sequences of Comma , Speckled Wood and Horse Chestnut Leafminer , plus Drosophila Antp as a control ., Starting with homopolymeric runs of either A , C , G or T , we ran 1000 cycles of ‘mutation’ and ‘selection’ to find the most energetically stable complexes , and generated consensus DNA sequences representing predicted optimal DNA binding sites for each homeodomain ( Figure 2; Note S1 ) ., The evolved consensus sequence generated for Drosophila Antp was an approximation of the known DNA motif including the core ATTA which contacts with helix 3 of the homeodomain , plus a G residue immediately 5′ ., The evolved preferred DNA sequences for ShxA , ShxB and ShxC proteins included core ATTA or ATCA motifs , while the ShxD homeodomain showed more variation between the species preferring GTTA , ATTA or TTTA ( Figure 2; Note S1 ) ., The zen proteins are somewhat different , tolerating a T in position 4 of the core ., These results indicate that Shx and zen proteins have potential to fold into stable helix-loop-helix-turn-helix motifs compatible with sequence-specific DNA-binding ., These analyses may not predict the exact in vivo binding sites 28 , 29 ., During insect oogenesis , localisation of RNA derived from maternal gene expression establishes the future positions of embryonic and extra-embryonic regions within the oocyte , as well as its body axes ( for an overview of lepidopteran embryology , see Kobayashi et al . 30 ) ., Maternal transcripts of zen and ShxC ( and weakly ShxD ) were detected by RT-PCR in ovarioles dissected from Speckled Wood female imagos ( Figure 3A ) ., Consistent with this , we also identified these transcripts in a maternal transcriptome dataset 31 ( ShxC:PaContig23051 , GB:GAIX01013843 . 1 , GI:509161192; ShxD:PaContig8659 , GB:GAIX01015570 . 1 , GI:509158266 ) ., After egg-laying ( AEL ) each Shx gene has a distinct temporal expression profile ( Figure 3A ) ., Our observations and comparison with other lepidopteran species 32 , 33 suggests the onset of blastoderm cellularization and major zygotic transcription commences around 8 h AEL; expression of all four Shx genes plus zen is clearly detected between 8 and 12 h AEL ., In situ hybridisation to dissected ovarioles revealed that the spatial distribution of maternal ShxC and ShxD RNA is quite different to that of transcripts from their progenitor gene , zen ( Figure 4 ) ., Pre-fertilisation transcripts from ShxC are detected in the nurse cells connected to the oocyte and are concentrated in a novel and striking asymmetrical ‘hourglass’ pattern which excludes the region later fated to become embryonic tissue , and corresponds to the presumptive serosal membranes ( Figures 3B and 4C , Figure S7A–C ) ., In contrast , transcripts of ShxD are faintly distributed throughout the developing oocyte without clear subcellular localisation ( Figure 4D ) and zen transcripts are specifically detected in the follicle cells surrounding the oocyte ( Figure 4E ) ., In the embryo at 10 h AEL , transcripts of ShxA , ShxB , ShxC and ShxD are each detected in clear hourglass patterns in the cellularised blastoderm matching the earlier maternal ShxC RNA location in the oocyte ( Figure 4F–I; Figure S7E–I ) ., The location of Shx transcripts thus marks a clear distinction between the future embryonic regions ( ‘germ anlage’: small cells lacking Shx expression ) and extraembryonic regions ( larger cells expressing Shx genes ) ., Within this latter domain , transcripts of ShxD are detected most strongly in the extraembryonic cells bordering the germ anlage ( Figure 4I; Figure S7E–F , H–I ) ., At the anterior pole of the egg near the micropyle , a cluster of cells with an increased concentration of ShxD transcripts correspond to a small region that previously lacked maternal ShxC transcripts ( Figure S7D–F ) ., In comparison , zen transcripts at 10 h AEL are very weakly detected throughout the blastoderm ( Figure 4J ) ., Between 10 and 12 h AEL , the extraembryonic region expands over the germ anlage forming a protective serosal cell layer between the germ anlage and the vitelline membrane ( Figure 3B ) ., During this cell movement , ShxC and ShxD transcript levels , already lowered in the anterior ( Figure S7E , F and I ) , reduce dramatically throughout the serosal layer ( Figure 4M and N ) ., However transcripts of ShxA and ShxB , which are only of zygotic origin , continue to be detected predominantly in the serosal layer , even after it has enveloped the germ anlage ( Figure 4K , L ) ., Transcripts of zen are detected in the serosa for the first time at this stage ( Figure 4O ) showing that expression patterns of zen and the Shx diverge dramatically in both time and space during butterfly embryogenesis ., Significant zygotic transcription of the ShxA and ShxD genes was also detected in the large yolk cells beneath the blastoderm at 10–12 h AEL where transcripts were restricted to the nuclei suggesting either incipient transcription or RNA degradation in cytoplasm ( Figure 4F , I; Figure S7H–J ) ., The common ancestor of living arthropods most likely had 10 Hox genes arranged in a single genomic cluster: lab , pb , zen , Dfd , Scr , ftz , Antp , Ubx , abdA and AbdB 3 ., The primary roles of Hox genes in bilaterian animals , including arthropods , are to encode positional information and to instruct position-specific cell fate along the anterior posterior axis of the embryo ., Two clear exceptions are ftz , which evolved a role in parasegment formation in insects , and zen ., The evolutionary history of insect zen has been well studied ., In chelicerates and a crustacean the orthologous gene has a typical Hox gene expression pattern 34 , 35 , while during insect evolution the gene diverged in sequence and acquired a different expression pattern and developmental role 14 ., In addition to loss of Hox-like function , the zen gene of insects has undergone independent tandem duplications in the Flour Beetle ( to yield zen and zen2 ) and the cyclorrhaphan flies ( to yield zen and bcd ) 12 , 14 ., In the Drosophila clade , within the Cyclorrhapha , zen has duplicated again to yield zen and zen2 36 , 37 ., Zen expression has been studied for a range of pterygote insects , including the Desert Locust Schistocerca gregaria , the Milkweed Bug Oncopeltus fasciatus 27 , the Flour Beetle 38 , and the flies 39 ., Expression of the Hox3/zen precursor has also been analysed in an outgroup to the Pterygota , the apterygote Firebrat Thermobia domestica 40 ., To some extent , inference of ancestral states within the insects is complicated by interspecific variation in the structure and function of the extraembryonic membranes and progression of embryogenesis 27 ., In all pterygote insects studied however , zen expression is confined to the extraembryonic tissues with a dominant expression domain associated with early zygotic specification of the serosa , which in some species is accompanied by later , weaker expression in the amnion 14 , 27 , 38 , 41 ., Where zen duplication has occurred , both sub- and neofunctionalisation has occurred ., Whereas zygotically expressed zen functions in extraembryonic membrane specification in Drosophila , maternally expressed bcd has radically diverged in sequence , and functions as an anterior determinant in the oocyte 12 , 39 ., A subsequent Drosophila zen duplication resulted in a putatively dispensable zen2 paralog 36 , unlike in the Flour Beetle where early-acting zen-1 mainly specifies the serosal membranes and late-acting zen-2 coordinates the fusion of amnion and serosa , initiating dorsal closure 38 ., In the present study , we demonstrate that the zen gene duplicated during evolution of the Lepidoptera , independently of its duplication in Diptera and Coleoptera ., In the Ditrysia , a clade encompassing most of lepidopteran diversity , these duplications generated four distinct Shx genes located next to the ancestral zen gene ., Lepidopteran zen and Shx genes are co-orthologues of the ancestral zen gene , hence ShxA to ShxD could logically be called zen2 to zen5 ., We retain the term Shx to avoid contradiction with earlier work , and to reflect their extensive sequence divergence and their shared ‘hourglass’ expression pattern in the blastoderm suggesting common functional roles ., Additional Shx duplications occurred in the silkmoth lineage , but we find these are not typical of Lepidoptera ., In the Orange Swift moth ( Hepialidae ) , which diverged from a more basal node in lepidopteran phylogeny , Shx genes are not present but there is evidence of independent zen gene duplication ., These data indicate that the generation of four recognisable Shx genes from an ancestral zen gene occurred after the Ditrysia had diverged; the common ancestor of Ditrysia and Hepialidae may have had multiple copies of zen but none had acquired sequence characters of Shx genes ., The common ancestor of Lepidoptera and Trichoptera had just a single zen gene ., The Shx genes are therefore an evolutionary novelty of ditrysian lepidopterans ., It is striking that all these examples of tandem gene duplication within insect Hox clusters can be traced to the same progenitor gene , zen ., Indeed , we find no evidence of duplication of any other Hox gene within the Lepidoptera , and no such event has been reported in another insect ., Why should the zen gene be prone to tandem gene duplication ?, The answer is likely to lie in the transition from an embryonic to extraembryonic function in the insects ., If genomic clustering is important to Hox gene function , through shared enhancers or long-range chromatin effects , then tandem duplication of a canonical Hox gene would most likely disrupt regulation and generate a dominant effect mutation ., Conversely , the expression of zen in extra-embryonic structures probably relies on a distinct regulatory mechanism less integrated with that of neighbouring genes; the immediate effect of duplication may therefore simply be increase of transcript dosage ., The functional redundancy that is generated then offers potential for subsequent mutations to modify expression of either , or both , daughter genes ., After origin of the Shx genes , in an ancestor of the Ditrysia clade , the genes diverged radically in sequence , both within and outside the homeodomain ., Within the Lepidoptera , the Shx genes also show an accumulation of coding substitutions , compared to other Hox genes , which likely reflects episodes of positive selection on some sites ., In particular , we detect evidence of positive selection after the initial Shx gene duplicated to give ShxA and a progenitor of ShxB , ShxC and ShxD ., We also find no Shx pseudogenes ( except in the atypical Bombyx ) , but instead retention of the core set of these genes ., Together these observations argue for functional constraints on Shx proteins and the acquisition of new essential roles for these genes in the biology of ditrysian lepidopterans ., Sequence divergence in the homeodomain raised the question of whether Shx proteins are still capable of functioning as DNA-binding proteins , potentially regulating the expression of other genes ., Evidence that this biochemical role has most likely been retained comes from molecular modelling ., We show that despite the extensive accumulation of amino acid substitutions in Shx homeodomains , they still have potential to fold into stable helix-loop-helix-turn-helix motifs with appropriate interaction surfaces for binding to DNA ., An in silico evolution approach revealed that the Shx and zen proteins may have subtly different DNA sequence binding preferences , though these are not likely to be grossly dissimilar from target sequences recognised by canonical Hox proteins ., We stress that these in silico approaches do not reveal definitive binding sites 28 , 29; however , they give confidence in the assertion that Shx proteins in Lepidoptera are likely to act as DNA-binding proteins ., What roles might Shx genes play in lepidopteran biology ?, Embryonic development is similar in the Silkmoth 42 and the Small White butterfly Pieris rapae suggesting conservation across the Ditrysia 30 ., Following egg-laying the fertilised egg ( zygote ) undergoes continuous mitotic divisions and in the Silkmoth two regions can be distinguished in the cellular blastoderm based on cell density: the germ anlage which will become the embryo , and the remaining cells which will form the extraembryonic tissues notably the serosa 30 , 42 ., As observed for the Speckled Wood butterfly in the current study , in the Small White and Silkmoth , the presumptive serosa has a distinctive hourglass-shape 30 ., At 10 h AEL in the Speckled Wood extraembryonic cells become polyploid , large and flat , and by 12 h this sheet of presumptive serosal cells moves over a region where more compact embryonic cells begin to sink into the yolk in the interior of the egg 32 ., Serosal closure completes around 12 h AEL in the Speckled Wood butterfly ( summarised in Figure 3B , Figure S8 ) ., As the embryonic germ anlage grows , cells at the edge of the anlage differentiate into a second extraembryonic membrane , the amnion , which extends around the ventral surface 30 , 42 ., The expression pattern of lepidopteran zen is intriguing because it differs from other insects ., In Pterygota , except the Milkweed Bug , zen functions in early embryogenesis in the early specification of the extraembryonic membranes 14 , 16 , including in those species with a zen gene duplication ., In the Lepidoptera , we find zen has largely lost this association and is instead expressed in follicle cells and then in the serosa following closure ., Lepidopteran zen is therefore likely to have derived roles in the downstream functions of the serosal membrane ., For example , we note that as the Speckled Wood zen expression intensifies , the maturing serosa takes on a glossy appearance indicative of cuticle secretion 43 ., It has been suggested that the serosa plays roles in the innate immune system , processing environmental toxins , yolk catabolism , cuticle formation and desiccation resistance 44 , 45 ., The contrast between zen and Shx gene expression is striking ., Our data reveal that Shx genes have a close association with development of the extraembryonic tissues of the Speckled Wood butterfly , but the zen gene does not ., Indeed , all four Shx genes are expressed in the presumptive serosa well before zen expression is observed ., We suggest that following zen gene duplication in Lepidoptera , the divergent Shx genes retained an ancestral association with extraembryonic membrane specification , while zen gene function diverged radically ., It would be a mistake , however , to consider all four lepidopteran Shx genes equivalent , as they have diverged from each other in sequence and in spatiotemporal expression patterns ., Most strikingly , in the Speckled Wood there is maternal expression of ShxC and ShxD , but not ShxA and ShxB ., It is notable that zen is maternally expressed in Locust and some basal fly species 39 , 41 , whilst in other pterygote insects zen transcripts are zygotically-derived ., Maternal expression of ShxC and ShxD suggests that maternal expression may be an ancestral property of the zen gene 41 ., However , in the flies and Locust zen transcripts are diffusely distributed within the oocyte , whereas in the Speckled Wood maternally-derived ShxC transcripts are tightly localised in a very distinctive hourglass shape , clearly prefiguring the region where extraembryonic tissues will later emerge after cellularisation ., This hourglass pattern of ShxC transcripts within the single cell represents one of the most complex examples of RNA localisation ever reported in any species , and suggests that the Shx genes specify the future serosal tissue domain within the unfertilised oocyte ., Differences between Shx gene expression domains are also seen in the embryonic stages: expression of ShxC and ShxD in serosal tissue is joined by expression of ShxA and ShxB , before these two genes become the dominant expressed Shx genes after serosal cell movements around the embryo ., The evolution of Shx genes provides some parallels to the evolution of bcd in Diptera ., In both cases , the zen gene has undergone tandem duplication , daughter genes have diverged in sequence and there has been recruitment to patterning roles in the unfertilized oocyte ., DNA was extracted from individual adult specimens of the Comma butterfly ( Polygonia c-album ) , the Speckled Wood butterfly ( Pararge aegeria ) , the Scarlet Tiger moth ( Callimorpha dominula ) , the Orange Swift moth ( Hepialus sylvina ) and a caddisfly ( Glyphotaelius pellucidus ) , and from 75 pooled specimens of the Horse Chestnut Leafminer moth ( Cameraria ohridella ) using a phenol-chloroform method 46 ., Sources of specimens are given in Table S3 ., Paired-end libraries were constructed and sequenced by Oxford Genomics Centre ( http://www . well . ox . ac . uk ) using standard Illumina procedures ( http://www . illumina . com ) ., Between 32 million and 83 million 101 bp paired-end

Introduction, Results, Discussion, Materials and Methods

Gene duplications within the conserved Hox cluster are rare in animal evolution , but in Lepidoptera an array of divergent Hox-related genes ( Shx genes ) has been reported between pb and zen ., Here , we use genome sequencing of five lepidopteran species ( Polygonia c-album , Pararge aegeria , Callimorpha dominula , Cameraria ohridella , Hepialus sylvina ) plus a caddisfly outgroup ( Glyphotaelius pellucidus ) to trace the evolution of the lepidopteran Shx genes ., We demonstrate that Shx genes originated by tandem duplication of zen early in the evolution of large clade Ditrysia; Shx are not found in a caddisfly and a member of the basally diverging Hepialidae ( swift moths ) ., Four distinct Shx genes were generated early in ditrysian evolution , and were stably retained in all descendent Lepidoptera except the silkmoth which has additional duplications ., Despite extensive sequence divergence , molecular modelling indicates that all four Shx genes have the potential to encode stable homeodomains ., The four Shx genes have distinct spatiotemporal expression patterns in early development of the Speckled Wood butterfly ( Pararge aegeria ) , with ShxC demarcating the future sites of extraembryonic tissue formation via strikingly localised maternal RNA in the oocyte ., All four genes are also expressed in presumptive serosal cells , prior to the onset of zen expression ., Lepidopteran Shx genes represent an unusual example of Hox cluster expansion and integration of novel genes into ancient developmental regulatory networks .

We have examined gene duplication in a set of ancient genes used in patterning of animal embryos: the Hox genes ., These genes code for proteins that bind DNA and switch on or off other genes , and they are very similar between distantly related animal species ., Butterflies and moths , however , have additional Hox genes whose origin and role has been unclear ., We have sequenced the genomes of five species of butterfly and moth , and of a closely related caddisfly , to examine these issues ., We found that one of the Hox genes , called zen , duplicated to generate four new genes in the evolution of the largest group of butterflies and moths ., Further mutations greatly modified the DNA sequence of the new genes , although maintaining potential to encode stable protein folds ., Gene expression also changed so that the new Hox-derived genes are deployed in egg and early embryonic stages marking the tissues that will later envelop , nourish and protect the embryo .

arthropoda, invertebrates, developmental biology, genomics, genome evolution, moths and butterflies, genetics, biology and life sciences, comparative genomics, molecular evolution, computational biology, evolutionary biology, gene duplication, animals, insects, organisms, evolutionary developmental biology

journal.pntd.0005569

Modulation of Mycobacterium tuberculosis-specific humoral immune responses is associated with Strongyloides stercoralis co-infection

Helminth infections are powerful modulators of the immune response and typically elicit both Type 2 and regulatory cytokine responses 1 , 2 ., Helminths can influence the host immune response to co-existent infections because of their propensity to establish longstanding , persistent infections that in turn can modulate host immunity 3 ., For example , helminth infections are known to modulate the immune response to Mycobacterium tuberculosis ( Mtb ) in a variety of ways 4 including:, 1 ) the down modulation of Th1 responses with diminished production of the cytokines IFNγ , TNFα and IL-2 5 , 6 , 7;, 2 ) the down regulation of the Th17 ( IL-17A , IL-17F and IL-22 ) response 5 , 6 , 7; and 3 ) the induction of regulatory T cell responses 8 ., While the T cell-mediated response is the cornerstone of the protective immune response to Mtb , recent evidence suggests that B cells can also play an important role 9 , 10 ., Thus , human studies have demonstrated that antibodies in LTBI are functionally more competent than antibodies in those with active TB 11 , 12 ., Moreover , active TB is characterized by altered levels of the B cell growth factors , BAFF and APRIL 13 , that are crucial factors for peripheral B cell survival and antibody production 14 ., In addition , those with active pulmonary tuberculosis ( TB ) are also known to have a dysfunctional circulating B cell compartment that can be reset following successful TB treatment 15 ., Since helminth infections are also known to influence B cell survival and function 1 , we postulated that helminth infections could affect Mtb-specific B cell responses in LTBI ., We , therefore , sought to examine the B cell arm of the immune response in LTBI and how it is influenced by the presence of Strongyloides stercoralis , an intestinal helminth known to infect about 50–100 million people worldwide 16 ., In so doing , we demonstrate that S . stercoralis infection is associated with alterations in the levels of Mtb–specific IgM and IgG , levels of BAFF and APRIL , and the number of B cells ( and their component subsets ) in LTBI and that most of these changes are reversible following anthelmintic therapy ., All individuals were examined as part of a natural history study protocol ( 12IN073 ) approved by Institutional Review Boards of the National Institute of Allergy and Infectious Diseases ( USA ) and the National Institute for Research in Tuberculosis ( India ) ., Informed written consent was obtained from all participants ., We studied 132 individuals in Tamil Nadu , South India; 44 with LTBI and clinically asymptomatic S . stercoralis infection ( hereafter LTBI/Ss ) , 44 with LTBI only ( hereafter LTBI ) and 44 with S . stercoralis infection alone ( hereafter Ss ) ( Table 1 ) ., None had previous anthelmintic treatment nor HIV ., Follow up was performed at 6 months following recruitment and treatment ., Those with LTBI were clinically asymptomatic with a positive QuantiFERON Gold-in-tube tests and normal chest radiographs ., Active TB was excluded by sputum smear negativity ., Ss infection was diagnosed by the presence of IgG antibodies to the recombinant NIE antigen as described previously 17 , 18 ., None of the study population had other intestinal helminths ( based on stool microscopy ) ., All LTBI/Ss and Ss individuals were treated with single doses of ivermectin ( 12mg ) and albendazole ( 400 mg ) and follow–up blood draws from LTBI/Ss individuals were obtained six months later ., Treated individuals were Ss infection negative by stool microscopy at six months post–treatment ., All LTBI alone individuals were anti- Ss-NIE negative and negative for other intestinal helminths ., Leukocyte counts and differentials were performed on all individuals using an AcT5 Diff hematology analyzer ( Beckman Coulter , Brea , CA , USA ) ., Whole blood was used for ex vivo phenotyping ., Briefly , 250μl aliquots of whole blood was added to a cocktail of monoclonal antibodies specific for B cell subtypes and memory markers ., B cell phenotyping was performed using antibodies directed against CD45-PerCP ( clone 2D1 , BD ) , CD19-Pacific Blue ( clone H1B19; Biolegend , San Diego , CA , USA ) CD27-APC-Cy7 ( clone M-T271; BD ) , CD21-FITC ( clone B-ly4; BD ) CD20-PE ( clone 2H7; BD ) and CD10-APC ( clone H110a; BD ) ., Naive B cells were classified as CD45+ CD19+ CD21+ CD27-; classical memory B cells as CD45+ CD19+ CD21+ CD27+; activated memory B cells as CD45+ CD19+ CD21- CD27+; atypical memory B cells as CD45+ CD19+ CD21-CD27-; immature B cells as CD45+ CD19+ CD21+ CD10+; and plasma cells as CD45+ CD19+ CD21- CD20- 19 , 20 ) ., Following 30 min of incubation at room temperature , erythrocytes were lysed using 2 ml of FACS lysing solution ( BD Biosciences , San Jose , CA , USA ) , and cells were washed twice with 2 ml of 1XPBS and suspended in 200 μl of PBS ( Lonza , Walkersville , MD , USA ) ., Eight- color flow cytometry was performed on a FACS Canto II flow cytometer with FACSDIVA software , version 6 ( Becton Dickinson , Franklin Lakes , NJ , USA ) ., The gating was set by forward and side scatter , and 100 , 000 gated events were acquired ., Data were collected and analyzed using FLOW JO ( TreeStar , Ashland , OR , USA ) ., Leukocytes were gated using CD45 expression versus side scatter ., Absolute counts of the subpopulations were calculated from flow cytometry and hematology data ., A representative flow cytometry plot showing the gating strategies for B cell subsets is shown in the S1 Fig . Plasma levels of BAFF ( B cell activating factor ) and APRIL ( A proliferation-inducing ligand ) ( R&D Systems , Minneapolis , MN , USA ) were measured using ELISA kits , according to the manufacturers instructions ., Plasma levels of human TB antibody IgM and IgG ( CUSABIO , College Park , MD , USA ) were measured using ELISA kits , according to the manufacturers instructions ., The TB antigens used in the kit include both membrane and secreted antigens from Mtb H37Rv ., The values are expressed as OD units ., Data analyses were performed using GraphPad PRISM 6 ( GraphPad Software , Inc . , San Diego , CA , USA ) ., Geometric means ( GM ) were used for measurements of central tendency ., Statistically significant differences were analyzed using the nonparametric Mann-Whitney U test and Wilcoxon matched pair test ., Multiple comparisons were corrected using the Holm’s correction ., Correlations were calculated by the Spearman rank correlation test ., The baseline demographics of the study population are shown in Table, 1 . As can be seen , there were no differences in age or sex between the groups ., As expected , all of the individuals in the LTBI/Ss and Ss groups had IgG antibodies to the NIE antigen , while those in the LTBI ( only ) group did not have IgG antibodies to NIE ., Similarly , those in the LTBI/Ss and LTBI groups were positive by QuantiFERON in–tube testing , indicative of latent M . tuberculosis infection , whereas those in the Ss group were not ., The baseline hematological characteristics of the study populations are shown in Table, 2 . As can be seen , compared to the LTBI group , those with LTBI/Ss or Ss had significantly higher eosinophil and basophil counts ., No significant differences in the other hematological parameters were observed ., To characterize the antibody responses in LTBI/Ss co-infection , we first measured the levels of Mtb–specific IgM and IgG in LTBI/Ss and compared these to levels in LTBI or Ss ., As shown in Fig 1A , the circulating levels of Mtb–specific IgM ( GM of 0 . 11U ) and IgG ( GM of 0 . 89 U ) in LTBI/Ss were significantly lower than in LTBI ( GM IgM of 0 . 4 U and IgG of 1 . 67 U ) , but were no different from those in Ss ( GM IgM 0 . 09 U and GM IgG of 1 . 18 U ) ., This suggests that the coincident Ss infection in LTBI/Ss is associated with a reduction in the levels of Mtb-specific antibodies to those seen in Ss alone ., When the circulating levels of the B cell growth/differentiation factors BAFF and APRIL were measured in the 3 groups ( Fig 1B ) , the systemic levels of BAFF ( GM of 584 . 6 pg/ml in LTBI/Ss vs . 1118 pg/ml in LTBI and 1007 pg/ml in Ss ) and APRIL ( GM of 468 . 4 pg/ml in LTBI/Ss vs . 607 . 9 pg/ml in LTBI and 242 . 4 pg/ml in Ss ) were significantly lower in LTBI/Ss group compared to those in the in LTBI and Ss groups ., To examine the ex vivo B cell ( and B cell subset ) phenotype LTBI/Ss co-infection , we analyzed the absolute numbers of each of the important B cell subsets in the 3 groups ., As shown in Fig 2 , the absolute numbers of naïve B cells and classical memory B cells were significantly lower in the LTBI/Ss group compared to the LTBI group ., The absolute numbers of immature B cells , activated memory B cells , atypical memory B cells and plasma cells were significantly also lower in LTBI/Ss when compared to LTBI , but they were also significantly lower than the levels seen in Ss ., Next , we performed correlation analyses between the levels of IgM and IgG with those of BAFF and APRIL and with the numbers of certain B cell subsets ., As shown in Fig 3A , the circulating levels of Mtb–specific IgM showed a significant positive relationship with the systemic levels of BAFF and APRIL; the circulating levels of IgG only showed a significant relationship with BAFF ( but not APRIL ) levels ., As shown in Fig 3B , the levels of IgM exhibited a significant positive relationship with numbers of activated and atypical memory B cells as well as plasma cells ., Finally , as shown in Fig 3C , the circulating levels of IgG were positively associated with activated memory B cells and plasma cells ., These data suggest that both IgM and IgG levels reflect levels of B cell growth factors and B cell memory subsets / plasma cells in LTBI/Ss coinfection ., To examine whether the modulation of B cell responses in LTBI/Ss co-infection is reversible following anthelmintic therapy , we measured the levels of Mtb–specific IgM and IgG , the circulating levels of BAFF and APRIL and the numbers of various B cell subsets in LTBI/Ss individuals six months following anthelmintic treatment ., As shown in Fig 4A , the circulating levels of Mtb–specific IgM and IgG levels increased significantly six months following anthelmintic treatment whereas there were no significant changes in levels of BAFF and APRIL ( Fig 4B ) in the LTBI/Ss group ., Finally , as seen in Fig 4C , the absolute numbers of naïve B cells , immature B cells , classical memory B cells , activated memory B cells , atypical memory B cells and plasma cells were all significantly increased following treatment ., Infection caused by Mtb induces a strong humoral response in humans 10 ., Although , T cell responses are considered the main driver of protective immunity , antibodies , may in part , contribute to protective immunity as well 9 , 10 ., Thus , human antibodies have been shown to exert inhibitory activity against the growth of Mtb and to neutralize certain mycobacterial antigens ( including virulence factor associated-antigens ) that play important roles in host infection 12 , 21 ., Based on an antibody profiling approach , it has also been demonstrated that in LTBI , there are distinct antibody profiles compared to the antibody profiles in those with active TB and that these antibodies can promote processes pivotal in host immunity , including enhanced phagolyosomal maturation , inflammasome activation and macrophage killing of intracellular Mtb 11 ., In addition , antibodies , plasma cells and Fc receptor-bearing cells are abundant in TB granulomas 22 , 23 and antibodies against Mtb lipoarabinomannan induce increased bacterial opsonization and restrict growth 21 , 24 ., Finally , mice lacking B cells or the ability to secrete antibodies are more susceptible to Mtb infection 25 , 26 , 27 ., We tested the hypothesis that helminth co-infection can alter B cell responses in LTBI ., Our data clearly reveal that Ss co-infection is associated with major effects on three different arms of the humoral immune response–antibody production , B cell growth factor levels and absolute numbers of B cells among the various subsets ., Our data clearly illustrate that Ss co-infection is associated with significant modulation of the systemic levels of Mtb-specific IgM and IgG antibodies in the context of LTBI ., IgM and IgG have the ability to opsonize antigens for complement mediated clearance , induce FcR mediated phagocytosis , direct anti-microbial activity by engagement of Fc receptors and augment cell mediated immune responses 28 , 29 ., Therefore , the diminished levels of IgM and IgG in LTBI/Ss could potentially have detrimental effects in the immune response to TB ., Interestingly , our post-treatment data also confirm a direct association of helminth infections on the modulation of B cell function in TB as the diminished levels of both IgM and IgG increased following successful anthelmintic treatment ., Our data also reveal important associations of Ss infection with BAFF and APRIL levels in LTBI/Ss ., BAFF and APRIL are TNF-like cytokines that support the survival and differentiation of B cells 14 ., BAFF is known to support naïve B cell survival and influences the development of other B cell subsets 14 , 30 ., During antigen activation , BAFF upregulates TLR expression , promotes B cell survival and in collaboration with other cytokines , costimulatory signals , or TLR signals , promotes antibody class switching 30 , 31 ., Moreover , BAFF in conjunction with inflammatory cytokines causes the induction of memory B cell differentiation into plasma cells 30 , 31 ., APRIL is known to mainly function by amplifying the effects of BAFF on B cells 30 , 31 ., Thus , BAFF and APRIL can profoundly influence B cell function; our data suggest that Ss infection is associated with significant modulation of their circulating levels ., Studies examining peripheral B cell numbers have suggested that B cell numbers are decreased in active TB compared to LTBI 15 , 32 , 33 , a decrease that can normalize following definitive anti-tuberculous treatment 15 ., Thus , LTBI appears to be characterized by higher numbers of different B cell subsets , and our data suggest a significant reduction in these numbers is associated with concomitant Ss infection ., These results suggest a significant compromise in B cell distribution in the periphery ., Combined with the finding of Ss-associated changes in absolute numbers of certain B cell subsets that are associated with changes in Mtb-specific IgM and IgG levels , our data indicate that Ss infection is associated with impaired functional responses of B cells ., In summary , our study has demonstrated clearly that Ss infections are associated with altered B cell responses and B cell subset numbers in the context of LTBI coinfection ., While our study does not prove causation , it does provide evidence of a significant association of Ss infection with modulation of B cell function ., With increasing data supporting a role of antibodies in protective immune responses to TB , our data add to the growing list of immunological mechanisms by which co-existent helminth infections can modulate responses in LTBI ., They also suggest that treatment of helminth infection would make for a prudent first step in the conduct of TB vaccine trials in countries endemic for both TB and helminths .

Introduction, Materials and methods, Results, Discussion

Helminth infections are known to influence T cell responses in latent tuberculosis ( LTBI ) ., Whether helminth infections also modulate B cell responses in helminth-tuberculosis co-infection is not known ., We assessed Mycobacterium tuberculosis ( Mtb ) –antigen specific IgM and IgG levels , circulating levels of the B cell growth factors , BAFF and APRIL and the absolute numbers of the various B cell subsets in individuals with LTBI , LTBI with coincident Strongyloides stercoralis ( Ss ) infection ( LTBI/Ss ) and in those with Ss infection alone ( Ss ) ., We also measured the above-mentioned parameters in the LTBI-Ss group after anthelmintic therapy ., Our data reveal that LTBI-Ss exhibit significantly diminished levels of Mtb-specific IgM and IgG , BAFF and APRIL levels in comparison to those with LTBI ., Similarly , those with LTBI-Ss had significantly diminished numbers of all B cell subsets ( naïve , immature , classical memory , activated memory , atypical memory and plasma cells ) compared to those with LTBI ., There was a positive correlation between Mtb—antigen specific IgM and IgG levels and BAFF and APRIL levels that were in turn related to the numbers of activated memory B cells , atypical memory B cells and plasma cells ., Finally , anthelmintic treatment resulted in significantly increased levels of Mtb—antigen specific IgM and IgG levels and the numbers of each of the B cell subsets ., Our data , therefore , reveal that Ss infection is associated with significant modulation of Mtb-specific antibody responses , the levels of B cell growth factors and the numbers of B cells ( and their component subsets ) .

Helminth infections and tuberculosis are two of the major health care problems worldwide and share a great deal of geographical overlap ., Moreover , helminth infections are known to induce immune responses that are antagonistic to the protective immune responses elicited by Mycobacterium tuberculosis ., Having previously demonstrated that helminth infections can profoundly alter protective T cell responses needed to control tuberculosis infection , we examined how Strongyloides stercoralis ( Ss ) infection influences B cell responses in latent tuberculosis infection ( LTBI ) in the context of co-infection and showed the Ss infection is associated with dramatic alterations in mycobacterial-specific IgG and IgM responses and levels of B cells and their growth factors BAFF and APRIL ., These alterations in B cell responses could have implications for vaccine-induced immune responses to tuberculosis in helminth—endemic countries .

blood cells, medicine and health sciences, immune cells, immune physiology, immunology, tropical diseases, parasitic diseases, bacterial diseases, memory b cells, antibodies, bacteria, immune system proteins, infectious diseases, white blood cells, animal cells, proteins, tuberculosis, actinobacteria, immune response, antibody-producing cells, helminth infections, biochemistry, cell biology, b cells, mycobacterium tuberculosis, physiology, co-infections, biology and life sciences, cellular types, organisms

journal.pcbi.1000997

Vulnerabilities in the Tau Network and the Role of Ultrasensitive Points in Tau Pathophysiology

Despite the fidelity of protein folding and the operation of quality control mechanisms to eliminate misfolded and otherwise abnormal proteins , a number of diseases can be traced to defects in these processes 1 ., Among them are many neurodegenerative disorders , including the tauopathies , which are characterized by the intraneuronal aggregation of tau protein and of which Alzheimers disease ( AD ) is an example ., Preventing aggregation to halt or reverse cognitive decline is the goal of many drug discovery programs , but effective , long-term treatments have yet to be discovered 2 ., A convincing body of evidence implicates defective tau processing and the formation of intraneuronal tau aggregates in cognitive decline ., Mutations in the gene encoding tau protein are directly responsible for a number of genetic conditions collectively called primary tauopathies , among which is frontotemporal dementia and Parkinsonism linked to chromosome 17 ( FTDP-17 ) 3 , 4 ., Tau pathology is also present in a large number of conditions whose cause cannot be traced to mutations in the gene encoding tau , including traumatic brain injury and repeated head trauma ( dementia pugilista ) from contact sports 5–7 as well as Alzheimers disease , and has been observed with and without amyloid-beta pathology ., Post-mortem assessment of the neurofibrillary tangle load in the brains of demented human patients showed that the severity of dementia was well correlated with the presence of tangles , a finding that argues strongly that tau plays a central role in disease progression 8–10 ., In addition , the deficits in spatial learning and memory observed in mouse models expressing human APP can be ameliorated by reducing endogenous , wild-type tau 11 , which also protects against early mortality and inhibits excitoxicity; this finding is supported by more recent experiments in an AB-forming mouse model 12 ., Taken together , these studies point to tau as a key causative factor in neurodegeneration and suggest that the tau pathway itself represents a reasonable therapeutic target for diseases in which the abnormal tau processing pathway is triggered ., Tau is a neuronal , microtubule-associated protein ( MAP ) whose physiological function is to regulate microtubule dynamics ( Figure S1 ) ., Alternative mRNA splicing yields 6 protein isoforms that are divided into two broad classes according to whether they contain 3 or 4 microtubule binding repeats; they are known as the 3R and 4R isoforms , respectively 13 , 14 ., The 4R isoforms have a higher affinity for microtubules and greater tendency to aggregate 15–18 ., A phospho-protein with nearly 30 phosphorylation sites , taus biological activity is also governed by its phosphorylation state ., In a healthy neuron , tau contains 2–3 moles of phosphate per mole of tau and is found almost entirely bound to microtubules 19 ., In degenerating neurons , kinase and phosphatase activity is dysregulated and an abnormal variant containing 5–9 mol phosphate/mol tau is generated ., While normal amounts of physiological tau are maintained , high amounts of hyper- and abnormally phosphorylated tau with low affinity for microtubules and resistance to degradation are generated 20 ., These tau species dissociate from microtubules and collect in the cytosol , where they subsequently misfold and aggregate ., The presence of ubiquitin , a molecular tag that facilitates degradation by the proteasome , in the aggregates suggests a failure of the quality control systems that clear aberrant proteins , contribute to the accumulation of abnormal tau and the neurofibrillary tangles 21 ., Experiments demonstrating that the ubiquitous , constitutively expressed chaperone Hsc70 binds tau support this view , as Hsc70 is a chaperone known to mediate a protein triage decision that results in either refolding or degradation 22 ., When the cells quality control systems fail , tau aggregates and eventually neuron death occurs ., The long , insoluble filaments that form may serve as a ‘stop-gap’ measure to protect the cell from adverse consequences by sequestering toxic intermediates ., However , the actual toxic moiety among various pathological tau states has not been conclusively determined ., The multifactorial nature of disease motivates our systems biology approach to understanding tau pathophysiology ., We have developed a computational model that represents the network of interactions in which tau is involved as a system of ordinary differential equations that describe the deterministic chemical kinetics ., The model was tuned to capture observed behavior in a healthy neuron and an aggregation-prone neuron ., Although the class of tauopathies contains several diseases , specific experimental data from Alzheimers disease studies informed this model ., Sensitivity analysis tools were used to interrogate the model and ascertain the relative contributions of each component in the tau pathway from its synthesis to its post-translational modifications , to its degradation ., Within both populations of neurons , and particularly the aggregation-prone population , we found ultrasensitive cellular conditions that are likely to be resistant to rescue ., As one of the first attempts at in silico simulation of tau pathophysiology , a mathematical model representative of the known biology was established within the limitations of the available data ( Figure 1 ) ., Although this model is necessarily a simplified version of reality , it captures essential features of the known tau network and could be easily extended to incorporate additional detail as new data is generated ., Among the key components are the 3R and 4R isoforms of tau ., Alternative splicing of other tau exons was not considered in the model; therefore we modeled two species to be representative of the 3R and 4R classes ., The isoform classes were divided into a number of phospho-states; although there are likely many disease-relevant phospho-isoforms , for simplicity , each 3R and 4R form was divided into in a minimally phosphorylated , normally phosphorylated , or abnormally phosphorylated/conformationally altered state ., Minimally phosphorylated 3R and 4R tau are constitutively produced in a single reaction that captures transcription and translation ., Specific tau kinases and phosphatases such as GSK3-β and PP5A were not explicitly included in the model ., The kinetics of phospho-isoform conversion were modeled using Michaelis-Menten kinetics and based on in vivo data , from which the bounds on the Michaelis-Menten constants and the dependence of the kinetics on the phospho-state were derived ., Tubulin , the building block of microtubules , was included although the total pool of tubulin with which tau interacts was considered constant throughout these analyses ., Makrides and colleagues 23 monitored the in vitro reaction kinetics between tau and pre-assembled microtubules and found that a two-step mechanism in which either tau or tubulin underwent a conformational change before binding fit the data best; we employed that two-step mechanism here , assuming the conformational change occurred in the tau protein prior to association ., Tau degradation by the proteasome has been shown both in vitro and in vivo in neuronal cell culture 24 , and has also been shown that natively unfolded tau can be degraded by the 20S proteasome in a non-ubiquitin dependent manner 25 ., This degradation process was modeled with first order kinetics and a constant pool of proteasomes ., Abnormal 3R and 4R tau are bound by the chaperone Hsc70 22 , which mediates a choice between rescue and ubiquitin-dependent degradation ., We assumed a simple , reversible binding reaction that does not involve ATP; although Hsp70 is usually an ATP-dependent chaperone , recent evidence suggests it binds tau independently of ATP 22 ., Rescue is facilitated by the chaperone Hsp90 26 , 27; although other proteins such as the peptidyl-prolyl isomerase PIN1 are likely to participate in this pathway 28 , we assumed a simple mechanism by which Hsp90 binds abnormal tau ., In this simplification , abnormal tau is dephosphorylated and restored to its normal functional form upon Hsp90 binding , and is released to re-bind microtubules ., CHIP , an Hsc70-interacting protein and E3 ligase , links the chaperone and degradation machineries and shuttles abnormal tau to the 26S proteasome 29 , 30 ., BAG-2 binds with the CHIP-Hsc70-Tau complex and subsequently dissociates with CHIP , restoring the Hsc70-Tau complex B , acting to potentially rescue tau from CHIP-mediated degradation 31 , 32 ., Alternatively , CHIP and Hsc70 can release ubiquitinated , abnormal tau in a single-step reaction , after which tau is degraded ., Because tau has been shown to be abnormally phosphorylated prior to ubiquitination , we assumed that only the abnormal tau species could be degraded in a ubiquitin-dependent , chaperone-assisted manner 33 ., Aggregation is an alternate pathway down which abnormal tau can travel ., Tau aggregation was modeled with the nucleation-elongation reaction mechanism and kinetics established by Congdon et . al . They monitored in vitro tau fibrillization and found that a tau dimer acted as the nucleus for the reaction , best fitting the experimental data and providing a good prediction of the length distribution of aggregates through time 34 ., We assumed that only abnormal , ubiquitinated tau could polymerize as the presence of ubiquitin in tau aggregates is well-established 35 , 36 and full-length , wild-type tau does not aggregate readily under physiological conditions in vitro in the absence of polymerization promoters because it is hydrophilic and relatively unstructured 18 ., Although normal tau may be sequestered by abnormal tau and thus aggregate 37 , this mechanism was excluded from our construction due to a paucity of available data ., Furthermore , the paired helical filaments into which abnormal tau aggregates in Alzheimers disease patients contain 3–4 times more phosphate than physiological tau and the level of phosphorylation observed in soluble amorphous tau is similarly elevated , suggesting that paired helical filaments are primarily comprised of abnormal tau 19 , 38 ., In a study of brains from patients diagnosed with the tauopathy FTDP-17 , in whom tau is mutated , the insoluble fraction was observed to have a much greater ratio of mutated tau than normal tau 39 , also supporting this assumption ., The effect of macromolecular crowding was also neglected for parsimony ., Excluding these mechanisms from our model is likely to have little effect on the qualitative results , resulting in a re-scaling of parameters but not substantially changing the qualitative behavior and overall conclusions ., Mass action kinetics described all reactions in the network except the phosphorylation and dephosphorylation reactions , which were described by Michaelis-Menten kinetics ., For each species represented by our model , an ordinary differential equation that describes the species time-evolution was constructed as illustrated in Eq ., S1 ., In total , the network contains 84 reactions , 93 parameters , and 45 states ( i . e . , differential equations ) ., A full listing of the states , reactions , parameters , and differential equations can be found in Tables S1 and S2 ., Parameter space for the healthy and aggregation-prone identifiability and optimization steps is different , as the chaperone and degradation machinery was considered to be operating homeostatically ., As a result , before initiating each stage of the optimization , an a priori identifiability analysis was completed ., Correlation matrices were calculated at 1024 quasi-random points in the relevant parameter space , each matrix was weighted based on the objective function value determined at its corresponding location in parameter space , and then the matrices were averaged to establish pseudo-global a priori identifiability ., The results of both stages of this analysis confirm that the proposed model is a priori identifiable and , by extension , structurally identifiable ( Figures S2 and S3 ) ., To improve the efficiency of the optimizations , we did remove three parameters ( k1 , k84 , k10 ) from the first stage of the procedure as they were highly correlated ( >0 . 95 ) ., In the next step , we optimized parameters to achieve steady-state behavior that represents healthy neuron function ., Parameters associated with phosphorylation and dephosphorylation , microtubule binding and release , synthesis , and ubiquitin-independent degradation were estimated ., We also estimated ATP synthesis and depletion ., Parameters were generally assumed isoform-independent , with the exception of the microtubule binding parameters and aggregation parameters ., Because evidence suggests that 4R tau has a greater affinity for microtubules 15–17 and for aggregation , these parameters were increased relative to the corresponding reactions involving 3R tau ., Estimating chaperone and degradation parameters was excluded from the healthy state computations because under normal conditions Hsc70 does not bind microtubule bound tau 22 ., Although Hsc70 may bind free normal tau species , these species represent a small portion of total tau and thus the model was simplified to exclude these minor interactions ., The objective function that mathematically quantifies the behavior of a healthy neuron was constructed to reflect known quantitative experimental data ., It is well-established that aberrant tau species are undetectable in normal neurons; thus we require that free and microtubule-bound aberrant tau is minimized ., From measurement of total tau in human brain homogenates 40 , and assuming total protein concentration is 500 mg/ml 41 , the total neuronal concentration of tau protein was estimated to be 5–10 µM , consistent with many reported values ., In adult human brain that is not afflicted by Alzheimers , the ratio of 3R to 4R tau was determined to be 1:1 14 , 42 ., The affinity of normal tau for microtubules is 16 nM 23 and at least 80% of the total neuronal tau is bound to microtubules ., These data are quantified in a cost function that sums the squared percent difference between the model result and the experimental results ., Several of the objectives in our cost function are “fuzzy” , i . e . they allow states to achieve a range of values without penalty , rather than admitting only a single value without penalty ., This construction is a better representation of biological systems than those that force the system to converge to a single value for objectives such as species concentrations , because it captures the intrinsic variability of these systems and it results in a large population of equally feasible parameter sets ., A global solver that uses a scatter-search method followed by refinement with a local , gradient-based method handles the flat expanses of the search space ., The sample code given in Eq ., S2 demonstrates the implementation of this type of multi-objective , fuzzy cost function ., Necessarily , the solution in this case is not unique ., Therefore , a set of 2500 optimizations was performed in which the model was run to steady-state , then evaluated against these objectives to generate a set of equally valid parameter vectors with which to initialize the model ( Dataset S1 ) ; qualitatively , the number of optimizations does not affect the results ., For this stage , the only species for which an initial condition was needed was microtubules; we assume 15 µM tubulin is present in abundance and excess over tau , and therefore do not include synthesis and degradation reactions for them ., A total of 31 parameters were estimated ., The resulting set of parameter vectors represents a population of neurons that behave in a healthy fashion and provides a way of evaluating the range of possible responses the system can display ., The median sensitivity of the population to perturbations in the parameters was calculated at steady-state , to provide insight into the triggers that disturb the systems homeostasis ( Figure 2 ) ., The 95% confidence interval for the sensitivities was also calculated ( Figure S4 ) ., Because the ratio of 3R to 4R tau is 1:1 in healthy neurons , the results for each are equivalent ., The identifiability of the sensitivity coefficients is defined by the span of the confidence interval; if the interval does not contain zero , the coefficient is considered identifiable ., Although some small sensitivity coefficients are identifiable , most are not and the converse is true for larger coefficients , particularly those >0 . 5 ( Figure S5 ) ., We find that changes in synthesis rates have the greatest positive impact on in silico homeostasis , while the perturbations in ubiquitin-independent degradation strongly and inversely alters the distribution of tau species ., The situation for sensitivity to phosphorylation and dephosphorylation is more complex ., Strong influences of this part of the network are found , but they do not act in concert ., For example , aberrant 3R tau has a positive correlation with perturbations to the rate at with normal 3R tau is phosphorylated but it has an inverse relationship with the Michaelis-Menten constant ., A similar situation is seen with bound tau states ., The relationship between the microtubule interactions and tau distribution is similarly complex ., In Figure 3 , the distribution of sensitivity coefficients within the healthy population is shown ., The coefficients for each state were consolidated and transformed by the cube root , to accommodate the large scale and preserve the sign information of the coefficients ., For all states , >99 . 9% of the coefficients fall below a value of 10 , but in a few important cases high sensitivity to perturbations is observed ., These individuals are relatively more vulnerable and less robust than the bulk of the population ., For each model of a healthy neuron , we established a corresponding aggregation-prone model ., The two models are coupled through the microtubule binding and release parameters ., Synthesis , degradation , and phosphorylation and dephosphorylation were re-estimated because these activities are known to be altered in neurons containing tau aggregates ., In addition , parameters associated with the chaperone and degradation machinery were estimated ., The objective function that quantifies the behavior of an aggregation-prone neuron is based on the data from several experiments ., Quantification of tau in adult human brains affected by Alzheimers was compared to that in control and showed that normal tau concentration was unaltered , but total tau concentration was 4–8 times normal tau; the increase is in the form of aberrant tau 40 ., The critical concentration for aggregation is reported to be 0 . 2 µM 34; necessarily , ubiquitinated tau approaches this concentration in an aggregation-prone neuron ., The results of two silencing experiments were used to finalize the construction of the cost function corresponding to the aggregation-prone population 43 ., In these experiments , silencing RNA was used to reduce the levels of Hsp70 and Hsp90 in COS-1 cells over-expressing human tau and the resulting effect on cytosolic ( unbound ) and microtubule-bound tau was assessed ., A 50% reduction in Hsp70 resulted in a 5% decrease in unbound tau and a 75% decrease in bound tau , while a 75% reduction in Hsp90 resulted in a 10% decrease in unbound tau and a 70% decrease in bound tau 43 ., The objective function was constructed as previously , resulting in the minimization of a function that is the sum of squared percent differences ., For the “fuzzy” objectives , no cost was assigned if the model simulated a result in the allowable range of values ., Each result from the tuning of a neuron to healthy behavior was used to seed an optimization run designed to generate aggregation-prone behavior ., For each run , the model was initialized to the steady-state concentrations achieved by the corresponding model of a healthy neuron ., The simulation was run until quasi-steady-state was achieved and evaluated against the objective function to find parameters that instantiate an aggregation-prone model ( Text S1 ) ., In general , a single primary route to establish the aggregation-prone behavior was not obvious ., Rather , the nature of the changes required to establish aggregation-prone neurons was multifactorial , although definite trends were observed in a small subset of the parameters ( Figure 4 ) ., Confidence intervals ( 95% ) were calculated and show just three identifiable trends; synthesis of 4R tau is generally increased while chaperone-independent degradation of normal 4R tau decreased , and the relative rate at which microtubule-bound , normal 4R tau was phosphorylated was elevated ., Relative rate is a more meaningful measure of the change in phosphorylation and dephosphorylation processes and thus the metric on which we focus ., The consistency with which these effects were observed suggests such behavior is likely to play a key role in initiating the pathological changes seen in vivo ., As this result is consistent with the known increase in tau levels and decrease in proteasomal activity , and increased kinase that occurs in affected neurons , it provides a measure of validation for the model and encourages efforts to test the subsequent conclusions drawn from its behavior ., In all cases , multiple perturbations in the rates of synthesis , degradation , and phosphorylation and dephosphorylation were required to induce an aggregation prone state ., The median sensitivity of the aggregation-prone population was calculated and the 95% confidence interval of the coefficients was used to determine their identifiability ( Figures S6 , S7 , S8 ) ., As in the healthy population , synthesis and degradation are important processes with respect to tau distribution ., Microtubule binding and phosphorylation and dephosphorylation are relatively less important in this population , although particularly for 3R tau a number of reactions in these processes are sensitive to tau distribution ., Chaperone system reactions , on the other hand , do affect the behavior of the aggregation-prone population ., Interestingly , the sensitivity to the aggregation reactions is only evident for aggregates; if the toxic moiety is actually soluble , aberrant tau , as it is increasingly thought , and not the aggregates then this has important ramifications for the selection of drug targets as the aggregation reactions have little effect on soluble tau ., To compare the aggregation-prone and healthy populations , the ratios between the sensitivity coefficients in each pair of matched individuals was calculated and the medians are shown in Figure 5 ., The aggregation-prone population exceeds twice the sensitivity of the healthy population 26% of the time and the magnitude of 46% of the median coefficients it is 2 fold lower ., Notably , in nearly 24% of cases , the sign of the median sensitivity coefficient changes ., This sign change is a striking and important phenomenon , as it suggests that the fundamental nature of the systems behavior changes during the transition from a healthy to an aggregation-prone state ., It also suggests that the effect of changing conditions in the cell , due to drug treatment , for instance , depends on the state of the system ., For example , the sensitivity of free and bound abnormal 4R tau species to phosphorylation shows a sign change; therefore , the efficacy of a treatment designed to influence phosphorylation reactions may depend upon the state of the system when treatment is initiated ., Evaluation of the distribution of the coefficients revealed subset of individuals with very large magnitude sensitivities to changing parameters , or ultrasensitivity ( Figure 3 ) ., As with the healthy population , >99% of individuals were more moderately impacted by parameter perturbations , but the ultrasensitive individuals were of a much higher magnitude in this population ., Additionally , this feature of ultrasensitivity was sharp and occurred after the accumulation of tau aggregates began ., Systems such as this represent large obstacles to treatment; although sensitivity is required of a suitable drug target , the complex nature of the systems behavior in combination with ultrasensitivity is a challenging control problem and will make it difficult to re-establish homeostasis in these individuals ., Disease progression independent of treatment is also significantly impacted by ultrasensitivity; cognitive decline is likely to be faster due to the fragile nature of this kind of network ., The in silico model developed to describe tau pathophysiology displays the very features of robustness and fragility that exist in real biological systems and these concepts are key to our understanding of the tauopathies ., Indeed , the concept of robustness provides a framework in which disease can be understood as the inevitable consequence of a breakdown in the systems that normally maintain functionality 44 ., Because these systems are complex , highly coupled , and nonlinear , their behavior is difficult to predict and systems-level approaches are required to understand and treat disease 45 ., The population of healthy neurons is considered to be robust in several ways ., The model generates healthy behavior in a relatively large domain of parameter space , a necessary property to maintain a phenotype given the inherent variation and noise in all biological systems ., Likewise , the healthy population is robust and demands a vectorial assault to become pathological , as a multitude of perturbations to synthesis , degradation , and phosphorylation and dephosphorylation are required to generate a corresponding population of aggregation-prone neurons ., In contrast , the aggregation-prone population is generally more sensitive to perturbations than the healthy population , as might be expected for a pathological phenotype ( Figure 3 ) ., Moreover , the change in sign of a quarter of the sensitivity coefficients suggests that the fundamental behavior of this nonlinear system changes during the transition from healthy to aggregation-prone conditions ., This change has implications for the drug discovery process; targeting such parts of the network is likely to be ineffective unless the timing is carefully considered ., The case study shown in Figure 6 illustrates this point ., In this individual , the binding of normally phosphorylated , 3R tau to microtubules was perturbed 5-fold and the concentration of microtubule bound , unphosphorylated 4R tau monitored in both the healthy and aggregation-prone states; the parameter perturbation is an in silico means of simulating drug treatment ., Not only is an inverse response observed in each condition , but the qualitative response of the healthy neuron is in direct opposition to that of the aggregation-prone neuron ., As the healthy and aggregation-prone neurons circumscribe the range of behaviors expected as a tauopathy advances , it logically follows that the sensitivity of relevant proteins to parameter perturbations switches at some point during disease progression ., Such phenomenon may play an important role in the effectiveness of any particular drug , whose impact may be exactly the opposite of that intended and indeed even validated in experimental models ., Therefore the identification of potential drug targets could be guided both by the identification of the perturbations that contribute to generate the diseased state and by the analysis of the parameter sensitivities in the healthy and diseased states ., To minimize undesirable system-dependent effects , we suggest to target parameters for which the sign of the sensitivity coefficients does not change between the healthy and aggregation-prone states ., Having identified synthesis , degradation , phosphorylation/dephosphorylation as keys to disease progression , the sensitivity coefficient associated synthesis and degradation reactions appeared to have a minimal number of changes of sign compared to the ones of phosphorylation/dephosphorylation reactions ( Figure 5 ) ., From that point of view , synthesis and degradation appear to be preferential drug targets within the tau network ., A subset of the aggregation-prone population displays extreme fragility ( Figure 3 ) ., This ultrasensitivity arises in the models of aggregation-prone neurons , and thus has implications for disease progression; the typical delay in diagnosing neurodegenerative diseases makes this phenomena potentially important with respect to treatment ., While it is important to develop drugs that target sensitive points in biological networks , the widespread ultrasensitivity and nonlinearity observed in a subset of the population are likely to make the response of these systems difficult to predict or control , and they are likely to be highly resistant to rescue ., The robustness of the tau network and the multifactorial nature of its vulnerability to pathological change presents a challenge to the selection of drug targets , and for a subset of patients the disease is likely to be nearly impossible to reverse after the network becomes ultrasensitive ., The model analysis also suggests that stalling or reversing tau pathophysiology will be further complicated by the timing at which the intervention is begun; a treatment may have an opposite effect on the system than is expected due to the sign inversion observed for some sensitivity coefficients ., The systems biology approach we have taken here has highlighted the complex , nonlinear behavior that cellular networks can display and suggests the difficulties the pharmaceutical and biotechnology industries will face in attempting to treat diseases associated with their aberrant functioning ., By modeling both the physiological and the pathological functioning of the network governing tau function , we have shown that the biological response to a perturbation is dependent on the condition of the network and that , therefore , the time at which a compensatory perturbation is made is potentially significant ., This implication is particularly relevant in therapeutic treatment timing and approach ., The population-based analyses we have completed also highlights the importance of variability in the study and treatment of disease and the need to characterize the variability of the network components , such as reaction rates , to more fully elucidate its nature ., Such variations are distinct from stochastic variation and the extent of the variability is likely dependent on the biological network and the particular network component ., From a modeling perspective , in silico populations can be created for any model in a straightforward manner , by retaining not just a single optimization result but a number of results that fit the data almost equally well ., As new experimental data is generated , the variation within the in silico populations will become more constrained and approach that seen in vivo ., With respect to the optimization results , they suggest an approach that considers fitting matched measurements from the same individuals , for example if data was collected from individual animals over time , rather than taking a conglomerated value over measurements from multiple individuals ., The computational , model-based approach to exploring cellular networks demonstrates a new paradigm for understanding disease that is likely to become increasingly effective as high-throughput and sequencing technologies quickly generate large databases of experimental data from which progressively more detailed , accurate models can be built ., Current knowledge about the molecular biology of tau protein was integrated into a deterministic , kinetic model that was realized as a set of 45 ordinary differential equations ( ODEs ) ( Tables S1 and S2 ) and implemented in MATLAB ( Mathworks , Cambridge , UK ) ., For each species , a differential equation was constructed from the rate

Introduction, Results, Discussion, Methods

The multifactorial nature of disease motivates the use of systems-level analyses to understand their pathology ., We used a systems biology approach to study tau aggregation , one of the hallmark features of Alzheimers disease ., A mathematical model was constructed to capture the current state of knowledge concerning taus behavior and interactions in cells ., The model was implemented in silico in the form of ordinary differential equations ., The identifiability of the model was assessed and parameters were estimated to generate two cellular states: a population of solutions that corresponds to normal tau homeostasis and a population of solutions that displays aggregation-prone behavior ., The model of normal tau homeostasis was robust to perturbations , and disturbances in multiple processes were required to achieve an aggregation-prone state ., The aggregation-prone state was ultrasensitive to perturbations in diverse subsets of networks ., Tau aggregation requires that multiple cellular parameters are set coordinately to a set of values that drive pathological assembly of tau ., This model provides a foundation on which to build and increase our understanding of the series of events that lead to tau aggregation and may ultimately be used to identify critical intervention points that can direct the cell away from tau aggregation to aid in the treatment of tau-mediated ( or related ) aggregation diseases including Alzheimers .

Neurodegenerative disorders , particularly the tauopathy Alzheimers disease , affect millions of people and cost billions of dollars a year in healthcare costs ., Although effective treatments to delay or reverse cognitive decline are still unavailable , several approaches to address this medical need are being pursued ., One such strategy involves ameliorating aberrant tau processing , as the characteristic tau tangles associated with the tauopathies are well-correlated with cognitive dysfunction , genetic mutations in tau lead directly to neurodegeneration , and experiments in animal models have yielded promising results ., Two avenues are currently being explored: inhibition of kinase activity to reduce the presence of aberrant , hyperphosphorylated tau and means to prevent and reduce tau aggregation ., We have taken a systems biology approach to understanding tau pathophysiology , creating a mathematical model to quantitatively explore the vulnerabilities in the tau network and identify effective intervention points ., Our analysis of the resulting in silico neuron populations , representing healthy and aggregation-prone neurons , highlights the multifactorial nature of the disease and provides insight into pathological triggers and the timing of treatment , which will be an important element in effectively treating patients .

computational biology/systems biology, neurological disorders